technology solving social problems

Applying artificial intelligence for social good

Artificial intelligence (AI) has the potential to help tackle some of the world’s most challenging social problems. To analyze potential applications for social good, we compiled a library of about 160 AI social-impact use cases. They suggest that existing capabilities could contribute to tackling cases across all 17 of the UN’s sustainable-development goals, potentially helping hundreds of millions of people in both advanced and emerging countries.

Real-life examples of AI are already being applied in about one-third of these use cases, albeit in relatively small tests. They range from diagnosing cancer to helping blind people navigate their surroundings, identifying victims of online sexual exploitation, and aiding disaster-relief efforts (such as the flooding that followed Hurricane Harvey in 2017). AI is only part of a much broader tool kit of measures that can be used to tackle societal issues, however. For now, issues such as data accessibility and shortages of AI talent constrain its application for social good.

This article is a condensed version of our discussion paper, Notes from the AI frontier: Applying AI for social good (PDF–3MB). It looks at domains of social good where AI could be applied, and the most pertinent types of AI capabilities, as well as the bottlenecks and risks that must be overcome and mitigated if AI is to scale up and realize its full potential for social impact. The article is divided into five sections:

Mapping AI use cases to domains of social good

Ai capabilities that can be used for social good.

• Overcoming bottlenecks, especially around data and talent

Risks to be managed

Scaling up the use of ai for social good.

For the purposes of this research, we defined AI as deep learning. We grouped use cases into ten social-impact domains based on taxonomies in use among social-sector organizations, such as the AI for Good Foundation and the World Bank. Each use case highlights a type of meaningful problem that can be solved by one or more AI capability. The cost of human suffering, and the value of alleviating it, are impossible to gauge and compare. Nonetheless, employing usage frequency as a proxy, we measure the potential impact of different AI capabilities.

For about one-third of the use cases in our library, we identified an actual AI deployment (Exhibit 1). Since many of these solutions are small test cases to determine feasibility, their functionality and scope of deployment often suggest that additional potential could be captured. For three-quarters of our use cases, we have seen solutions deployed that use some level of advanced analytics; most of these use cases, although not all, would further benefit from the use of AI techniques . Our library is not exhaustive and continues to evolve, along with the capabilities of AI.

Crisis response

These are specific crisis-related challenges, such as responses to natural and human-made disasters in search and rescue missions, as well as the outbreak of disease. Examples include using AI on satellite data to map and predict the progression of wildfires and thereby optimize the response of firefighters. Drones with AI capabilities can also be used to find missing persons in wilderness areas.

Economic empowerment

With an emphasis on currently vulnerable populations, these domains involve opening access to economic resources and opportunities, including jobs, the development of skills, and market information. For example, AI can be used to detect plant damage early through low-altitude sensors, including smartphones and drones, to improve yields for small farms.

Educational challenges

These include maximizing student achievement and improving teachers’ productivity. For example, adaptive-learning technology could base recommended content to students on past success and engagement with the material.

Environmental challenges

Sustaining biodiversity and combating the depletion of natural resources, pollution, and climate change are challenges in this domain. (See Exhibit 2 for an illustration on how AI can be used to catch wildlife poachers.) The Rainforest Connection , a Bay Area nonprofit, uses AI tools such as Google’s TensorFlow in conservancy efforts across the world. Its platform can detect illegal logging in vulnerable forest areas by analyzing audio-sensor data.

Equality and inclusion

Addressing challenges to equality, inclusion, and self-determination (such as reducing or eliminating bias based on race, sexual orientation, religion, citizenship, and disabilities) are issues in this domain. One use case, based on work by Affectiva, which was spun out of the MIT Media Lab, and Autism Glass, a Stanford research project, involves using AI to automate the recognition of emotions and to provide social cues to help individuals along the autism spectrum interact in social environments.

Health and hunger

This domain addresses health and hunger challenges, including early-stage diagnosis and optimized food distribution. Researchers at the University of Heidelberg and Stanford University have created a disease-detection AI system—using the visual diagnosis of natural images, such as images of skin lesions to determine if they are cancerous—that outperformed professional dermatologists . AI-enabled wearable devices can already detect people with potential early signs of diabetes with 85 percent accuracy by analyzing heart-rate sensor data . These devices, if sufficiently affordable, could help more than 400 million people around the world afflicted by the disease.

Information verification and validation

This domain concerns the challenge of facilitating the provision, validation, and recommendation of helpful, valuable, and reliable information to all. It focuses on filtering or counteracting misleading and distorted content, including false and polarizing information disseminated through the relatively new channels of the internet and social media. Such content can have severely negative consequences, including the manipulation of election results or even mob killings, in India and Mexico , triggered by the dissemination of false news via messaging applications. Use cases in this domain include actively presenting opposing views to ideologically isolated pockets in social media.

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Infrastructure management.

This domain includes infrastructure challenges that could promote the public good in the categories of energy, water and waste management, transportation, real estate, and urban planning. For example, traffic-light networks can be optimized using real-time traffic camera data and Internet of Things (IoT) sensors to maximize vehicle throughput. AI can also be used to schedule predictive maintenance of public transportation systems, such as trains and public infrastructure (including bridges), to identify potentially malfunctioning components.

Public and social-sector management

Initiatives related to efficiency and the effective management of public- and social-sector entities, including strong institutions, transparency, and financial management, are included in this domain. For example, AI can be used to identify tax fraud using alternative data such as browsing data, retail data, or payments history.

Security and justice

This domain involves challenges in society such as preventing crime and other physical dangers, as well as tracking criminals and mitigating bias in police forces. It focuses on security, policing, and criminal-justice issues as a unique category, rather than as part of public-sector management. An example is using AI and data from IoT devices to create solutions that help firefighters determine safe paths through burning buildings.

Our use-case domains cover all 17 of the UN’s Sustainable Development Goals

The United Nations’ Sustainable Development Goals (SDGs) are among the best-known and most frequently cited societal challenges, and our use cases map to all 17 of the goals, supporting some aspect of each one (Exhibit 3). Our use-case library does not rest on the taxonomy of the SDGs, because their goals, unlike ours, are not directly related to AI usage; about 20 cases in our library do not map to the SDGs at all. The chart should not be read as a comprehensive evaluation of AI’s potential for each SDG; if an SDG has a low number of cases, that reflects our library rather than AI’s applicability to that SDG.

We identified 18 AI capabilities that could be used to benefit society. Fourteen of them fall into three major categories: computer vision, natural-language processing, and speech and audio processing. The remaining four, which we treated as stand-alone capabilities, include three AI capabilities: reinforcement learning, content generation, and structured deep learning. We also included a category for analytics techniques.

When we subsequently mapped these capabilities to domains (aggregating use cases) in a heat map, we found some clear patterns (Exhibit 4).

Image classification and object detection are powerful computer-vision capabilities

Within computer vision, the specific capabilities of image classification and object detection stand out for their potential applications for social good. These capabilities are often used together—for example, when drones need computer vision to navigate a complex forest environment for search-and-rescue purposes. In this case, image classification may be used to distinguish normal ground cover from footpaths, thereby guiding the drone’s directional navigation, while object detection helps circumvent obstacles such as trees.

Some of these use cases consist of tasks a human being could potentially accomplish on an individual level, but the required number of instances is so large that it exceeds human capacity (for example, finding flooded or unusable roads across a large area after a hurricane). In other cases, an AI system can be more accurate than humans, often by processing more information (for example, the early identification of plant diseases to prevent infection of the entire crop).

Computer-vision capabilities such as the identification of people, face detection, and emotion recognition are relevant only in select domains and use cases, including crisis response, security, equality, and education, but where they are relevant, their impact is great. In these use cases, the common theme is the need to identify individuals, most easily accomplished through the analysis of images. An example of such a use case would be taking advantage of face detection on surveillance footage to detect the presence of intruders in a specific area. (Face detection applications detect the presence of people in an image or video frame and should not be confused with facial recognition, which is used to identify individuals by their features.)

Natural-language processing

Some aspects of natural-language processing, including sentiment analysis, language translation, and language understanding, also stand out as applicable to a wide range of domains and use cases. Natural-language processing is most useful in domains where information is commonly stored in unstructured textual form, such as incident reports, health records, newspaper articles, and SMS messages.

As with methods based on computer vision, in some cases a human can probably perform a task with greater accuracy than a trained machine-learning model can. Nonetheless, the speed of “good enough” automated systems can enable meaningful scale efficiencies—for example, providing automated answers to questions that citizens may ask through email. In other cases, especially those that require processing and analyzing vast amounts of information quickly, AI models could outperform humans. An illustrative example could include monitoring the outbreak of disease by analyzing tweets sent in multiple local languages.

Some capabilities, or combination of capabilities, can give the target population opportunities that would not otherwise exist, especially for use cases that involve understanding the natural environment through the interpretation of vision, sound, and speech. An example is the use of AI to help educate children who are on the autism spectrum. Although professional therapists have proved effective in creating behavioral-learning plans for children with autism spectrum disorder (ASD), waitlists for therapy are long. AI tools, primarily using emotion recognition and face detection, can increase access to such educational opportunities by providing cues to help children identify and ultimately learn facial expressions among their family members and friends.

Structured deep learning also may have social-benefit applications

A third category of AI capabilities with social-good applications is structured deep learning to analyze traditional tabular data sets. It can help solve problems ranging from tax fraud (using tax-return data) to finding otherwise hard to discover patterns of insights in electronic health records.

Structured deep learning (SDL) has been gaining momentum in the commercial sector in recent years. We expect to see that trend spill over into solutions for social-good use cases, particularly given the abundance of tabular data in the public and social sectors. By automating aspects of basic feature engineering, SDL solutions reduce the need either for domain expertise or an innate understanding of the data and which aspects of the data are important.

Advanced analytics can be a more time- and cost-effective solution than AI for some use cases

Some of the use cases in our library are better suited to traditional analytics techniques, which are easier to create, than to AI. Moreover, for certain tasks, other analytical techniques can be more suitable than deep learning. For example, in cases where there is a premium on explainability, decision tree-based models can often be more easily understood by humans. In Flint, Michigan, machine learning (sometimes referred to as AI, although for this research we defined AI more narrowly as deep learning) is being used to predict houses that may still have lead water pipes (Exhibit 5) .

Overcoming bottlenecks, especially for data and talent

While the social impact of AI is potentially very large, certain bottlenecks must be overcome if even some of that potential is to be realized. In all, we identified 18 potential bottlenecks through interviews with social-domain experts and with AI researchers and practitioners. We grouped these bottlenecks in four categories of importance.

The most significant bottlenecks are data accessibility, a shortage of talent to develop AI solutions, and “last-mile” implementation challenges (Exhibit 6).

Data needed for social-impact uses may not be easily accessible

Data accessibility remains a significant challenge. Resolving it will require a willingness, by both private- and public-sector organizations, to make data available. Much of the data essential or useful for social-good applications are in private hands or in public institutions that might not be willing to share their data. These data owners include telecommunications and satellite companies; social-media platforms; financial institutions (for details such as credit histories); hospitals, doctors, and other health providers (medical information); and governments (including tax information for private individuals). Social entrepreneurs and nongovernmental organizations (NGOs) may have difficulty accessing these data sets because of regulations on data use, privacy concerns, and bureaucratic inertia. The data may also have business value and could be commercially available for purchase. Given the challenges of distinguishing between social use and commercial use, the price may be too high for NGOs and others wanting to deploy the data for societal benefits.

The expert AI talent needed to develop and train AI models is in short supply

Just over half of the use cases in our library can leverage solutions created by people with less AI experience. The remaining use cases are more complex as a result of a combination of factors, which vary with the specific case. These need high-level AI expertise—people who may have PhDs or considerable experience with the technologies. Such people are in short supply.

For the first use cases requiring less AI expertise, the needed solution builders are data scientists or software developers with AI experience but not necessarily high-level expertise. Most of these use cases are less complex models that rely on single modes of data input.

The complexity of problems increases significantly when use cases require several AI capabilities to work together cohesively, as well as multiple different data-type inputs. Progress in developing solutions for these cases will thus require high-level talent, for which demand far outstrips supply and competition is fierce .

‘Last-mile’ implementation challenges are also a significant bottleneck for AI deployment for social good

Even when high-level AI expertise is not required, NGOs and other social-sector organizations can face technical problems, over time, deploying and sustaining AI models that require continued access to some level of AI-related skills. The talent required could range from engineers who can maintain or improve the models to data scientists who can extract meaningful output from them. Handoffs fail when providers of solutions implement them and then disappear without ensuring that a sustainable plan is in place.

Organizations may also have difficulty interpreting the results of an AI model. Even if a model achieves a desired level of accuracy on test data, new or unanticipated failure cases often appear in real-life scenarios. An understanding of how the solution works may require a data scientist or “translator .” Without one, the NGO or other implementing organization may trust the model’s results too much: most AI models cannot perform accurately all the time, and many are described as “brittle” (that is, they fail when their inputs stray in specific ways from the data sets on which they were trained).

AI tools and techniques can be misused by authorities and others who have access to them, so principles for their use must be established. AI solutions can also unintentionally harm the very people they are supposed to help.

An analysis of our use-case library found that four main categories of risk are particularly relevant when AI solutions are leveraged for social good: bias and fairness, privacy, safe use and security, and “explainability” (the ability to identify the feature or data set that leads to a particular decision or prediction).

Bias in AI may perpetuate and aggravate existing prejudices and social inequalities, affecting already-vulnerable populations and amplifying existing cultural prejudices. Bias of this kind may come about through problematic historical data, including unrepresentative or inaccurate sample sizes. For example, AI-based risk scoring for criminal-justice purposes may be trained on historical criminal data that include biases (among other things, African Americans may be unfairly labeled as high risk). As a result, AI risk scores would perpetuate this bias. Some AI applications already show large disparities in accuracy depending on the data used to train algorithms; for example, examination of facial-analysis software shows an error rate of 0.8 percent for light-skinned men; for dark-skinned women, the error rate is 34.7 percent.

One key source of bias can be poor data quality—for example, when data on past employment records are used to identify future candidates. An AI-powered recruiting tool used by one tech company was abandoned recently after several years of trials. It appeared to show systematic bias against women, which resulted from patterns in training data from years of hiring history. To counteract such biases, skilled and diverse data-science teams should take into account potential issues in the training data or sample intelligently from them.

Breaching the privacy of personal information could cause harm

Privacy concerns concerning sensitive personal data are already rife for AI. The ability to assuage these concerns could help speed public acceptance of its widespread use by profit-making and nonprofit organizations alike. The risk is that financial, tax, health, and similar records could become accessible through porous AI systems to people without a legitimate need to access them. That would cause embarrassment and, potentially, harm.

Safe use and security are essential for societal good uses of AI

Ensuring that AI applications are used safely and responsibly is an essential prerequisite for their widespread deployment for societal aims. Seeking to further social good with dangerous technologies would contradict the core mission and could also spark a backlash, given the potentially large number of people involved. For technologies that could affect life and well-being, it will be important to have safety mechanisms in place, including compliance with existing laws and regulations. For example, if AI misdiagnoses patients in hospitals that do not have a safety mechanism in place—particularly if these systems are directly connected to treatment processes—the outcomes could be catastrophic. The framework for accountability and liability for harm done by AI is still evolving.

Decisions made by complex AI models will need to become more readily explainable

Explaining in human terms the results from large, complex AI models remains one of the key challenges to acceptance by users and regulatory authorities. Opening the AI “black box” to show how decisions are made, as well as which factors, features, and data sets are decisive and which are not, will be important for the social use of AI. That will be especially true for stakeholders such as NGOs, which will require a basic level of transparency and will probably want to give clear explanations of the decisions they make. Explainability is especially important for use cases relating to decision making about individuals and, in particular, for cases related to justice and criminal identification, since an accused person must be able to appeal a decision in a meaningful way.

Mitigating risks

Effective mitigation strategies typically involve “human in the loop” interventions: humans are involved in the decision or analysis loop to validate models and double-check results from AI solutions. Such interventions may call for cross-functional teams, including domain experts, engineers, product managers, user-experience researchers, legal professionals, and others, to flag and assess possible unintended consequences.

Human analysis of the data used to train models may be able to identify issues such as bias and lack of representation. Fairness and security “red teams” could carry out solution tests, and in some cases third parties could be brought in to test solutions by using an adversarial approach. To mitigate this kind of bias, university researchers have demonstrated methods such as sampling the data with an understanding of their inherent bias and creating synthetic data sets based on known statistics.

Guardrails to prevent users from blindly trusting AI can be put in place. In medicine, for example, misdiagnoses can be devastating to patients. The problems include false-positive results that cause distress; wrong or unnecessary treatments or surgeries; or, even worse, false negatives, so that patients do not get the correct diagnosis until a disease has reached the terminal stage.

Technology may find some solutions to these challenges, including explainability. For example, nascent approaches to the transparency of models include local-interpretable-model-agnostic (LIME) explanations, which attempt to identify those parts of input data a trained model relies on most to make predictions.

As with any technology deployment for social good, the scaling up and successful application of AI will depend on the willingness of a large group of stakeholders—including collectors and generators of data, as well as governments and NGOs—to engage. These are still the early days of AI’s deployment for social good, and considerable progress will be needed before the vast potential becomes a reality. Public- and private-sector players all have a role to play.

Improving data accessibility for social-impact cases

A wide range of stakeholders owns, controls, collects, or generates the data that could be deployed for AI solutions. Governments are among the most significant collectors of information, which can include tax, health, and education data. Massive volumes of data are also collected by private companies—including satellite operators, telecommunications firms, utilities, and technology companies that run digital platforms, as well as social-media sites and search operations. These data sets may contain highly confidential personal information that cannot be shared without being anonymized. But private operators may also commercialize their data sets, which may therefore be unavailable for pro-bono social-good cases.

Overcoming this accessibility challenge will probably require a global call to action to record data and make it more readily available for well-defined societal initiatives.

Data collectors and generators will need to be encouraged—and possibly mandated—to open access to subsets of their data when that could be in the clear public interest. This is already starting to happen in some areas. For example, many satellite data companies participate in the International Charter on Space and Major Disasters , which commits them to open access to satellite data during emergencies, such as the September 2018 tsunami in Indonesia and Hurricane Michael, which hit the US East Coast in October 2018.

Notes from the AI frontier: Applying AI for social good

Close collaboration between NGOs and data collectors and generators could also help facilitate this push to make data more accessible. Funding will be required from governments and foundations for initiatives to record and store data that could be used for social ends.

Even if the data are accessible, using them presents challenges. Continued investment will be needed to support high-quality data labeling. And multiple stakeholders will have to commit themselves to store data so that they can be accessed in a coordinated way and to use the same data-recording standards where possible to ensure seamless interoperability.

Issues of data quality and of potential bias and fairness will also have to be addressed if the data are to be deployed usefully. Transparency will be a key for bias and fairness. A deep understanding of the data, their provenance, and their characteristics must be captured, so that others using the data set understand the potential flaws.

All this is likely to require collaboration among companies, governments, and NGOs to set up regular data forums, in each industry, to work on the availability and accessibility of data and on connectivity issues. Ideally, these stakeholders would set global industry standards and collaborate closely on use cases to ensure that implementation becomes feasible.

Overcoming AI talent shortages is essential for implementing AI-based solutions for social impact

The long-term solution to the talent challenges we have identified will be to recruit more students to major in computer science and specialize in AI. That could be spurred by significant increases in funding—both grants and scholarships—for tertiary education and for PhDs in AI-related fields. Given the high salaries AI expertise commands today, the market may react with a surge in demand for such an education, although the advanced math skills needed could discourage many people.

Sustaining or even increasing current educational opportunities would be helpful. These opportunities include “AI residencies”—one-year training programs at corporate research labs—and shorter-term AI “boot camps” and academies for midcareer professionals. An advanced degree typically is not required for these programs, which can train participants in the practice of AI research without requiring them to spend years in a PhD program.

Given the shortage of experienced AI professionals in the social sector, companies with AI talent could play a major role in focusing more effort on AI solutions that have a social impact. For example, they could encourage employees to volunteer and support or coach noncommercial organizations that want to adopt, deploy, and sustain high-impact AI solutions. Companies and universities with AI talent could also allocate some of their research capacity to new social-benefit AI capabilities or solutions that cannot otherwise attract people with the requisite skills.

Overcoming the shortage of talent that can manage AI implementations will probably require governments and educational providers to work with companies and social-sector organizations to develop more free or low-cost online training courses. Foundations could provide funding for such initiatives.

Task forces of tech and business translators from governments, corporations, and social organizations, as well as freelancers, could be established to help teach NGOs about AI through relatable case studies. Beyond coaching, these task forces could help NGOs scope potential projects, support deployment, and plan sustainable road maps.

From the modest library of use cases that we have begun to compile, we can already see tremendous potential for using AI to address the world’s most important challenges. While that potential is impressive, turning it into reality on the scale it deserves will require focus, collaboration, goodwill, funding, and a determination among many stakeholders to work for the benefit of society. We are only just setting out on this journey. Reaching the destination will be a step-by-step process of confronting barriers and obstacles. We can see the moon, but getting there will require more work and a solid conviction that the goal is worth all the effort—for the sake of everyone.

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How Technology Can Help Solve Societal Problems

April 21, 2017 • 14 min read.

Nonprofits must embrace Social Change as a Platform to reach more people and at lower cost, experts say.

In the next article of the   series , “The Network Revolution: Creating Value through Platforms, People and Technology,” authors   Barry Libert ,   Megan Beck , Brian Komar and Josue Estrada debut the concept of Social Change as a Platform.   Libert is a Wharton senior fellow and CEO of OpenMatters; Beck is the firm’s chief insights officer. Komar is vice president of community engagement for Salesforce.org, the nonprofit reseller of Salesforce.com Inc. Estrada is the senior vice president of strategy and operations at Salesforce.org. 

As Charles Dickens so astutely observed about life during the French Revolution in “A Tale of Two Cities,” it was the best and worst of times. One could say the same thing today. The Fourth Industrial Revolution of technology networks and platforms could usher in an era of mass societal disruption — as well as unprecedented social cooperation. Whether the latter would prevail depends on the ability of nonprofit entities and the broader social sector to boost their collective impact by adopting the new business models that are disrupting the for-profit world. It would also depend on whether they can embrace what we call ‘Social Change as a Platform’ or SCaaP.

During the turbulent 1960s, Bob Dylan wrote the following powerful lyrics for “The Times They Are A-Changin’” that seems apropos for today. “ Come gather ’round people, wherever you roam, and admit that the waters around you have grown. And accept it that soon, you’ll be drenched to the bone. If your time to you is worth savin’, then you better start swimmin’ or you’ll sink like a stone. For the times they are a-changin’.” At the time, anti-war protests ruled the day. A generational collide over the future of America was afoot. And all the images of a nation coming apart at its seams were emblazoned across a new communications medium — TV — that was coming of age.

And so is it today. The Fourth Industrial Revolution — what Klaus Schwab (founder of the World Economic Forum) defines as the fusion of technologies blurring the lines among the physical, digital and biological spheres — is upon us. Meanwhile, nationalism is colliding with globalism, machine learning and artificial intelligence advancing geometrically, and global warming is on a direct path to changing the very nature of our planet. Despite these many challenges, this revolution, like the many that have preceded it, also comes with a great promise of opportunity.

To be sure, there are reasons for great optimism. In just the past 30 years, the global poverty rate halved with many of the poorest people in the world becoming significantly less poor. These gains mirror dramatic improvements in health and education including advances in life expectancy, child mortality, health care provision, among other important areas. Moreover, most of these gains predate the effective integration of digital technologies into the cause. In short, it is reasonable to argue that the potential for social ‘changemakers’ armed with today’s digital platforms in partnership with large and growing virtual networks can dramatically improve the human condition.

“The potential for social ‘changemakers’ armed with today’s digital platforms in partnership with large and growing virtual networks can dramatically improve the human condition.”

Self-organization Powered by Technology

Civil society — the network of institutions that define us as actors in the civil sphere independent of governments — is supposed to serve as the leader in promoting pluralism and social benefit. As Klaus Schwab notes that “a renewed focus on the essential contribution of civil society to a resilient global system alongside government and business has emerged.” Unfortunately, nonprofit groups, academic institutions and philanthropic organizations engaged in social change are struggling to adapt to the new global, technological and virtual landscape.

Legacy modes of operation, governance and leadership competencies rooted in the age of physical realities continue to dominate the space. Further, organizations still operate in internal and external silos — far from crossing industry lines, which are blurring. And their ability to lead in a world that is changing at an exponential rate seems hampered by their mental models and therefore their business models of creating and sustaining value as well.

If civil society is not to get drenched and sink like a stone, it must start swimming in a new direction. This new direction starts with social organizations fundamentally rethinking the core assumptions driving their attitudes, behaviors and beliefs about creating long-term sustainable value for their constituencies in an exponentially networked world. Rather than using an organization-centric model, the nonprofit sector and related organizations need to adopt a mental model based on scaling relationships in a whole new way using today’s technologies — the SCaaP model.

Embracing social change as a platform is more than a theory of change, it is a theory of being — one that places a virtual network or individuals seeking social change at the center of everything and leverages today’s digital platforms (such as social media, mobile, big data and machine learning) to facilitate stakeholders (contributors and consumers) to connect, collaborate, and interact with each other to exchange value among each other to effectuate exponential social change and impact.

SCaaP builds on the government as a platform movement (Gov 2.0) launched by technologist Tim O’Reilly and many others. Just as Gov 2.0 was not about a new kind of government but rather, as O’Reilly notes, “government stripped down to its core, rediscovered and reimagined as if for the first time,” so it is with social change as a platform. Civil society is the primary location for collective action and SCaaP helps to rebuild the kind of participatory community celebrated by 19 th century French historian Alexis de Tocqueville when he observed that Americans’ propensity for civic association is central to making our democratic experiment work. “Americans of all ages, all stations in life, and all types of disposition,” he noted, “are forever forming associations.”

But SCaaP represents a fundamental shift in how civil society operates. It is grounded in exploiting new digital technologies, but extends well beyond them to focus on how organizations think about advancing their core mission — do they go at it alone or do they collaborate as part of a network? SCaaP requires thinking and operating, in all things, as a network. It requires updating the core DNA that runs through social change organizations to put relationships in service of a cause at the center, not the institution. When implemented correctly, SCaaP will impact everything — from the way an organization allocates resources to how value is captured and measured to helping individuals achieve their full potential.

SCaaP “requires updating the core DNA that runs through social change organizations to put relationships in service of a cause at the center, not the institution.”

Digital Platforms Empower Social Change at Scale

To be sure, early adopters are already using technology to effectuate change at a pace and scale not previously available in the physical and digitally disconnected world. The marginal cost of delivery remains too high. But with today’s technologies, with support from the board and management to make it happen, social change at scale is possible. Here are some organizations that are on the way to implementing SCaaP.

• DonorsChoose.org: Every one of their 1.5 million donors can create engagement paths for each potential recipient of a classroom project, matching their specific giving preferences and history — something previously available only to large donors. It is the only nonprofit to be named to Fast Company’s list of the 50 Most Innovative Companies in the world.
• Health Leads: It is a healthcare organization that connects low-income patients with the basic resources they need to be healthy, as part of their regular doctor’s visits. As Forbes noted, “Community health workers, case managers and/or student volunteers screen patients for unmet needs and help them access any of the 50 basic resource needs relevant for their circumstances, such as food assistance, childcare vouchers, enrollment in a GED program — even negotiating with the utilities company to get their heat turned back on.”
• College for America: Southern New Hampshire University went from a small, relatively unremarkable New England institution to one of the biggest nonprofit online educators in the country. According to Campus Technology magazine, “SNHU has succeeded in the online space by leveraging technology and providing well-constructed courses and Amazon-like customer service to mostly older students at a cost they can afford.”
• Salesforce.org’s Power of Us Hub: Among the most successful online communities built on Salesforce technology, the Power of Us Hub facilitates peer-to-peer collaboration around the effective use of technology for more than 30,000 social change organizations. More than 98% of the questions asked get answered by the community, a real shared benefit model in action.

Just as Apple chose a platform approach when launching their App Store, these organizations are enabling their partners and contributors to share and co-create in the value chain they co-inhabit. Each has moved beyond allowing supporters to donate and promote, toward sharing real value through stakeholders’ talents and assets.

Tomorrow’s SCaaP

We are at the dawn of the SCaaP era. The future of social change as a platform is a world of connected platforms working to solve society’s most pressing challenges more effectively as fast as possible. These platforms will supersede and encompass existing social change organizations. Those organizations that embrace social change as a platform will lead the way in helping to usher in this new era of connected social change platforms.

The core assets needed today to advance social change — ideas, individuals and institutions — continue to be the primary ingredients. What is changing and will continue to change, however, is the way these assets are assembled to deliver maximum social impact. Organizations can achieve SCAAP to the extent that those with a shared cause can gradually maximize shared capability (platforms) and minimize organization products. This represents a radical shift in approach.

Every organization relies on its information, capabilities and assets to be effective, but their networks are largely untapped or underutilized. Creating more value and scaling social impact requires the organizations’ leaders to leverage their networks, tapping into new sources of value, both tangible and intangible.

Value in the social impact supply chain will continue to come from new sources, for those who allow that to happen. Existing stakeholders in social change organizations will add value in new ways and new stakeholders will interact in new ways with the community’s resources and assets via the platform. SCaaP will increasingly bring all those actors and sectors together.

Philanthropic institutions supporting similar causes will be working together out in the open, ensuring all their resources and those supported through their grant-making are at the disposal of the community working to advance social change — not any one individual or institution. These efforts will be focused on maximizing the way value is derived and how the agency is built, shared and advanced throughout the network.

“The future of social change as a platform is a world of connected platforms working to solve society’s most pressing challenges more effectively as fast as possible.”

Key SCaaP Advantages to Nonprofits

Social change organizations that leverage their stakeholder’s networks as well as their tangible (programs and services) and intangible (expertise and relationships) assets will gain these and other advantages from embracing the SCaaP business model.

• Decreases costs: Stakeholders willing to share their opinions, skills, relationships and even real assets for shared value to the cause, at a very low or near-zero cost , stretch an organization’s very scarce resources. Moreover, reinventing the wheel each time social change products and services are created lead to duplication and waste.
• Deepens community engagement: Enabling meaningful ways for stakeholders to add value increases engagement and deepens understanding and strengthens these relationships. SCaaP enables anyone with a good idea to build innovative services that connect citizens to the cause of their choice, allowing citizens to more directly participate.
• Increases organizational flexibility and decreases risk: Operating as a network increases an organization’s adaptability and speed. Work is more distributed and lends itself to self-organizing, which makes it highly responsive to changing needs. Allowing common functions to be implemented as shared utilities across social change organizations instead of replicating them in each silo also reduces risk.
• Enhances transparency and accountability: SCaaP fundamentally shifts the power dynamic within the social change community. Grant makers work with community stakeholders as peers, helping them achieve full potential as individuals and their organizations.
• Expands impact: Ultimately, scaling relationships lets an organization secure more value, which helps maximize social impact. As co-creating partners who have a vested interest in advancing a cause, stakeholders’ incentive to add value is clear. The platform’s success is their success.

To succeed, a clear and understandable pathway to adopting SCaaP is necessary for this large, untapped market.

Seven Steps to Embracing SCaaP Today

Social change as a platform is first and foremost a business strategy, a theory of change that needs to be integrated into every organization’s five-year strategic plan. That effort begins by identifying how and where an organization can accelerate the transition to a network-model across the entire organization. Specifically, organizations must assess their business model and inventory network assets, and start to reallocate resources and capital to networks as well as develop network key performance indicators (KPIs).

• Choose the right platform. Platforms that embrace intelligence, speed, productivity, mobility, and connectivity empower social change organizations to take advantage of the most significant transformations taking place in enterprise software.
• Select the relationships to scale. Identify all the key stakeholders for advancing your mission and indicate which relationships are the most important to scale. Be sure to include existing and potential relationships, including other partners and organizations that can add value.
• Connect programs and services. Plot the organization’s various offerings — programs and services offers to various stakeholders — and map how each contributes value to advance the relationships with different stakeholders.
• Convert the data into intelligence. A unified view of relationships and programs creates troves of data. Convert the data into useful, real-time intelligence integrated into the organization’s processes in real-time.
• Drive one-to-one engagement. Real-time intelligence lets organizations engage more effectively with all.
• Track what matters. It’s not just financial performance that matters, but also engagement, sentiment and co-creation. Create KPI’s for each of these items and add them to daily performance reviews.
• Keep platforms, networks and intelligence at the center. Products and services are helpful, but in the final reckoning, it is the breadth and depth of the network that will create the scale of social change desired.

The biggest hurdle to SCaaP is changing the mental models and core competencies of the leadership team and board of directors. However, nonprofit organizations and academic institutions are better positioned to embrace SCaaP because they are more accustomed to imagining their community as active participants, instead of passive recipients. But it is critical that leaders significantly change how they embrace today’s technologies.

With SCaaP, the nonprofit world will have the potential to enact social change on a scale previously unimagined. It is time to take up the mantle because doing so can unlock the future potential of every human being. People are worth it.

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A guide to when and how to build technology for social good

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People frequently try to participate in political processes, from organizing to hold government to account for providing quality health care and education to participating in elections. But sometimes these systems are set up in a way that makes it difficult for people and government to engage effectively with each other. How can technology help?

In a new how-to guide, Luke Jordan , an MIT Governance Lab (MIT GOV/LAB) practitioner-in-residence, advises on how — and more importantly, when — to put together a team to build such a piece of “civic technology.” 

Jordan is the founder and executive director of Grassroot, a civic technology platform for community organizing in South Africa. “With Grassroot, I learned a lot about building technology on a very limited budget in difficult contexts for complex problems,” says Jordan. “The guide codifies some of what I learned.” 

While the guide is aimed at people interested in designing technology that has a social impact, some parts might also be useful more broadly to anyone designing technology in a small team. 

The “don’t build it” principle 

The guide’s first lesson is its title: “Don’t Build It.” Because an app can be designed cheaply and easily, many get built when the designer hasn’t found a good solution to the problem they're trying to solve or doesn’t even understand the problem in the first place. 

Koketso Moeti, founding executive director of amandla.mobi, says she is regularly approached by people with an idea for a piece of civic technology. “Often after a discussion, it is either realized that there is something that already exists that can do what is desired, or that the problem was misdiagnosed and is sometimes not even a technical problem,” she says. The “don’t build it” principle serves as a reminder that you have to work hard to convince yourself that your project is worth starting. 

The guide offers several litmus tests for whether or not an idea is a good one, one of which is that the technology should help people do something that they’re already trying to do, but are finding it difficult. “Unless you’re the Wright brothers,” says Jordan, “you have to know if people are actually going to want to use this.” 

This means developing a deep understanding of the context you’re trying to solve a problem in. Jordan’s original conception of Grassroot was an alert for when services weren’t working. But after walking around and talking to people in communities that might use the product, his team found that people were already alerting each other. “But when we asked, ‘how do people come together when you need to do something about it,’” says Jordan, “we were told over and over, ‘that’s actually really difficult.’” And so Grassroot became a platform activists could use to organize gatherings. 

Building a team: hire young engineers

One section of the guide advises on how to put together a team to build a project, such as what qualities one should want in a chief technology officer (CTO) who will help run things; where to look for engineers; and how a tech team should work with one's field staff. 

The guide suggests hiring entry-level engineers as a way to get some talented people on board while operating on a limited budget. “When I’ve hired, I’ve tended to find most of the value among very unconventional and raw junior hires,” says Jordan. “I think if you put in the work in the hiring process, you get fantastic people at junior levels.”

“Civic tech is one exciting area where promising young engineers, like MIT students, can apply computer science skills for the public good,” says Professor Lily L. Tsai, MIT GOV/LAB’s director and founder. “The guide provides advice on how you can find, hire, and mentor new talent.”

Jordan says the challenge is that while people in computer science find these “tech for good” projects appealing, they often don’t pay nearly as well as other opportunities. Like in other startup contexts, though, young engineers have the opportunity to learn a lot in an engaging environment. “I tell people, ‘come and do this for a year-and-a-half, two years,’” he says. “‘You’ll get paid perhaps significantly below industry rate, but you’ll get to do a really interesting thing, and you’ll work in a small team directly with the CTO. You’ll get a lot more experience a lot more quickly.’” 

How to work: learn early, quickly, and often

Jordan says that both a firm and its engineers must have “a real thirst to learn.” This includes being able to identify when things aren’t working and using that knowledge to make something better. The guide emphasizes the importance of ignoring “vanity metrics,” like the total number of users. They might look flashy and impress donors, but they don’t actually describe whether or not people are using the app, or if it’s helping people engage with their governments. Total user numbers “will always go up except in a complete catastrophe,” Jordan writes in the guide. 

The biggest challenge is convincing partners and donors to also be willing to accept mistakes and ignore vanity metrics. Tsai thinks that getting governments to buy into civic tech projects can help create an innovation culture that values failure and rapid learning, and thus leads to more productive work. “Many times, civic tech projects start and end with citizens as users, and leave out the government side,” she says. “Designing with government as an end user is critical to the success of any civic tech project.”

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4 Innovations that Use Technology to Solve Social Issues

From mobile health and telemedicine to educational apps and microfinance, these nonprofits are all about positive impact..

(CECIL BO DZWOWA / Shutterstock.com)

While technology is the root of some profound instances of disconnection between people, it’s worth noting that many positive forms of interaction have also stemmed from the digital age - particularly in the nonprofit world. Charitable organizations have increasingly started to soak up technological innovations and approaches to solving societal problems in recent years. From mobile health and telemedicine to educational apps and micro-finance, digital advancements have greatly benefited a number of nonprofits, giving  them the tools to create lasting change. The following four organizations are particularly innovative in their uses of technology for tackling today’s most pressing social issues.

1. EDUCATION TECHNOLOGY

NONPROFIT: Worldreader While many of us may miss the smell and feel of a paperback, the advent of digital books has been enormously useful for this nonprofit. Worldreader is working towards “creating a literate world.” According to UNESCO, literacy has the power to be a game changer. It can lead to a higher income and help break the cycle of poverty. Despite this great potential, 250 million school-aged children do not have elementary reading and writing abilities and 740 million people are illiterate. As Worldreader tells us, 50 percent of African schools have zero (or close to zero) books. Worldreader works to increase access to books and the experience of reading. Realizing that the primary way of achieving this is through technology—specifically, e-readers—they launched Worldreader Mobile, enabling anyone with a cell phone or tablet to utilize the organization’s digital library. Worldreader also works on curating books with genres that are accessible to children in different areas—such as books by African or Indian authors—recognizing that the more interested and engaged children are when they read, the more they want to read.

2. HEALTH TECHNOLOGY

NONPROFIT: CareMessage Based in San Francisco, CareMessage utilizes mobile technologies in an effort to improve healthcare in underprivileged areas. The nonprofit focuses on enhancing patients’ ability to self-manage their health (taking medications properly and attending appointments), expanding health literacy, and improving care in general in underserved populations primarily through text messaging. CareMessage stemmed from CEO Vineet Singal’s personal experience at a Galveston, Texas free clinic. As a volunteer, he witnessed the substantial health knowledge deficits of the lower socio-economic patients. He conferred with Corral and Rivera, and CareMessage was born. Through CareMessage’s platform, physicians can send appointment reminders (and receive RSVPs), collect data (from surveys using analytics engine), use filters for demographics or conditions, provide educational programs through texting, and create patient profiles. CareMessage also has handcrafted programs to help patients manage behavior changes (nutrition, medication management), chronic disease (hypertension, diabetes), women’s health (maternal health), pediatric health, and even mental health. CareMessage hopes to help improve the health of more than a million people by 2016 year’s end.

3. WELFARE TECHNOLOGY

NONPROFIT: Samasource We’ve seen how nonprofits are using technology for healthcare and education, but what about poverty alleviation? Enter: Samasource. “Sama” means “equal” in Sanskrit, and sure enough, social entrepreneur Leila Janah founded the organization in 2008 with the goal of working towards global economic equality. Samasource’s vision is “to connect the one billion people living in poverty around the world to work using the power of technology.” Samasource utilizes a microwork model that rests on the notion that even complicated assignments can be broken down into simple distinct tasks.

Through this framework, Samasource provides women and youth with simple digital tasks that are part of a larger data project, enabling them to develop tech skills, while also helping them to make the transition out of poverty.  Before working through Samasource, 92% of the workers are under-employed or unemployed. Six months of work at Samasource yields an average income increase of 114%; 89% of the workers seek out other work and educational opportunities when they finish at Samasource. Workers are able to make lasting improvements in the lives of their families, including bringing in healthier food, acquiring safer housing, and providing their children with educational opportunities. 

4. HUMAN RIGHTS TECHNOLOGY

Nonprofit: polaris project.

It is difficult to believe that slavery exists in our contemporary society and affects 20.9 million people in the world. Katherine Chon and Derek Ellerman were appalled when they encountered an article on the terrible state of a brothel near their campus during their senior year at Brown. When police raided the building they came across six Asian women who were “being held in a situation of debt bondage.” They founded Polaris in 2002, an organization whose name is based on the North Star that 19th century American slaves followed to freedom. One day after Commencement, the Brown graduates moved to D.C. and opened an office. In the beginning, Katherine and Derek created a victim outreach program to locate trafficking places and networks, and help victims obtain services. They soon worked with other partners to bring bills to Congress and introduce legislation that protects victims while penalizing offenders. Polaris made the National Human Trafficking Resource Center into a national anti-slavery hotline in 2007, which is available in over 200 languages, and a place where callers can report a tip or receive anti-trafficking services; in March 2013 they established a texting option where victims can text HELP or INFO to “BeFree.”

YOU MIGHT ALSO LIKE:

8 Videos to Make You Think About How You Use Technology 4 Mind-Blowing Examples of 3D Printers Being Used For Good 7 of the Best Apps for People with Disabilities This article by Victoria Gaffney was originally published on The Culture-ist , and appears here with permission.

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A Guide to Solving Social Problems with Machine Learning

• Jon Kleinberg,
• Jens Ludwig,
• Sendhil Mullainathan

Predictive technology can improve public policy — if we use it right.

It’s Sunday night. You’re the deputy mayor of a big city. You sit down to watch a movie and ask Netflix for help. (“Will I like Birdemic? Ishtar? Zoolander 2?”) The Netflix recommendation algorithm predicts what movie you’d like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don’t believe it’s possible to forecast which families will wind up on the streets.

• Jon Kleinberg is a professor of computer science at Cornell University and the coauthor of the textbooks Algorithm Design (with Éva Tardos) and Networks, Crowds, and Markets (with David Easley).
• JL Jens Ludwig is the McCormick Foundation Professor of Social Service Administration, Law and Public Policy at the University of Chicago.
• SM Sendhil Mullainathan is a professor of economics at Harvard University and the coauthor (with Eldar Shafir) of Scarcity: Why Having Too Little Means So Much.

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• Published: 02 July 2024

Big data analytics capability and social innovation: the mediating role of knowledge exploration and exploitation

• Nan Wang 1 , 2 ,
• Baolian Chen 3 ,
• Liya Wang 3 ,
• Zhenzhong Ma   ORCID: orcid.org/0000-0003-3012-2810 4 , 5 &
• Shan Pan 1  

Humanities and Social Sciences Communications volume  11 , Article number:  864 ( 2024 ) Cite this article

Metrics details

• Business and management
• Information systems and information technology

While many organizations have successfully leveraged big data analytics capabilities to improve their performance, our understanding is limited on whether and how big data analytics capabilities affect social innovation in organizations. Based on the organizational information processing theory and the organizational learning theory, this study aims to investigate how big data analytics capabilities support social innovation, and how knowledge ambidexterity mediates this relationship. A total of 354 high-tech companies in China, this study shows that big data analytics management, big data analytics technology, and big data analytics personnel capabilities all have positive effects on social innovation. In addition, both knowledge exploration and knowledge exploitation play a mediating role in this process. Furthermore, a polynomial regression and response surface analysis shows that social innovation increases when knowledge exploration and knowledge exploitation are highly consistent but declines when knowledge exploration and knowledge exploitation are inconsistent. This study not only provides new perspectives for understanding how big data analytics capabilities contribute to social innovation, complementing the existing literature on big data analytics capabilities and social innovation, but also provides important practical guidance on how organizations can develop big data analytics capabilities to improve social innovation and solve social problems in the digital age.

Introduction

The increased concerns with sustainability in the global context have prompted organizations to pay more attention to social innovation in their business operations (Saka-Helmhout et al., 2022 ; Tjörnbo and McGowan, 2022 ). Social innovation as a socially-orinted innovation not only can help solve social problems more effectively, but also can provide organizations with opportunities to enhance their sustainable competitive advantage (Carayannis et al., 2020 ; Wang et al., 2023a ). However, social innovation also brings resource challenges to organizations, such as challenges in capital, talent, and knowledge (Saka-Helmhout et al., 2022). An increasing number of organizations have thus turned to big data analytics capabilities to crack the resource pressure in order to deal with these challenges (Bonina et al., 2021 ).

Big data analytics capabilities have been found capable of facilitating organizations to address social issues and create sustainable values (Ashaari et al., 2021 ; Said et al., 2023 ; Wang and Hajli, 2017 ). However, most existing studies have explored the economic performance of big data analytics capabilities (Ferraris et al., 2019 ; Mikalef et al., 2019 ), somehow ignoring big data analytics capabilities’ impact on social elements. The relationship between big data analytics capabilities and social innovation has not been adequately examined in the literature (Calic and Ghasemaghaei, 2021 ; Krishnamurthy and Desouza, 2014 ), even though it is well known that social innovation places the emphasis more on the creation of social values. Therefore, the relationship between big data analytics capabilities and social innovation in the organizational context remains unclear (Agarwal et al., 2018 ; Wang et al., 2023a ). In response to this research gap, this study attempts to explore whether big data analytics capabilities affect social innovation from the information processing perspective.

In addition, it is equally important to understand how big data analytics capabilities affect social innovation in order to generate more practical implications in the organizational context. It is contended in this study that big data analytics capabilities may enhance social innovation through the process of knowledge management (Unceta et al., 2016 ). On the one hand, the organizational learning theory shows that organizations can increase their capabilities to compete by exploring and exploiting knowledge (Andriopoulos and Lewis, 2009 ; Crossan et al., 1999 ; Wang et al., 2023b ), during which big data analytics capabilities can support employees to explore and exploit internal and external knowledge and thus facilitate the organizational learning process (Gupta and George, 2016 ). On the other hand, the urgent need for more social innovation in the organizational context also drives organizations to utilize their knowledge exploration and exploitation capabilities to generate the information needed for social innovation, which again can be facilitated by big data analytics capabilities (Unceta et al., 2016 ). Therefore, this study expects that knowledge exploration and exploitation mediate the relationship between big data analytics capabilities and social innovation. Furthermore, in most cases, knowledge exploration and knowledge exploitation are interrelated in a complex way that they can mutually reinforce or counteract the influence they have on organizational learning, depending on their configurations (Li et al., 2018 ). While past research has shown that either knowledge exploration or knowledge exploitation helps firms solve social problems (Unceta et al., 2016 ; Xu et al., 2022 ), little is known what is their joint effect on social innovation. To bridge such a gap, this study also attempts to explore the joint impact of knowledge exploration and exploitation on social innovation.

In order to answer the research questions discussed above, this study adopts the organizational information processing theory and the organizational learning theory to explore the relationships between big data analytics capabilities, knowledge exploration and exploitation, and social innovation. Based on the data from 354 Chinese high-tech firms, we aim to shed light on the impact of big data analytics capabilities on social innovation, and on the optimal configuration of knowledge exploration and knowledge exploitation in affecting social innovation. The findings of this study can expand the research on social innovation and bridge the research gaps in the relationship between big data analytics capabilities and social innovation, which helps gain a more comprehensive understanding of knowledge and technology requirements of social innovation. The results also provide useful and timely guidance for organizations to develop social innovation for better organizational performance. The remaining sections of this study are organized as follows: Section 2 provides a literature review; Section 3 develops hypotheses; Sections 4 and 5 provide empirical analysis and results, and the final section summarizes findings and implications of this study.

Literature review

Organizational information processing theory.

The organizational information processing theory views information and its processing and management as a key factor to organizational performance. Organizational information processing theory holds that when organizations attempt to complete uncertain or ambiguous tasks, they need to simplify information requirements or enhance information processing capabilities through a series of organizational system designs to effectively utilize and manage information and to cope with market uncertainties for optimal firm performance (Galbraith, 1974 ; Gupta et al., 2019 ). As uncertainty increases, information processing capabilities must also increase to accommodate information requirements (Yu et al., 2021 ). Information processing capabilities with strong ability to collect, analyze, and integrate data can cope with changes in uncertain market environments and thus promote innovation (Yu et al., 2021 ; Xie et al., 2022 ).

Organizational information processing theory contends that organizations are able to enhance their information processing capabilities by investing in vertical information systems and by building horizontal relationships (Galbraith, 1974 ; Srinivasan and Swink, 2018 ). On the one hand, big data analytics capabilities, as an emerging big-data based information system, can provide organizations with an effective way to process acquired data, help accurately predict risks in the external environment (Liu et al., 2022 ), and efficiently deploy resources to meet vertical information processing requirements (Dubey et al., 2019 ), thereby improving the efficiency of organizations innovation decisions. On the other hand, as an essential means for organizations to build horizontal relationships, knowledge management capabilities are often closely related to organizational processes and interactions (Yu et al., 2021 ). Knowledge management capabilities can assist organizations in building relationships with external partners for information acquisition and integrating external information into internal knowledge systems, which also helps improve innovation decisions (Srinivasan and Swink, 2018 ). In addition, as social innovation mainly exists in highly ambiguous contexts, organizations need to use big data analytics capabilities and knowledge management capability to analyze and integrate relevant data both horizontally and vertically in order to promote effective decision-making for social innovation. As a result, organizational information processing theory provides a suitable theoretical framework for a better understanding of the relationship between big data analytics capabilities, knowledge exploration and knowledge exploitation, and social innovation, with big data analytics capabilities as a vertical information system and knowledge management capabilities as a horizontal information system.

Organizational learning theory

The central idea of organizational learning theory is that organizations develop new knowledge and insights from experiences, which has the potential to contribute to organizational behavior and improve future organizational performance (Argote and Hora, 2017 ). March ( 1991 ) classified organizational learning into exploration and exploitation, where exploration includes things captured by such as search, change, adventure, experimentation, play, flexibility, discovery, and innovation, and exploitation includes things such as improvement, selection, production, efficiency, choice, implementation, and execution. On this basis, Benitez et al. ( 2018 ) integrated the exploration and exploitation activities of organizational learning into the field of knowledge management, proposing knowledge exploration and knowledge exploitation. Through the process of organizational learning, organizations are able to facilitate the generation and development of competencies that enhance the organization’s innovativeness and performance and its sustainable competitive advantage (Real et al., 2006 ; Ghasemaghaei and Calic, 2019 ). Therefore, drawing on the framework of organizational learning theory, this study considers knowledge exploration and knowledge exploitation as two types of learning activities for firms (Gupta et al., 2006 ), and explains how these two different types of learning activities can better contribute to the process of social innovation.

Big data analytics capabilities

Big data analytics capabilities refer to the abilities to leverage data management, technology, and personnel resources to obtain business insights and boost competitiveness to realize full strategic potentials, and big data analytics capabilities thus consist of big data analytics management capabilities, big data analytics technology capabilities, and big data analytics personnel capabilities (Akter et al., 2016 ; Kiron et al., 2013 ; Lavalle et al., 2011 ; Wang et al., 2023 ). Among them, big data analytics management capabilities include the planning, coordination, investment, and control of big data analytics (Kiron et al., 2013 ). Big data analytics technology capabilities are the information systems that collect, store, process, and analyze big data (Rialti et al., 2019 ), and big data analytics personnel capabilities include management, technical, business, and relationship capabilities (Wamba et al., 2017 ).

As shown in Table 1 , existing studies often focus on the link between big data analytics capabilities and innovation, including green innovation, supply chain innovation, business model innovation, eco-innovation and dual innovation, mainly from a dynamic capability view and a resource-based view (Al-Khatib, 2022 ; Bhatti et al., 2022 ; Ciampi et al., 2021 ; Munodawafa and Johl, 2019 ; Su et al., 2022 ). However, only a few studies have explored big data analytics capabilities to help organizations solve social problems, and they are often based on qualitative case methods, Ashaari et al. ( 2021 ) state that big data analytics capabilities can drive data to improve decision-making in educational institutions and improve public education. Wang and Hajli ( 2017 ), and Mani et al. ( 2017 ) highlight that big data analytics capabilities can help healthcare organizations to analyse, predict and decide on patient data in a timely manner. However, few have employed empirical methods to examine the impact of big data analytics capabilities on social innovation in for-profit organizations that consider both economic and social effects. This study focuses on how big data analytics capabilities can be used to drive the development of social innovation.

Social innovation

Scholars have explored the definition of social innovation from different perspectives. On the one hand, social innovation is sometimes viewed as a social exchange process that integrates multiple promoting elements to address social needs and societal issues (Olszak, 2014 ; Neumeier, 2012 ). On the other hand, social innovation also focuses on the result of exploring products, services, and business models to meet social needs and increase economic profits (Wamba et al., 2017 ). Therefore, this study defines social innovation as a practical process by which organizations and stakeholders solve social problems that are difficult to solve by market or government in order to promote social justice and improve social living conditions, and ultimately create social and economic value for the whole society.

Social innovation is dynamic and complex, and it is influenced by different factors from organization, society, and technology, as shown in Table 2 . Past research has discovered that social entrepreneurship, knowledge networks and corporate strategic orientation as organizational factors aid in the promotion of social innovation and the provision of long-term solutions to social problems (Ho and Yoon, 2022 ; Krlev et al., 2014 ; Mirvis et al., 2016 ). Social factors include institutional and environmental factors, where both institutional gaps and environmental unrest affect the growth of social innovation (Gasparin et al., 2021 ; Guerrero and Urbano, 2020 ; Onsongo, 2019 ). Further, IT can stimulate the realization of social innovations through enablement and generate social impacts in areas such as education, employment, environment and healthcare (Fursov and Linton, 2022 ; Suseno and Abbott, 2021 ). It has been noted that big data analytics(BDA) is beginning to be used as a new IT tool to support the development of social innovation (Batko, 2023 ). With the support of big data analytics capabilities, firms can quickly access and analyze huge amounts of data and derive important and useful information (Mikalef et al., 2018 ), providing support for companies to achieve social innovation. However, the challenge of how big data analytics capabilities can access and analyze data for social innovation by allocating different resources is currently unresolved. Therefore, this paper will delve into the complex role of big data analytics capabilities in influencing social innovation through empirical research.

Knowledge ambidexterity

The organizational ambidexterity theory suggests that organizations that are able to simultaneously explore new knowledge while exploiting current knowledge can outperform their rivals while enhancing innovation, competitive advantage, and business sustainability (O’Reilly and Tushman, 2013 ). Based on the organizational ambidexterity theory and the organizational learning theory, scholars have explored the knowledge ambidexterity that encompasses knowledge exploration and knowledge exploitation (Benitez et al., 2018 ), where knowledge exploration emphasizes the discovery and pursuit of new or unresolved knowledge, skill, and processes, and is the stage of introducing new practices (Koryak et al., 2018 ); Knowledge exploitation is the practice of reusing, transforming, and applying existing or new knowledge in an organization to meet current needs and ensure survival (Crossan et al., 1999 ).

In order to achieve enterprise knowledge ambidexterity, scholars have focused on the influential role of IT infrastructure and IT capabilities (Benitez et al., 2018 ; Beck et al., 2014 ). Specifically, IT capabilities that rely on various digital technologies (e.g., big data capabilities, Internet capabilities) facilitate firms’ access to new knowledge as well as the transformation of knowledge into usable and accessible forms for application in the organization (Ferraris et al., 2019 ; Javed et al., 2022 ). Moreover, knowledge management is an effective way for firms to realize social innovation (Maalaoui et al., 2020 ). Allal-Cherif et al. ( 2022 ) found social innovation depends on external knowledge exploration by multiple parties and firms’ efforts to transform knowledge into technologies and products. Therefore, this paper will focus on the process of developing knowledge ambidexterity through big data analytics capabilities, so as to promote the development of corporate social innovation.

Big data analytics capabilities and social innovation

Based on organizational information processing theory, big data analytics capabilities act as an organizational information processing capability that permits companies to improve data-driven decision-making and innovation ways, and is an critical driver for survival and growth of firm (Ferraris et al., 2019 ; Su et al., 2022 ). Studies have pointed out that big data analytics capabilities is a higher-order multidimensional construct that includes big data analytics management capabilities, big data analytics technology capabilities and big data analytics personnel capabilities (Akter et al., 2016 ). Based on organizational information processing theory, this study will explore the relationship between big data analytics capabilities and social innovation with big data analytics capabilities consisting of managerial capability, technological capability and personnel capability respectively.

Big data analytics management capabilities and social innovation

Big data analytics management capabilities refer to the business choices made by organizations and consists of four basic components: planning, investing, coordinating, and controlling (Akter et al., 2016 ). In highly uncertain environments, it becomes particularly important for businesses to embrace and improve big data analytics management capabilities to support social innovation. Big data analytics management capabilities begins with proper big data analytics planning process that identifies business opportunities and determines how big data-based models can enable innovation (Barton and Court, 2012 ). During the business planning process, companies can prioritize innovation to solve social problems. Big data analytics investments respond to cost effects and can help firms to develop smarter strategies based on investing in analyses of huge amounts of data (Akter et al., 2016 ). For example, big data analytics investments can be used to assist companies in adapting and developing strategies for sustainable growth. By reducing the cost of green product development and increasing profits, they can improve their competitive advantage while addressing social issues (Verhoef et al., 2016 ). In addition, the coordination and control of big data analytics facilitates cooperation between various business activities. By allocating resources and information between departments in a timely manner, it ensures efficient use of resources (Bag et al., 2020 ), enable continuous monitoring of innovation capabilities (Akter et al., 2016 ). Based on the big data analytics coordination and big data analytics control, enterprises can obtain information about social issues, collaborate with enterprise departments to allocate resources and information, and help enterprises effectively implement social innovation. Accordingly, we propose a first hypothesis:

H1a: Big data analytics management capabilities are positively related to social innovation .

Big data analytics technology capabilities and social innovation

Big data analytics technology capabilities are tool that can assist data technicians in developing, deploying, and supporting business extensions with connectivity, compatibility, and modularity (Akter et al., 2016 ). It can help organizations to be more aware of market trends, the business environment and social issues, and provide new directions and guidelines for social innovation. Technologies such as sensors and Radio Frequency Identification in big data analytics technology capabilities allow for product traceability recall, remanufacturing, recycling and reuse at the point of production (Okorie et al., 2018 ). These technologies not only increase the effectiveness and recycling of materials and the sustainability of businesses (Awan et al., 2021 ; Rashidin et al., 2021 ), but also minimize the social problem of waste in the production process and increase social innovation. big data analytics technology capabilities also enables enterprises to collect and analyse data faster and more accurately, helping them to gain access to vital information related to consumer behavior and preferences (Su et al., 2022 ). Enabling social innovation by modeling various social scenarios. Sustainably changing energy production and consumption, improving its structure (Mikalef et al., 2020 ), eradicating poverty, and solving social problems (Alnuaimi et al., 2021 ). Thus, we propose the hypothesis:

H1b: Big data analytics technology capabilities are positively related to social innovation .

Big data analytics personnel capabilities and social innovation

Big data analytics personnel capabilities refer to the technical, technology management, business, and relational capabilities of data scientists to perform specific tasks in a big data environment, which are widely considered to play an important role in fostering innovation (Akter et al., 2016 ). Big data analysts can gather a variety of valuable information about the market and consumers by effectively integrating and analyzing big data (Müller et al., 2018 ; Deng et al., 2024 ). This information helps organizations to better understand market trends, guide business operations, and improve the quality of product and service development (Su et al., 2022 ). In terms of social value, big data analytics personnel capabilities has made a significant contribution to creating eco-friendly products and raising the social awareness of employees within the organization. Examples include increased compliance with legal requirements, protection from social and environmental issues, and corporate social innovation (Alnuaimi et al., 2021 ; Bag et al., 2020 ). Big data analytics personnel capabilities can also enhance data-driven insights, increase the level of understanding of business staff about current social issues and innovation generation. Improve material efficiency through effective decision-making and the use of technology to redesign products and services, and help organizations achieve a circular economy and promote social innovation (Awan et al., 2021 ). Thus, we propose the hypothesis:

H1c: Big data analytics personnel capabilities are positively related to social innovation .

The mediating role of knowledge ambidexterity

One of the main issues in the digital age is how to extract the necessary facts from large amounts of data and transform them into usable new knowledge. With the support of big data analytics capabilities, enterprises utilize data management, technical and person to obtain information (Akter et al., 2016 ), and enhance innovation capabilities through knowledge exploration and knowledge exploitation (Benitez et al., 2018 ).

First off, big data analytics management capabilities can support knowledge exploration at the strategic level of the organization by extracting the correct information from the data (Ferraris et al., 2019 ). Enterprises will spend a significant amount of money building knowledge management infrastructure (Sun et al., 2019 ). And then they can choose from a variety of methods of knowledge exploration through access, contextualization, experimentation, and application of big data insights. Secondly, big data analytics technology capabilities provide enterprises with a constant flow of external information to tap into the original ideas of different types of users in the innovation ecosystem (Zeng et al., 2010 ), and enrich the company’s knowledge base. Finally, big data analytics personnel capabilities provide staff support for knowledge exploration. Previous research has overemphasized the influence of data software on knowledge ambidexterity and neglected the role of data analysts (Conboy et al., 2020 ). Organizations with excellent data analysts can achieve knowledge discovery by collecting, observing, analyzing, and condensing large amounts of fresh, unstructured information to rapidly generate new insights and valuable knowledge (He et al., 2015 ). Thus, we propose the following hypotheses:

H2a : Big data analytics management capabilities are positively related to knowledge exploration .

H2b : Big data analytics technology capabilities are positively related to knowledge exploration .

H2c: Big data analytics personnel capabilities are positively related to knowledge exploration .

In the dynamic perspective of knowledge, big data analytics management capabilities ensure knowledge application (Oeij et al., 2019 ). Big data analytics management capabilities’s planning, co-ordination and control functions can be used to analyse disparate data to discover useful information and use it to improve knowledge exploitation. These functions can also be used to define big data analytics models used by the enterprise and build a cross-functional synchronization of the entire company analysis activities (Kiron et al., 2013 ). Big data analytics technology capabilities provide companies with various types of knowledge exploitation tools to improve coordination up and down the supply chain and to flexibly and quickly convert and exploit new organizational knowledge (Chen et al., 2017 ). In addition, IT infrastructure within the organization enhances internal coordination by facilitating cross-functional communication, allowing employees to share their business ideas and offer solutions to streamline the knowledge exploitation (Benitez et al., 2018 ). In big data analytics personnel capabilities, the business and interpersonal skills of big data analysts can support analysts to communicate and collaborate with others to understand the development needs of the market. It also generates new knowledge in the process of communication, improves the ability of the firm to use the knowledge inventory in a variety of situations, and facilitates knowledge exploitation in organizations (Nwankpa et al., 2022 ; Gebauer et al., 2020 ). Based on the above analyses, we propose the following hypotheses:

H3a : Big data analytics management capabilities are positively related to knowledge exploitation .

H3b : Big data analytics technology capabilities are positively related to knowledge exploitation .

H3c : Big data analytics personnel capabilities are positively related to knowledge exploitation .

Knowledge-based social services have been shown to help firms achieve innovation and improve innovation performance (Desmarchelier et al., 2020 ). Knowledge exploration can produce more cutting-edge analytical capabilities and knowledge resources, which can help organizations overcome difficulties in innovation (Xiao and Oh, 2021 ). Knowledge exploitation enables organizations to continuously improve their understanding of knowledge, identify and absorb corporate knowledge more effectively. It also enables the creation of new models of innovation and the creation of value through digital technologies to improve innovation outcomes (Benitez et al., 2018 ). When an organization must apply social innovation in a different culture, it must survey relevant information with its partners or users to build the knowledge resources needed for social innovation through dialog and communication (Herrera, 2015 ). Based on the above analysis, we propose the following hypotheses:

H4a : Knowledge exploration is positively correlated with social innovation .

H4b : Knowledge exploitation is positively correlated with social innovation .

In order to ensure that firms are able to gain a competitive advantage in a turbulent environment, organizations apply big data analytics capabilities to appropriate management frameworks to ensure that reliable business decisions are made (Akter et al., 2016 ). In practical, big data analytics capabilities require organizations to realize social innovation through the exploration and exploitation of knowledge in order to satisfy the unity of economic and social value.

As a dynamic capability, big data analytics management capabilities can help enhance the knowledge exploration ability of enterprises and enable them to obtain the required knowledge (Shamim et al., 2021 ). By constantly exploring knowledge, they can track unpredictable market trends and understand social problems and trends, so as to help companies to generate new solutions to address social issues and increase their social innovation. Secondly, big data analytics technology capabilities provide technical support for organizations to conduct knowledge exploration. By using big data analytics technology capabilities, organizations may acquire new knowledge from external markets and share knowledge with partners (Castillo et al., 2021 ). Thus, information about social innovation in the market is obtained, providing knowledge to help organizations realize social innovation. Finally, studies have shown that the big data analytics personnel capabilities can bring about changes in knowledge management and increase and expand personal knowledge (Pauleen, 2009 ). Big data analysts can thus work closely with other business department personnel to achieve knowledge and technology sharing in the communication process. Organizations can also obtain information about social issues in the collaboration and help solve them through social innovation. Based on the above analysis, we propose the following hypothesis:

H5a: Knowledge exploration mediates the relationship between big data analytics management capabilities and social innovation .

H5b: Knowledge exploration mediates the relationship between big data analytics technology capabilities and social innovation .

H5c: Knowledge exploration mediates the relationship between big data analytics personnel capabilities and social innovation .

Organizations equipped with big data analytics management capabilities are adept at feeding back the meaning of data-driven insights to internal departments (Mikalef et al., 2018 ). Internal knowledge development helps improve internal knowledge exploitation and foster technological and process advancements, which is beneficial for social innovation. Meanwhile, big data analytics technology capabilities can help organizations eliminate production failures and improve production techniques faster (Wang et al., 2018 ). The improvement in the production process through knowledge exploitation enables organizations to realize social innovation faster and contribute to the solution of social problems. In addition, when organization personnel master big data technology and business knowledge, they are more likely to transform them into actual innovations (Su et al., 2022 ). With big data analytics personnel capabilities, organizations can help achieve knowledge exploitation, reduce the failure in innovation transformation, and provide a knowledge base for organizations to carry out social innovation. Based on the above analysis, we propose the following hypothesis:

H6a : Knowledge exploitation mediates the relationship between big data analytics management capabilities and social innovation .

H6b: Knowledge exploitation mediates the relationship between big data analytics technology capabilities and social innovation .

H6c: Knowledge exploitation mediates the relationship between big data analytics personnel capabilities and social innovation .

Configurations of knowledge ambidexterity and social innovation

Considering that both knowledge exploitation and knowledge exploration are important contributors to social innovation, it is important to understand how the configuration of knowledge exploration and knowledge exploitation drives social innovation. There are four pairs of different configuration between knowledge exploration and exploitation, and among them, “high exploration-high exploitation” and “low exploration-low exploitation” being examples of consistent ability, and “high exploration-low exploitation” and “low exploration-high exploitation” being examples of inconsistent ability, as shown in Fig. 1 .

Knowledge ambidexterity combination configuration.

In the “high exploration-high exploitation” scenario, high knowledge exploration can help firms to acquire new knowledge related to social innovation from both inside and outside the organization, expanding the firm’s knowledge base and encouraging innovative thinking and idea sharing within the firm (Benitez et al., 2018 ). It can also provide access to different social information, perceive social problems, help organizations to see problems from different perspectives (Nicolopoulou et al., 2017 ), transform their potential knowledge into realized innovation (Cheng and Sheu, 2023 ), and thus can improve social innovation. High knowledge exploitation can encourage the use of a wide range of knowledge in the existing knowledge base to transform product development and design, increasing the competitive advantage of the firm (Sandberg and Aarikka-Stenroos, 2014 ). It also enables organizations to address social problems by creating service offerings that better meet the needs and expectations of local communities, which enhances social innovation (Ndou and Schiuma, 2020 ). In the “low exploration-low exploitation” scenario, it is difficult for firms to solve social problems because of weak exploration and exploitation capabilities, which make it difficult for organizations to acquire cutting-edge knowledge from external sources to update their knowledge base or create new knowledge. Therefore, we propose the following hypothesis:

H7a: The level of social innovation is higher when both knowledge exploration and knowledge exploitation are high than when both are low .

Not all companies can carry out highly balanced knowledge exploration and knowledge exploitation. Therefore, it is also important to consider the effects of unbalanced knowledge exploration and knowledge exploitation on social innovation. The unbalanced knowledge ambidexterity includes “high exploration-low exploitation” and “high exploitation-low exploration”, both of them can be detrimental to the development of social innovation in organizations. When organizations are in the state of “high exploration-low exploitation”, they get more fresh information and ideas from outside. However, excessive exploration may make it difficult for organizations to understand, absorb, and apply unfamiliar technologies inside the organizations (Fleming and Sorenson, 2001 ), resulting in increased search costs. Moreover, low knowledge exploitation cannot provide a foundation to transform acquired new knowledge, resulting in localization challenges in absorbing new knowledge (Ferreira et al., 2020 ),constraining social innovation in organizations. When organizations are in the state of “high exploitation-low exploration”, they only obtain knowledge from their own existing knowledge bases. They are not able to obtain sufficient new information or ideas from external organizations, which are essential for their own social innovation. As a result, social innovation is also limited, resulting in the trap of familiarity (Li et al., 2018 ). Therefore, we propose the following hypothesis:

H7b: When the imbalance between knowledge exploration and knowledge exploitation increases in either direction, social innovation will decline .

Combining the above assumptions, we developed a conceptual model, which is shown in Fig. 2 .

Research model.

Research design

Respondent profiles.

To test our hypotheses, we surveyed Chinese high-tech firms’ CEOs and CIOs with information technology experience. The Digital China Development Report (2022) shows that China is the world’s second largest data-producing country and has a high level of information technology adoption, which enables Chinese high-tech firms to use big data analytics capabilities to analyse large amount of data to create innovation opportunities. At the same time, Chinese high-tech companies often emphasize technology for good causes and thus actively address social issues through new technologies. In this study, we set the following sampling criteria: (1) participating firms must have been concerned about big data analytics capabilities and social issues in the last five years; (2) participating firms must have complete email contact information for their CEOs and CIOs so that they can be reached by emails.

Sample and data collecting processes

We used a random sampling technique to collect data. As “the statistical analysis report on the development of China’s high-tech industry in 2020” states that Beijing, Zhejiang, Jiangsu, and Guangdong are home to a large number of high-tech companies in China, we randomly selected a sample of about 500 high-tech firms focusing on big data and social innovation through a local government’s enterprise information database in Beijing, Zhejiang, Jiangsu, and Guangdong, and then distributed questionnaires to their CIO and CEO. The CIOs and CEOs were chosen to distribute the questionnaire because they are familiarize with corporate digital strategy and have the knowledge of social orientation in their organizations, and also have a clear understanding of the company’s knowledge exploration and knowledge exploitation. We emailed a questionnaire to the CIOs of these companies covering basic information, big data analytics capabilities, and knowledge ambidexterity strategies in Time 1 (T1). In the end, 463 questionnaires were returned, of which 442 were valid. One year later (T2), we sent questionnaires by E-mail to the CEOs of these 442 companies that had returned valid questionnaires in T1 to collect data on social innovation. 402 questionnaires finally returned, of which 354 were valid. As shown in Table 3 , the questionnaire asked the respondents about their gender, age, time of using big data analytics capabilities, age of the company, industry, and the size of the business.

Measurement of variables

All variable were measured using the scales designed based on well-known scales that have been widely used in previous research, and a two-way translation procedure was utilized to translate the scales. To ensure the validity of the scale, we contacted two experts in the fields of information systems and strategic management to review our questionnaire. According to experts’ comments and suggestions, we further modified it to guarantee that all items were content valid. All items were validated on seven-point Likert scales ranging from 1 = “strongly disagree” to 7 = “strongly agree”. Specific variables were measured as follows:

Big data analytics management capabilities, big data analytics technology capabilities and big data analytics personnel capabilities are the independent variables in this research. The scales were adapted from those used by Akter et al. ( 2016 ). The big data analytics management capabilities scale has 16 items, the big data analytics technology capabilities scale has 12 items, and the big data analytics personnel capabilities scale has 16 items.

Knowledge exploration is a mediating variable in this research. The scale was adapted from the one used by Cegarra-Navarro et al. ( 2011 ), with five question items.

Knowledge exploitation is another mediating variable in this research. The scale was adapted from the one used by Arias-Pérez et al. ( 2021 ), with five question items.

Social innovation is the dependent variable in this study. The scale was adapted from the scale used by Adomako and Tran ( 2022 ) and consists of five question items. Detailed measurements are shown in Table 4 .

In addition, firm age, firm size and industry category are used as control variables as they may affect firms’ innovative behavior. The details of the scale are shown in Table 5 .

Analytical methods

A quantitative research method was used in this study. SPSS software, and AMOS software were used to analyze and process the data to maximize the validity of the questionnaire data testing (Jarjabka et al., 2024 ). In particular, SPSS analysis software was used to calculate the reliability and validity of the data, multiple regression, and response surface analysis. AMOS was used on construct structural methodological models to test hypotheses. The details of the scale are shown in Fig. 3 .

Research procedure.

Reliability and validity

In this study, SPSS 25.0 was used to analyze the reliability of each variable. From the results in Table 6 , the Cronbach’s α values of variables are all greater than 0.7, above acceptable levels. The KMO of each variable is greater than 0.7, and the Bartlett’s spherical test is significant, which was suitable for factor analysis. AVE values are all greater than 0.5 (Netemeyer et al., 2003 ), and CR values are all greater than 0.8 (Nunnally, 1994 ), indicating that the scale has good convergence validity and internal consistency. To examine discriminant validity, the correlation shared between the square AVE of the construct and any other construct is compared (Fornell and Larcker, 1981 ). As shown in Table 7 , the measurement models have enough discrimination validity because the squared AVE is bigger than the shared correlation between the constructs. In general, all measures have sufficient reliability and validity.

Common method bias

We used procedural remedies and statistical tests to avoid common method bias. First, the dependent variable was collected in a different questionnaire from other variables and we made sure that everyone filled these questionnaires out anonymously. Second, we used Harman’s one-way analysis of variance to test the common method bias (Harman, 1976 ), and the data showed that the unrotated first factor explained only 26.42% of the variance (less than 30%). In addition, we compared the fit of a one-factor model and the measurement model, with the one-factor model having the worse fit (χ 2 (df) = 1547.677 (299)) than the measurement model (χ 2 (df) = 434.335 (284)). Meanwhile, The RESEA of the measurement model was 0.039, χ 2 /df =1.529, and IFI, CFI, and TLI were all greater than 0.9. Therefore, the results indicate that there is no serious common method bias in this study.

Correlation analysis

The variables in this study were analyzed for correlation using SPSS25.0, and the findings are presented in Table 7 . The correlations between the big data analytics management capabilities, big data analytics technology capabilities, big data analytics personnel capabilities, social innovation, knowledge exploration, and knowledge exploitation are positive. The variables have a positive association, which supports the hypothesis testing in the following section.

Hypothesis testing

Main effects test.

We tested the H1-H4 hypotheses through structural equation modeling using AMOS (Bollen, 1989 ). We examined the VIF values before conducting the main effects test and the data showed that they were all less than 3, indicating that there was no significant multicollinearity problem.

Table 8 and Fig. 4 reports the results of the structural modeling analysis. The results show that big data analytics management capabilities ( β  = 0.194, p  < 0.01), big data analytics technology capabilities ( β  = 0.161, p  < 0.01) and big data analytics personnel capabilities ( β  = 0.299, p  < 0.001) are all significantly and positively associated with social innovation, indicating that H1a, H1b and H1c are all supported. Big data analytics management capabilities ( β  = 0.217, p  < 0.01), big data analytics technology capabilities ( β  = 0.315, p  < 0.001), and big data analytics personnel capabilities ( β  = 0.295, p  < 0.001) all positively affect knowledge exploration, and thus Hypotheses H2a, H2b and H2c are supported. Big data analytics management capabilities ( β  = 0.194, p  < 0.01), big data analytics technology capabilities ( β  = 0.265, p  < 0.001), and big data analytics personnel capabilities ( β  = 0.557, p  < 0.001) also positively influence knowledge exploitation, thus supporting Hypotheses H3a, H3b, and H3c. In the study of knowledge exploration, knowledge exploitation and social innovation, the data suggests that knowledge exploration ( β  = 0.134, p  < 0.05) and knowledge exploitation (β = 0.252, p  < 0.001) positively affect social innovation, and Hypotheses H4a and H4b are supported.

Path analysis diagram.

Mediating effect test

Before testing the mediating effects, we assessed the effect of big data analytics capabilities on the relationship between knowledge exploration and knowledge exploitation, and the effect of knowledge exploration and knowledge exploitation on social innovation. The results in Table 8 show that big data analytics management capabilities, big data analytics technology capabilities, and big data analytics personnel capabilities significantly improve knowledge exploration and knowledge exploitation. Knowledge exploration and knowledge exploitation play an important positive role in social innovation. In order to verify the mediating role of knowledge exploration and knowledge exploitation, we used the Bootstrap mediation effect in SPSS to test it. The results in Table 9 show that the indirect effects of big data analytics management capabilities, big data analytics technology capabilities, and big data analytics personnel capabilities on social innovation through knowledge exploration and knowledge exploitation are all free of 0 in the 95% confidence interval. This suggests that knowledge exploration and knowledge exploitation mediate the impact of big data analytics management capabilities, big data analytics technology capabilities, and big data analytics personnel capabilities on social innovation, and Hypotheses H5a, H6a, H5b, H6b, H5c, and H6c are also supported.

Matching consistency verification

We examined the sample proportion situation. It discovered that the percentage of samples with consistent knowledge exploration and knowledge exploitation was 50%. And the percentage of samples with inconsistent sample proportions of “high knowledge exploitation-low knowledge exploration” and “high knowledge exploration-low knowledge exploitation” were 23.2% and 26.8%, respectively, which met the criteria for polynomial regression. Equation ( 1 ) below is the polynomial regression equation applied in this study, which includes the higher-order term of the two predictors (knowledge exploration and knowledge exploitation), and the square term of the predictor variables and their product (Yao and Ma, 2023 ).

As in Table 10 , the slope of the response surface along the knowledge exploration and knowledge exploitation consistency line is significantly higher than 0 (slope = 0.634, p  < 0.001), and the curvature are not significant. It indicated that “high knowledge exploration-high knowledge exploitation” is promoting social innovation when knowledge exploration and knowledge exploitation are consistent. Hypothesis H7a is supported. As can be seen in Fig. 5 , the higher levels of social innovation are at the back corner of the figure among the fit line of Y = X where knowledge exploration and knowledge exploitation are both high. When Y = −X, the response surface slope and Curvature along the inconsistency line are significantly negative correlated (slope = −0.202, p  < 0.05, Curvature = −0.204, p  < 0.001). This shows that social innovation will decrease after knowledge exploration and knowledge exploitation change from a balanced match to an unbalanced match. H7b was supported. Moreover, as can be seen from Fig. 5 , when the difference of knowledge exploitation is greater than that of knowledge exploration, the degree of social innovation is relatively higher.

Response surface analysis.

Discussions and implications

Although it has been documented that organizations can use big data analytics capabilities to promote product innovation and performance (e.g., Ciampi et al., 2021 ; Ma et al., 2015 ; Mikalef et al., 2019 ; Wamba et al., 2017 ), little is known how big data analytics capabilities affects social innovation and what is the internal mechanism. This study examines the impact of big data analytics capabilities on social innovation and the mediating role of knowledge ambidexterity with a sample of 354 high-tech companies, and further examines the joint influence of knowledge exploration and knowledge exploitation on social innovation. The result show that big data analytics management capabilities, big data analytics technology capabilities, and big data analytics personnel capabilities all have a significant positive impact on social innovation, which provides empirical evidence for the use of big data analytics capabilities to facilitate social innovation (Calic and Ghasemaghaei, 2021 ; Maiolini et al., 2016 ), that is, social innovation can be achieved by increasing big data analytics management capabilities, big data analytics technology capabilities and big data analytics personnel capabilities to enhance the efficiency of social innovation while reducing costs and resource consumption, and to gain access to new information and data needed for social innovation. Second, based on the organizational learning theory and the organizational information processing theory, this study proposes a mediated model on the impact of big data analytics capabilities on social innovation, and the empirical results show that knowledge exploration and knowledge exploitation play a mediating role in big data analytics capabilities and social innovation, further emphasizing the importance of knowledge management in big data analytics capabilities and innovation (Mikalef et al., 2019 ). Big data analytics capabilities can help enhance knowledge exploration and knowledge exploitation to obtain relevant information through joint exploration of new knowledge and exploitation of existing knowledge, increasing the success rate of social innovation. Finally, the response surface analysis shows that the impact of high knowledge exploration – high knowledge exploitation on social innovation is greater than that of low knowledge exploration-low knowledge exploitation. When there is an imbalance between knowledge exploration and knowledge exploitation, the imbalance will lead to the decrease of social innovation. This study develops a perspective to investigate the impact of balanced and unbalanced match between knowledge exploration and knowledge exploitation on social innovation, and thus expands the research on knowledge exploration and knowledge exploitation in social innovation. The findings highlight the importance of knowledge exploration and knowledge exploitation in the process of social innovation.

Theoretical implications

This study contributes to the literature on big data analytics capabilities, knowledge ambidexterity, and social innovation. First, this study is the first to empirically investigate the relationship between big data analytics capabilities and social innovation based on the organizational information processing theory theory and the results show a significant positive relationship between big data analytics capabilities and social innovation, which thus enriches the study of social innovation in the digital age. Previous studies have mainly explored the impact of corporate factors, social factors and technical factors (Gasparin et al., 2021 ; Ho and Yoon, 2022 ; Mirvis et al., 2016 ) on social innovation through theoretical discussions or case studies, but there lacks empirical exploration of the development of social innovation in the big-data based digital context. With the advent of the Industry 4.0 era, more organization are focusing on the use of big data analytics to create new ideas to optimize social relationships and solve social problems (Herrera, 2015 , Maiolini et al., 2016 ). Therefore, this study responds to the call for a better understanding of the role big data analytics capabilities in promoting social innovation (Maiolini et al., 2016 ), and the findings help enrich current innovation management theory on social innovation with a new big data analytics capabilities perspective.

Second, our study explores the mediating role of knowledge exploration and knowledge exploitation in the relationship between big data analytics capabilities and social innovation based on the organizational learning theory, which helps reveal the black box of big data analytics capabilities and social innovation. Previous research on big data analytics capabilities and innovation have been primarily based on dynamic capabilities theory and resource-base view (Al-Khatib, 2022 ; Bhatti et al., 2022 ; Ciampi et al., 2021 ; Mikalef et al., 2019 ; Su et al., 2022 ), and using an organizational learning perspective to explore the impact of knowledge exploration and knowledge exploitation on social innovation is in dearth. There has been evidence for the importance of knowledge ambidexterity for innovation research (Li et al., 2018 ), and one of the key reasons for the slow growth of new social enterprises is the inefficiency of an effective knowledge management process (Maalaoui et al., 2020 ), yet the evidence on the impact of knowledge exploration and knowledge exploitation on social innovation is not sufficient (Maalaoui et al., 2020 ). This study explicitly explores how knowledge exploration and knowledge exploitation contribute to social innovation and how they mediate the relationship between big data analytics capabilities and social innovation by identifying the path from big data analytics capabilities to social innovation, which thus bridges the gap in existing research, and also provides a new view, on the impact of big data analytics capabilities on organizational development.

Furthermore, our study also explores the impact of different configurations of knowledge exploration and knowledge exploitation on social innovation from the perspective of capability complementarity. Previous studies have focused on the isolated impact of knowledge ambidexterity on innovation (Benitez et al., 2018 ). However, knowledge exploration and knowledge exploitation do not operate independently in most cases, and their complex configuration can either reinforce or counteract each other’s impact (Arias-Pérez et al., 2021 ; Dezi et al., 2021 ). The joint impact of appropriate configurations of knowledge exploration and knowledge exploitation has been rarely discussed in previous studies. This study fills this research gap by empirically examining how knowledge exploration and knowledge exploitation interact with each other to influence social innovation and demonstrates that proper synergies between knowledge exploration and knowledge exploitation contributes more to social innovation.

Managerial implications

Our study also has important implications in managerial practices. First, our study shows that big data analytics capabilities including big data analytics management capabilities, big data analytics technology capabilities, and big data analytics personnel capabilities all positively affect social innovation. Following these findings and considering the increased concerns with social issues in the global economy, organizations can develop stronger big data analytics capabilities to promote social innovation more effectively. On the one hand, organizations are encouraged to build a big data-driven culture within the organization and cultivate valuable big data analytics capabilities at the managerial levels throughout the organization for better big data analytics management capabilities. On the other hand, organizations can actively develop big data analytics technology capabilities by investing in big data technologies to accelerate advancement of big data analytics technologies and thus enhance their ability to conduct social innovation. In addition, organizations should recruit and train big data analytics staff to develop big data analytics personnel capabilities so as to improve their ability to use big data analytics to solve social issues and promote social innovation.

Second, organizations should focus on knowledge management development in their efforts to booster social innovation. Our study shows that knowledge exploration and knowledge exploitation play an important role in relating the influence of big data analytics capabilities to social innovation, which points to an important implication: developing stronger knowledge management capabilities to facilitate social innovation. This can be done by putting more efforts to explore new ideas and information from outside the organizations and to exploit internal knowledge stocks to improve efficiency and quality, both of which can facilitate the process of social innovation.

Third, in addition to realizing the important role of knowledge management in promoting social invocation in organizations and thus investing more in knowledge management, managerial practitioners should also focus on striking a balance of knowledge exploration and knowledge exploitation. The response surface analysis shows that it is clear that organizations should not only encourage R&D staff to strengthen the interactions with external knowledge networks and cooperate with external partners such as universities, governments, and customers to acquire information and knowledge to enrich their own knowledge base, but also they should effectively exploit internal knowledge to combine with new knowledge for innovation, transforming knowledge into social innovation, a joint effect on innovations to complex social issues.

More importantly, managers should be cautious with the trap of knowledge exploration and exploitation mismatch and its impact on social innovation. Overly relying on or ignoring either kind of knowledge ambidexterity is detrimental to social innovation. It is crucial that organizations maintain a balanced position in their knowledge management strategies: a match between knowledge exploration and knowledge exploitation is much more important. When an organization is unable to pursue and maintain knowledge exploration and knowledge exploitation at a balanced level, the priority should be given to knowledge exploitation over knowledge exploration. This is because social innovation is relatively high when knowledge exploitation is greater than knowledge exploration (Shanock et al., 2010 ).

Limitations and future research

Although our study has the potential to make important contributions to the literature on big data analytics capabilities and social innovation and also to managerial practices for better organizational development, it is important to understand the limitations in generalizing the findings. First, the data gathered are solely reflective of Chinese scenario and cannot be generalized without careful considerations because this study is exclusively based on Chinese companies with big data analytics capabilities and social innovation. A cross-country analysis should be conducted in the future in order to determine whether the current results are applicable to other countries. Second, this study examined the mediating role of knowledge exploration and knowledge exploitation between big data analytics capabilities and social innovation. However, there are still other variables that could affect the process, and future studies can investigate other variables such as strategic orientation for their mediating effect between big data analytics capabilities and social innovation. Finally, we used questionnaires to collect data, but the questionnaire data contained some subjective factors. Future studies could analyze objective data from enterprise reports to improve data objectivity and external validity.

Data availability

The data are available from the corresponding author on reasonable request.

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This work are supported by the Beijing Social Science Foundation (21DTR052) and Innovation Centre for Digital Business and Capital Development of Beijing Technology and Business University (SZSK202213).

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Wang, N., Chen, B., Wang, L. et al. Big data analytics capability and social innovation: the mediating role of knowledge exploration and exploitation. Humanit Soc Sci Commun 11 , 864 (2024). https://doi.org/10.1057/s41599-024-03288-8

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Each year, MIT Solve launches four open Challenges that address different aspects of socioeconomic, environmental, and technological issues facing society. This year, these Challenges sought solutions to four categories: Coastal Communities , Frontlines of Health , Teachers and Educators , and Work of the Future . More than 1,100 solutions were submitted in July 2018, and after filtering them to 15 per Challenge, finalists were invited to pitch their solutions to a panel of judges at Solve Challenge Finals in New York City on September 23.

Hamilton Insurance Group is the first insurance company to join MIT Solve, and I was proud to be one of the judges for the Coastal Communities Challenge. As a re/insurance company focused on applying technology and data science to our underwriting, Hamilton is passionate about finding new solutions that address the protection gap, mitigate or reduce risk to people and property, and build resilience.

Coastal communities represent about 30 percent of the global population and are some of the most vulnerable segments of society. The Challenge set by MIT Solve was this: How can coastal communities mitigate and adapt to climate change while developing and prospering?

MIT Solve invited social entrepreneurs to propose solutions to this Challenge that address climate change issues such as sea level rise, tropical storms and flooding, and ocean warming and acidification.

“Solver teams,” as these entrepreneurs are called, were asked to address sustainable economic activity and infrastructure approaches that could increase resilience, ecosystem regeneration, and technology concepts to enable data-based decision-making.

A range of ideas were presented to us, and we were tasked with judging them against five criteria: alignment to the Challenge, the potential to impact lives, the scalability of the solution, the feasibility of the solution—being self-sustaining, and the innovative nature of the solution.

Within the re/insurance industry, we are challenged with a variety of similar issues. In 2017, three major hurricanes—Harvey, Irma, and Maria—made landfall in the United States, resulting in a massive impact on the coastal communities of Houston, the fourth most populous US city; much of Florida; and the island of Puerto Rico, which was devastated by Hurricane Maria.

Events like these have a profound and long-lasting impact on the people living in these communities, and the recovery can be challenging. The economic losses from these three events are estimated by Swiss Re’s sigma service at US $217 billion. Just $92 billion of this amount represents insured losses, a dramatic example of the extent of the protection gap.

The good news is that the reinsurance industry ensured that insurance companies in these communities were rapidly provided with the additional capital required to help respond and rebuild.

So, the big question for people who spend their career managing risk is: how can we close this gap and reduce the loss of life, destruction of personal property, and loss of income that accompany catastrophes of this nature?

There are several ways we can do this:

• Increase the amount of the economic impact that is insured.
• Mitigate the risk through improved infrastructure and increased resilience.
• Reduce the severity of the events where there’s an established link to anthropogenic climate change or environmental impact by addressing the human-induced component.
• Improve the immediate post-disaster response.
• Reduce the need for people to live in the most vulnerable regions.

MIT Solve’s finalists in the Coastal Communities Challenge provided solutions that addressed one of these five action points while also providing improved quality of life for coastal communities in the absence of disasters.

Of the nine solutions selected as Solver teams, two have a clear post-disaster response application through improved real-time monitoring and information services; two provided improved and more resilient infrastructure that functions off-grid; two provided solutions to improve natural coastal defenses through environmental regeneration; and the remaining three provided an approach to combat invasive species as well as a natural method for pollution clean-up; a technological method for monitoring shipping emissions; and a novel method for farming rice in a less water-intensive fashion.

These solutions all have significant potential to improve the adaptability of coastal communities all over the world, with solutions selected being implemented across three continents.

Over the next few weeks, I will explore the nine selected solutions and provide insights into the socioeconomic and environmental impact along with the potential to close the protection gap these solutions hold. Stay tuned.

Allison Archambault pitches Earthspark Participant Power during the Coastal Communities session at Solve Challenge Finals, September 23, 2018. (Photo: Adam Schultz / MIT Solve)

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Technology is all about solving big thorny problems. Yet one of the hardest things about solving hard problems is knowing where to focus our efforts. There are so many urgent issues facing the world. Where should we even begin? So we asked dozens of people to identify what problem at the intersection of technology and society that they think we should focus more of our energy on. We queried scientists, journalists, politicians, entrepreneurs, activists, and CEOs.

Some broad themes emerged: the climate crisis, global health, creating a just and equitable society, and AI all came up frequently. There were plenty of outliers, too, ranging from regulating social media to fighting corruption.

Reporting: MIT Technology Review Staff Editing: Allison Arieff, Rachel Courtland, Mat Honan, Amy Nordrum Copy editing: Linda Lowenthal Fact checking: Matt Mahoney Art direction: Stephanie Arnett

The Perils of Using Technology to Solve Other People's Problems

What will it take to design socio-technical systems that actually work?

I found Shane Snow’s essay on prison reform —  “How Soylent and Oculus Could Fix the Prison System”  — through hate-linking.

Friends of mine hated the piece so much that normally-articulate people were at a loss for words.

A real person thought it would be a good idea to write this and post it on the Internet. pic.twitter.com/rj8viJr1HQ — Susie Cagle (@susie_c) January 30, 2016

With a recommendation like that, how could I pass it up? And after reading it, I tweeted my astonishment to Susie, who told me, “I write comics, but I don’t know how to react to this in a way that’s funny.” I realized that I couldn’t offer an appropriate reaction in 140 characters either. The more I think about Snow’s essay, the more it looks like the outline for a class on the pitfalls of solving social problems with technology, a class I’m now planning on teaching this coming fall.

Using Snow’s essay as a jumping off point, I want to consider a problem that’s been on my mind a great deal since joining the MIT Media Lab five years ago: How do we help smart, well-meaning people address social problems in ways that make the world better, not worse?

In other words, is it possible to get beyond both a naïve belief that the latest technology will solve social problems—and a reaction that rubbishes any attempt to offer novel technical solutions as inappropriate, insensitive, and misguided? Can we find a synthesis in which technologists look at their work critically and work closely with the people they’re trying to help in order to build sociotechnical systems that address hard problems?

Obviously, I think this is possible — if really, really hard — or I wouldn’t be teaching at an engineering school. But before considering how we overcome a naïve faith in technology, let’s examine Snow’s suggestion. It’s a textbook example of a solution that’s technically sophisticated, simple to understand, and dangerously wrong.

Though he may be best known as co-founder of the content-marketing platform “Contently,” Shane Snow describes himself as a “journalist, geek and best-selling author.” That last bit comes from his book Smartcuts: How Hackers, Innovators, and Icons Accelerate Success , which offers insights on how “innovators and icons” can “rethink convention” and break “rules that are not rules.”

That background may help readers understand where Snow is coming from. His blog is filled with plainspoken and often entertaining explanations of complex systems followed by apparently straightforward conclusions — evidently, burning coal and natural gas to generate electricity is a poor idea, so oil companies should be investing in solar energy. Fair enough.

Some of these explorations are more successful than others. In Snow’s essay about prison reform, he identifies violence, and particularly prison rape, as the key problem to be solved, and offers a remedy that he believes will lead to cost savings for taxpayers as well: all prisoners should be incarcerated in solitary confinement, fed only Soylent meal replacement drink through slots in the wall, and all interpersonal interaction and rehabilitative services will be provided in Second Life using the Oculus Rift virtual reality system. Snow’s system eliminates many features of prison life — “cell blocks, prison yards, prison gyms, physical interactions with other prisoners, and so on.” That’s by design, he explains. “Those are all current conventions in prisons, but history is clear: innovation happens when we rethink conventions and apply alternative learning or technology to old problems.”

An early clue that Snow’s rethinking is problematic is that his proposed solution looks a lot like “ administrative segregation ,” a technique used in prisons to separate prisoners who might be violent or disruptive from the general population by keeping them in solitary confinement 23 hours a day. The main problem with administrative segregation or with what’s known as the SHU (the “secure housing unit” in supermax prisons) is that inmates tend to experience serious mental health problems connected to sustained isolation.

“Deprived of normal human interaction, many segregated prisoners reportedly suffer from mental health problems including anxiety, panic, insomnia, paranoia, aggression and depression,” explains the social psychologist Craig Haney in a paper for the journal Crime & Delinquency . Shaka Senghor, a writer and activist who was formerly incarcerated for murder, explains that many inmates in solitary confinement have underlying mental health issues, and the isolation damages even the sound of mind. Solitary confinement, he says, is “one of the most barbaric and inhumane aspects of our society.”

Due to the psychological effects of being held in isolation, the UN Special Rapporteur on Torture has condemned the use of sustained solitary confinement , and called for a ban on solitary confinement for people under 18 years old. Rafael Sperry of Architects/Designers/Planners for Social Responsibility has called for architects to stop designing prisons that support solitary confinement —the argument being that they enable violations of human rights. Snow’s solution may be innovative, but it’s also a large-scale human rights violation.

Snow and supporters might argue that he’s not trying to deprive prisoners of human contact, but wants to give them a new, safer form of contact. But there’s essentially no research on the health effects of sustained exposure to head-mounted virtual reality.

Would prisoners be forced to choose between simulator sickness or isolation? What are the long-term effects on vision of immersive VR displays? Will prisoners experience visual exhaustion through vergence-accommodation , a yet-to-be-solved problem of eye and brain due to problems focusing on objects that are very nearby but appear to be distant? Furthermore, will contact with humans through virtual worlds mitigate the mental problems prisoners face in isolation, or exacerbate them? How do we answer any of these questions ethically, given the restrictions we’ve put on experimenting on prisoners in the wake of Nazi abuse of concentration camp prisoners.

How does an apparently intelligent person end up suggesting a solution that might, at best, constitute unethical medical experiments on prisoners? How does a well-meaning person suggest a remedy that likely constitutes torture?

The day I read Snow’s essay, I happened to be leading a workshop on social change during the Yale Civic Leadership conference . Some of the students I worked with were part of the movement to rename Yale’s Calhoun College, and all were smart, thoughtful, creative, and openminded .

The workshop I led encourages thinkers to consider different ways they might make social change, not just through electing good leaders and passing just laws. Our lab at MIT examines the idea that changemakers can use different levers of change, including social norms, market forces, and new technologies to influence society , and the workshop I led asks students to propose novel solutions to long-standing problems featuring one of these levers of change. With Snow’s essay in mind, I asked the students to take on the challenge of prison reform.

Oddly, none of their solutions involved virtual reality isolation cells. In fact, most of the solutions they proposed had nothing to do with prisons themselves. Instead, their solutions focused on over-policing of black neighborhoods, America’s aggressive prosecutorial culture that encourages those arrested to plead guilty, legalization of some or all drugs, reform of sentencing guidelines for drug crimes, reforming parole and probation to reduce re-incarceration for technical offenses, and building robust re-entry programs to help ex-cons find support, housing, and gainful employment.

In other words, when Snow focuses on making prison safer and cheaper, he’s working on the wrong problem.

Yes, prisons in the United State could be safer and cheaper. But the larger problem is that the U.S. incarcerates more people than any other nation on Earth. With five percent of the world’s population, we are responsible for 25 percent of the world’s prisoners.

Snow may see his ideas as radical and transformative, but they’re fundamentally conservative — he tinkers with the conditions of confinement without questioning whether incarceration is how our society should solve problems of crime and addiction. As a result, his solutions can only address a facet of the problem, not the deep structural issues that lead to the problem in the first place.

Many hard problems require you to step back and consider whether you’re solving the right problem. If your solution only mitigates the symptoms of a deeper problem, you may be calcifying that problem and making it harder to change. Cheaper, safer prisons make it easier to incarcerate more Americans. They also avoid addressing fundamental problems of addiction, joblessness, mental illness, and structural racism.

Some of my hate-linking friends began their eye-rolling about Snow’s article with the title, which references two of Silicon Valley’s most hyped technologies. With the current focus on the U.S. as an “innovation economy,” it’s common to read essays predicting the end of a major social problem due to a technical innovation. Bitcoin will end poverty in the developing world by enabling inexpensive money transfers . Wikipedia and One Laptop Per Child will educate the world’s poor without need for teachers or schools. Self driving cars will obviate public transport and reshape American cities.

The writer Evgeny Morozov has offered a sharp and helpful critique to this mode of thinking, which he calls “solutionism.” Solutionism demands that we focus on problems that have “nice and clean technological solution at our disposal.” In his book, To Save Everything, Click Here , Morozov savages ideas like Snow’s, regardless of whether they are meant as thought experiments or serious policy proposals. (Indeed, one worry I have in writing this essay is taking Snow’s ideas too seriously, as Morozov does with many of the ideas he lambastes in his book.)

The problem with the solutionist critique, though, is that it tends to remove technological innovation from the problem-solver’s toolkit. In fact, technological development is often a key component in solving complex social and political problems, and new technologies can sometimes open a previously intractable problem. The rise of inexpensive solar panels may be an opportunity to move nations away from a dependency on fossil fuels and begin lowering atmospheric levels of carbon dioxide, much as developments in natural gas extraction and transport technologies have lessened the use of dirtier fuels like coal.

But it’s rare that technology provides a robust solution to a social problem by itself. Successful technological approaches to solving social problems usually require changes in laws and norms, as well as market incentives to make change at scale.

I installed solar panels on the roof of my house last fall . Rapid advances in panel technology made this a routine investment instead of a luxury, and the existence of competitive solar installers in our area meant that market pressures kept costs low. But the panels were ultimately affordable because federal and state legislation offered tax rebates for their purchase, and because Massachusetts state law rewards me with solar credits for each megawatt I produce—which I can sell to utilities through an online marketplace because energy companies are legally mandated to produce a percentage of their total power output via solar generation. And while there are powerful technological, economic, and legal forces pushing us toward solar energy, the most powerful driver may be the social, normative pressure of seeing our neighbors install solar panels—leaving us feeling like we weren’t doing our part.

My Yale students who tried to use technology as their primary lever for reforming U.S. prisons had a difficult time. One team offered the idea of an online social network that would help recently released prisoners connect with other ex-offenders to find support, advice, and job opportunities in the outside world. Another looked at the success of Bard College’s remarkable program to help inmates earn bachelor’s degrees , and wondered whether online learning technologies could allow similar efforts to reach thousands more prisoners. But many of the other promising ideas that arose in our workshops had a technological component — given the ubiquity of mobile phones, why can’t ex-offenders have their primary contact with their parole officers via mobile phones? And given the rise of big-data techniques used for “smart policing,” can we better review patterns of policing—including identifying and eliminating cases where officers are over-focusing on some communities?

The temptation of technology is that it promises fast and neat solutions to social problems. It usually fails to deliver. The problem with Morozov’s critique, though, is that technological solutions, combined with other paths to change, can sometimes turn intractable problems into solvable ones. The key is to understand technology’s role as a lever of change in conjunction with complementary levers.

Shane Snow introduces his essay on prison reform not with statistics about the ineffectiveness of incarceration in reducing crime, but with his fear of being sent to prison. Specifically, he fears prison rape, a serious problem which he radically overestimates: “My fear of prison also stems from the fact that some 21 percent of U.S. prison inmates get raped or coerced into giving sexual favors to terrifying dudes named Igor.” Snow is religious about footnoting his essays, but not as good at reading the sources he cites — the report he uses to justify his fear of “Igor” (nice job avoiding accusations of overt racism there, Shane) indicates that 2.91 of 1,000 incarcerated persons experienced sexual violence, or 0.291 percent, not 21 percent.

Perhaps for Snow, isolation for years at a time, living vicariously through a VR headset while sipping an oat flour smoothie would be preferable to time in the prison yard, mess hall, workshop, or classroom. But there’s no indication that Snow has talked to any current or ex-offenders about their time in prison, or about the ways in which encounters with other prisoners led them to faith, to mentorship, or to personal transformation.

The people Shane imagines are so scary, so other, that he can’t imagine interacting with them, learning from them, or anything but being violently assaulted by them. No wonder he doesn’t bother to ask what aspects of prison life are most and least livable, and which would benefit most from transformation.

Much of my work focuses on how technologies spread across national, religious and cultural borders, and how they are transformed by that spread. Cellphone networks believed that pre-paid scratch cards were an efficient way to sell phone minutes at low cost — until Ugandans started using the scratch off codes to send money via text message in a system called Sente, inventing practical mobile money in the process. Facebook believes its service is best used by real individuals using their real names, and goes to great lengths to remove accounts it believes to be fictional. But when Facebook comes to a country like Myanmar, where it is seen as a news service, not a social networking service, phone shops specializing in setting up accounts using fake names and phone numbers render Facebook’s preferences null and void.

Smart technologists and designers have learned that their preferences are seldom their users’ preferences, and companies like Intel now employ brilliant ethnographers to discover how tools are used by actual users in their homes and offices. Understanding the wants and needs of users is important when you’re designing technologies for people much like yourself, but it’s utterly critical when designing for people with different backgrounds, experiences, wants, and needs. Given that Snow’s understanding of prison life seems to come solely from binge-watching Oz , it’s virtually guaranteed that his proposed solution will fail in unanticipated ways when used by real people.

Of the many wise things my Yale students said during our workshop was a student who wondered if he should be participating at all. “I don’t know anything about prisons, I don’t have family in prison. I don’t know if I understand these problems well enough to solve them, and I don’t know if these problems are mine to solve.”

Talking about the workshop with my friend and colleague Chelsea Barabas , she asked the wonderfully deep question, “Is it ever okay to solve another person’s problem?”

On its surface, the question looks easy to answer. We can’t ask infants to solve problems of infant mortality, and by extension, it seems unwise to let kindergarten students design educational policy or demand that the severely disabled design their own assistive technologies.

But the argument is more complicated when you consider it more closely. It’s difficult if not impossible to design a great assistive technology without working closely, iteratively, and cooperatively with the person who will wear or use it. My colleague Hugh Herr designs cutting-edge prostheses for U.S. veterans who’ve lost legs, and the centerpiece of his lab is a treadmill where amputees test his limbs, giving him and his students feedback about what works, what doesn’t, and what needs to change. Without the active collaboration with the people he’s trying to help, he’s unable to make technological advances.

Disability rights activists have demanded “nothing about us without us,” a slogan that demands that policies should not be developed without the participation of those intended to benefit from those policies.

Design philosophies like participatory design and codesign bring this concept to the world of technology, demanding that technologies designed for a group of people be designed and built, in part, by those people. Codesign challenges many of the assumptions of engineering, requiring people who are used to working in isolation to build broad teams and to understand that those most qualified to offer a technical solution may be least qualified to identify a need or articulate a design problem. This method is hard and frustrating, but it’s also one of the best ways to ensure that you’re solving the right problem, rather than imposing your preferred solution on a situation.

On the other pole from codesign is an approach to engineering we might understand as “Make things better by making better things.” This school of thought argues that while mobile phones were designed for rich westerners, not for users in developing nations, they’ve become one of the transformative technologies for the developing world. Frustratingly, this argument is valid, too. Many of the technologies we benefit from weren’t designed for their ultimate beneficiaries, but were simply designed well and adopted widely. Shane Snow’s proposal is built in part on this perspective — Soylent was designed for geeks who wanted to skip meals, not for prisoners in solitary confinement, but perhaps it might be preferable to Nutraloaf or other horrors of the prison kitchen.

I’m not sure how we resolve the dichotomy of “with us” versus “better things.” I’d note that every engineer I’ve ever met believes what she’s building is a better thing. As a result, strategies that depend on finding the optimum solutions often rely on choice-rich markets where users can gravitate towards the best solution. In other words, they don’t work very well in an environment like prison, where prisoners are unlikely to be given a choice between Snow’s isolation cells and the prison as it currently stands, and are even less likely to participate in designing a better prison.

Am I advocating codesign of prisons with the currently incarcerated? Hell yeah, I am. And with ex-offenders, corrections officers, families of prisoners, as well as the experts who design these facilities today. They’re likely to do a better job than smart Yale students, or technology commentators.

It is unlikely that anyone is going to invite Shane Snow to redesign a major prison any time soon, so spending more than 3,000 words urging you to reject his solution may be a waste of your time and mine. But the mistakes Snow makes are those that engineers make all the time when they turn their energy and creativity to solving pressing and persistent social problems. Looking closely at how Snow’s solutions fall short offers some hope for building better, fairer, and saner solutions.

The challenge, unfortunately, is not in offering a critique of how solutions go wrong. Excellent versions of that critique exist, from Morozov’s war on solutionism, to Courtney Martin’s brilliant “The Reductive Seduction of Other People’s Problems.” If it’s easy to design inappropriate solutions about problems you don’t fully understand, it’s not much harder to criticize the inadequacy of those solutions.

What’s hard is synthesis — learning to use technology as part of well-designed sociotechnical solutions. These solutions sometimes require profound advances in technology. But they virtually always require people to build complex, multifunctional teams that work with and learn from the people the technology is supposed to benefit.

Three students at the MIT Media Lab taught a course last semester called “Unpacking Impact: Reflecting as We Make.” They point out that the Media Lab prides itself on teaching students how to make anything, and how to turn what you make into a business, but rarely teaches reflection about what we make and what it might mean for society as a whole. My experience with teaching this reflective process to engineers is that it’s both important and potentially paralyzing, that once we understand the incompleteness of technology as a path for solving problems and the ways technological solutions relate to social, market, and legal forces, it can be hard to build anything at all.

I’m going to teach a new course this fall, tentatively titled “Technology and Social Change.” It’s going to include an examination of the four levers of social change Larry Lessig suggests in Code, and which I’ve been exploring as possible paths to civic engagement. The course will include deep methodological dives into codesign, and will examine using anthropology as tool for understanding user needs. It will look at unintended consequences, cases where technology’s best intentions fail, and cases where careful exploration and preparation led to technosocial systems that make users and communities more powerful than they were before.

I’m “calling my shot” here for two reasons. One, by announcing it publicly, I’m less likely to back out of it, and given how hard these problems are, backing out is a real possibility. And two, if you’ve read this far in this post, you’ve likely thought about this issue and have suggestions for what we should read and what exercises we should try in the course of the class — I hope you might be kind enough to share those with me .

In the end, I’m grateful for Shane Snow’s surreal, Black Mirror vision of the future prison both because it’s a helpful jumping-off point for understanding how hard it is to make change well by using technology, and because the U.S. prison system is a broken and dysfunctional system in need of change. But we need to find ways to disrupt better, to challenge knowledgeably, to bring the people they hope to benefit into the process. If you can, please help me figure out how we teach these ideas to the smart, creative people I work with—people who want to change the world, and are afraid of breaking it in the process.

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Social entrepreneurs are inventing new technologies to solve the world’s problems – disease, malnutrition, pollution, and illiteracy – to name just a few. But it takes more than a fancy new gadget to make life better. That’s why the organizations profiled here are working with businesses, NGOs, and governments to get their inventions into the hands of those who need them most.

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The role of digital social innovations to address SDGs: A systematic review

• Published: 23 February 2023
• Volume 26 , pages 5709–5734, ( 2024 )

Cite this article

• Marcelo Dionisio 1 ,
• Sylvio Jorge de Souza Junior 1 ,
• Fábio Paula 1 &
• Paulo César Pellanda 2  

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The impact of the COVID-19 pandemic has increased the search for solutions to social problems associated with the Sustainable Development Goals (SDGs). Main actors are turning to Digital Social Innovations (DSIs), defined as collaborative innovations where enterprises, users and communities collaborate using digital technologies to promote solutions at scale and speed, connecting innovation, the social world and digital ecosystems to reach the 2030 Agenda. This study aims to identify how digital transformations and social innovations solve social problems and address SDGs. We conducted a systematic review based on a sample of 45 peer-reviewed articles published from 2010 to 2022, combining a bibliometric study and a content analysis focusing on opportunities and threats impacting these fields. We observed the spread and increasing use of technologies associated with all 17 SDGs, specially blockchain, IoT, artificial intelligence, and autonomous robots that are increasing their role and presence exponentially, completely changing the current way of doing things, offering a dramatic evolution in many different segments, such as health care, smart cities, agriculture, and the combat against poverty and inequalities. We identified many threats concerning ethics, especially with the increased use of public data, and concerns about the impacts on the labor force and the possible instability and impact it may cause in low skill/low pay jobs. We expect that our findings advance the concept of digital social innovations and the benefits of its adoption to promote social advancements.

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Research Methodology: An Introduction

A bibliometric analysis of sustainable development goals (SDGs): a review of progress, challenges, and opportunities

Key factors influencing farmers’ adoption of sustainable innovations: a systematic literature review and research agenda

Avoid common mistakes on your manuscript.

1 Introduction

Social innovation (SI) has been among the most discussed topics in innovation and business in the last few years. It has become central to policy debates around the world, as it aims to engage civil society, businesses, and governments to find new ways to address persisting social problems, driven by such trends as criticism of existing business models, narrow economic outlooks, extensive declines in public spending, and the needs of developing and emerging economies, moving the idea of innovation from cutting-edge technology to the solution of social problems (Ayob et al., 2016 ; Dionisio, 2021 ; Dionisio & Vargas, 2019 ; van der Have & Rubalcaba, 2016 ). The concept of social innovation emerged after the turn of the century, and it is defined as ideas that meet social goals based on the reconfiguration of social practices and the promotion of social development, connected to methods, processes, and outcomes that offer innovative ways to address social needs (Ayob et al., 2016 ; Mirvis et al., 2016 ; Phillips et al., 2015 ). The recent COVID-19 pandemic has accelerated a worldwide debate about searching for solutions to complex problems and challenges associated with the UN Sustainable Development Goals (SDGs) that has gained momentum. With the help of digital transformation (DT), a key concept to promote sustainable development, different actors must raise awareness and advocate for more engagement from governments, businesses, and society to solve social problems with better governance and inclusive innovative processes (Bria et al., 2015 ; Sempere & Moreno, 2021 ; Soni et al., 2021 ). This attempt is recognized as vital for the economic and social development of organizations, regions, and countries (Carayannis & Morawska-Jancelewicz, 2022 ; Schwab & Malleret, 2020 ). The connection between social innovations and digital transformations is called Digital Social Innovation (DSI), and is defined as “a type of social and collaborative innovation in which innovators, users, and communities collaborate using digital technologies to co-create knowledge and solutions for a wide range of social needs and at a scale and speed that was unimaginable before the rise of the Internet” (Bria et al., 2015 , p. 9). According to Carayannis and Morawska-Jancelewicz ( 2022 ), DSI highlights the connection of three elements: the innovation process; the social world; and the digital ecosystem, and literature indicates that there is no turning back on the connection between these concepts, arguing that they should be considered together to further develop the concept of DSI. Various authors state that sustainability is one of the drivers of digital transformation and should be considered at the very core of its strategies, nevertheless they also claim that this relationship is at an early stage, with few studies addressing the interconnection of these two concepts, not providing a complete overview of the opportunities and challenges of DSIs, to comprehend the policy implications and social effects of technological change, and their potential role in addressing SDGs (Carayannis & Morawska-Jancelewicz, 2022 ; Mazzucato, 2018 ; Moulaert et al., 2017 ; Piccarozzi et al., 2022 ).

As this concept will play a vital role in helping actors address SDGs and reach the 2030 agenda, this study has the objective to further explore the concept of digital social innovation. Thus, we performed a systematic literature review to solve our research problem by offering an understanding of how the field evolved, the points of consensus and divergences in the literature, and analyzing the discussion of this theme in the field (Hemingway & Brereton, 2009 ; Purssell & McCrae, 2020 ; Torgerson, 2003 ). This study proposes the following research questions:

RQ 1.: How have the studies of digital social innovations evolved and been discussed in the literature through time?

RQ 2.: What are the main opportunities and threats identified in this context?

RQ 3.: Does the concept of digital social innovation connect to SDGs? How?

Our study combines a bibliometric study and a content analysis, and it is based on a sample of 45 peer-reviewed articles published from 2010 to 2022 (Phillips et al., 2015 ; Tranfield et al., 2003 ). Results of the bibliometric analysis allowed us to identify the evolution of a research based on the number of articles produced yearly, in which countries this research is concentrated, and to produce a map of the connections between social innovation and all the digital transformation terms, allowing us to identify the connection and relevance of DSIs with SDGs. Then, the content analysis indicated three paths to explore: (1) the predominance of each technology in the literature; (2) which areas DSIs are more present, and how they address SDGs; and (3) what are the main opportunities and threats DSIs face. We expect to offer a theoretical contribution by advancing the theory of digital social innovation (DSI) and offer a practical contribution by supporting practitioners to adopt a more contingent approach to DSI.

This article is structured as follows. We first provide a brief theoretical background of the most relevant themes of our study. Then we describe the method used to conduct the systematic literature review. Following, we present the results of the bibliometric analysis and content analysis and finalize with concluding remarks, limitations, and suggestions for future research.

2 Literature review

The concept of social innovation emerged after the turn of the century and has seen an astonishing rise over the last years, both in theory and practice, where initial definitions focused on new solutions and innovations that were not only social in their ends but aimed to address societal challenges based on new forms of collaboration (Ayob et al., 2016 ; Mirvis et al., 2016 ; Osburg & Schmidpeter, 2013 ; Peredo & McLean, 2006 ). The idea has evolved to become an effective way of dealing with societal challenges, manifested in policy discourses across the USA where, in 2009, President Barack Obama opened the “Office of Social Innovation and Civic Participation” (Eichler & Schwarz, 2019 ), and in the EU, where former president Barroso stated that “if encouraged and valued, social innovation can bring immediate solutions to the pressing social issues citizens are confronted with” (Haxeltine et al., 2017 , p. 2). In emerging economies, evidence from several studies indicate that formal institutions may be absent, weak, or fail to accomplish their expected role (e.g. Mair & Marti, 2009 ; Rao-Nicholson et al., 2017 ). In this case, local players are prone to find formal and informal ways to fill the gap exposed by institutional voids and play a stronger part in contributing to the delivery of social innovations to meet social objectives, reconfigure social practices, and promote social development (Puffer et al., 2016 ; Saka-Helmhout et al., 2021 ; Zhao et al., 2014 ). Nevertheless, recent studies observed that in the case of emerging economies, social innovations have the capacity to act as a catalyst to reduce and overcome institutional voids, which become more relevant when we consider that emerging economies are assuming an increasingly prominent position in the global market, providing a new and different context for the development of social innovations (do Adro & Fernandes, 2020 ; Phillips et al., 2015 ; Rao-Nicholson et al., 2017 ).

At the same time, the direct impact of technology has long been a topic of the social innovation discourse as it represents an extensive range of activities, sectors, and a broad spectrum of scientific disciplines. These are also developed and proposed by several types of organizations, where advanced information technology (IT) tools and other innovative technologies are increasingly used to implement social innovations. It is also established that social change may stimulate the potential for digital innovation, as we know that social problems may create new business opportunities, which confirms the need to understand and research digital innovation and its connection to society, as social innovations and digital transformations are considered twin concepts that promote sustainable development. New digital technologies allow organizations to collect, assess, and analyze data, and then communicate those results to a broader public, raising awareness and advocating more engagement from society in solving complex problems, leading to better governance and more inclusive innovation processes (Cajaiba-Santana, 2014 ; Carayannis & Morawska-Jancelewicz, 2022 ; Faludi, 2020 ; Smith, 2017 ).

The emergence of these new technologies is promoting what is called the Fourth Industrial Revolution, or Industry 4.0, a term coined at the Hannover Fair in 2011 to describe how these changes will revolutionize the organization of global value chains through broad-based innovation that is diffusing much faster and more widely than in previous ones (Piccarozzi et al., 2022 ; Schwab, 2017 ). According to Schwab (2016), Industry 4.0 is unique as it has dramatic differences from the other industrial revolutions due to the scale, scope, complexity, and transformative power, especially concerning the change in existing relationships in the different processes, making it unique compared to all other revolutions (Mhlanga, 2021 ). Industry 4.0 is characterized by highly developed automation and digitalization processes, the implementation of which should be interdisciplinary within different key areas, described as the nine pillars of I4.0. This concept claims that isolated, optimized cells will turn into fully integrated, automated, and optimized production flows that will promote greater efficiencies and that will change the traditional production relationships among suppliers, producers, customers, and humans, with machines (Alcácer & Cruz-Machado, 2019 ; Russmann et al., 2015 ). These nine pillars are comprised of: ‘Big Data’, ‘Autonomous Robots’, ‘System Integration’, ‘IoT’, ‘Cybersecurity’, ‘Cloud Computing’, ‘Additive Manufacturing’, ‘Simulation’, and ‘Augmented Reality’ and to actively adapt their full transformation power, producers and suppliers must take decisive action to embrace them, addressing the needs in terms of infrastructure, collaboration, innovation, and education that will enable the successful improvement in the many designated areas they allow (Russmann et al., 2015 ).

In this collaborative scenario, diverse DT interventions may connect with SDGs, the United Nations’ 17 Sustainable Development Goals that present a concept for the sustainable development vision for 2030, focusing on poverty, inequality, decent work, gender equality, and ecosystem conservation, and the necessity for all societal actors to jointly tackle them see Appendix 1 ) The SDGs are well known and accepted both academically and politically and can be considered universal and thus applicable to developing and developed countries (Eichler & Schwarz, 2019 ). The SDGs include the private sector, governments, and civil society, designing a global sustainable agenda, explicitly encouraging the adoption of responsible practices, and the partnership of these actors to achieve these goals (Martinuzzi et al., 2017 ). As an example, DSIs are solving problems related to safety for drivers and pedestrians, considering that traffic accidents are one of the greatest hazards, especially in developing countries, or through the development of poverty maps or digital financial inclusion to influence poverty reduction (Mhlanga, 2021 ).

3 Research method

We conducted a systematic literature review to analyze the connection between digital transformations and social innovations. A systematic literature review is a scientific method that offers a transparent, reproducible, and iterative review process, using a comprehensive search and analysis framework that combines cross-referencing between journals and researchers and extensive searches of research databases. It applies inclusion and exclusion criteria that could provide methodologically rigorous and theoretically sound research, providing a reliable basis on which to formulate decisions and take actions, in this study, based on a sample of 45 peer-reviewed articles published from 2010 to 2022 (Phillips et al., 2015 ; Tranfield et al., 2003 ).

The systematic literature review conducted in this study includes a bibliometric analysis to help us answer our first research question that searches to understand the evolution of the literature of digital social innovations, and a content analysis that identifies literature trends, the most frequently discussed topics and fields, and gaps that may exist within the literature, allowing us to answer our research question two, which asks about the main opportunities and threats identified in this context, and three, which questions how the concept of digital social innovation connects to SDGs. Bibliometric studies are becoming more relevant considering the growing number of scientific publications and the availability of tools to analyze huge amounts of data (Gomes et al., 2018 ). Our systematic literature review is based on the Scopus database to assess peer-reviewed documents ( www.scopus.com ). We use the keyword “social innovation” in combination with the “nine pillars” of the technological advancement toward Industry 4.0 (de Almeida et al., 2020 ; Russmann et al., 2015 ), which covers the areas of: ‘Big Data’, ‘Autonomous Robots’, ‘System Integration’, ‘IoT’, ‘Cybersecurity’,’Cloud Computing’, ‘Additive Manufacturing’, ‘Simulation’, and ‘Augmented Reality’. We found the need to include additional references, namely ‘artificial intelligence’, ‘machine learning’, ‘digital transformation’, ‘blockchain’, and ‘predictive analysis’, which are regular terms associated, and considered core technologies to Industry 4.0, to make our assessment more complete, guaranteeing that it covered most of the relevant articles on the digital transformation field (see Table 1 ). In this way, we can identify the connection between digital transformation and social innovation to solve social problems (Kim, 2021 ).

The string of our search was ( TITLE-ABS-KEY ( "social innovation") AND TITLE-ABS-KEY ( "big data") OR TITLE-ABS-KEY ( "autonomous robot") OR TITLE-ABS-KEY ( "System Integration") OR TITLE-ABS-KEY ( "IoT") OR TITLE-ABS-KEY ( "Cybersecurity") OR TITLE-ABS-KEY ( "Cloud Computing") OR TITLE-ABS-KEY ( "Additive Manufacturing") OR TITLE-ABS-KEY ( "Augmented Reality") OR TITLE-ABS-KEY ( "Simulation") OR TITLE-ABS-KEY ( "Artificial Intelligence") OR TITLE-ABS-KEY ( "Machine Learning") OR TITLE-ABS-KEY ( "Digital Transformation") OR TITLE-ABS-KEY ( "Blockchain") OR TITLE-ABS-KEY ( "Predictive Analysis") OR TITLE-ABS-KEY ( "industry 4.0")). An initial search resulted in 203 studies categorized by Scopus into different research areas. From this initial sample, we refined the results by considering the filters “article” and “review”, which resulted in a sample of 84 articles and reviews. We read all these abstracts and excluded 39 documents that, despite containing topics used in the search, covered subjects that were either out of scope, such as “animal health” or were focused on too specific areas such as “tourism sector” or “Cryptocurrency”, so we ended up with a final list of 45 peer-reviewed articles published from 2010 to 2022 (see Fig.  1 ).

Phases of the systematic review

After reading all articles from our final list, data were analyzed in two stages: 1) a bibliometric study based on the metadata file supplied by Scopus with VOS Viewer; and 2) a content analysis performed with QDA Miner. VOS viewer is a software tool intended to analyze bibliometric networks, create maps based on network data, and visualize and explore these maps (van Eck & Waltman, 2021 ). The VOS viewer software was adopted to identify all papers selected, analyze the connections between digital transformation and social innovations, and establish a link between the selected keywords to evaluate the connection between the different terms, offering a strong visualization of the relationships among social innovation and the various technologies. QDA Miner is a data analysis software designed to assist researchers in managing, coding, and analyzing qualitative data. The content analysis focused on the relationship between DSI and SDGs, and on opportunities and threats that impact these fields. The first step of our content analysis was to use the DT keywords from our systematic literature review as codes to evaluate the frequency with which they appear in our sample literature, thus we searched for the predominant technologies presented in the literature, which areas DSIs are promoting more impact, and what is their relationship with SDGs, to finally analyses the main opportunities and threats DSIs face.

4.1 Bibliometric analysis

This bibliometric analysis aims to map the field of digital social innovations, identify the evolution of a research based on the number of articles produced yearly, in which countries this research is concentrated, and create a map of the connections between social innovation and digital innovation transformation terms. As we cover an emerging topic, our analysis benefits from a comprehensive integration of different concepts emerging from different areas as the 45 selected articles come from 23 journals that cover various fields such as health care, architecture, energy, education, and finance. This finding emphasizes the diverse character of the subject and helps us obtain a deeper insight from the literature.

We observed that 33 articles (73%) have more than one citation. Concerning the journals’ h-index, 10 journals (30%) with 17 articles have an h-index of more than 40, and 7 journals (21%) with 9 articles have an h-index of more than 20 (see Table 2 ). According to Hirsch ( 2005 ), an h-index of 20 is good, 40 is outstanding, and 60 is truly exceptional. Mingers et al. ( 2012 ) claimed that the h-index could be a better citation-based metric for evaluating the quality and contribution of scholarly journals than the impact factor (IF) or the number of cites per paper (CPP). We call the attention for the fact that although our string list covered the main terms concerning digital transformations, our list does not include any journals in Earth Sciences or in the Environment. We believe that this is a result of the procedure involved in the systematic literature review process and the fact that we concentrated our search on social innovations without a specific focus on eco-innovations. Therefore, we assume that this selection of articles is adequate for our literature review.

When we analyze the publications over the years, we observe the novelty of the subject, as articles start in 2010, and continuously evolve with a spike of publications in 2019 moving from three to eight articles, despite the effects of the pandemic, which slightly reduced the number of articles in 2020, and recovered their pace in 2021 and 2022 (Fig.  2 ). Articles published until 2018 are more introductory, focusing on the Internet and ICT, still concentrating on the potential solutions that the combination of social innovation and digital transformation may provide to society. As we get closer to 2018, a move towards innovative technologies and new social applications such as the development of ‘smart’ systems, techno society, and cybercities can be observed. In 2019, we had the most significant production of articles that focus on the impact and benefits of the DSI in diverse areas such as agriculture, human capital, employment, finance, and the development of rural regions. There is a growing concern about the opportunities and threats presented by the transformative concept of Industry 4.0.

Articles by year

We observe the evolution of this trend in articles from 2020 to 2022. They evaluate existing projects, with a growing focus on smart cities, and the development of the concepts of society 5.0 and Industry 5.0, exploring these groundbreaking perspectives and connecting DSI with the United Nations’ SDGs.

The analysis of the origins of the selected articles shows a dominance of Asian countries, with Japan leading the rank followed by South Korea and India, and European countries with Italy, and the UK among the leaders in the number of articles (Fig.  3 ). Australia, the USA, and Canada complete the list with five articles. The dominance of Asia could be explained by the support of companies, especially from Japan in DT research, which along with South Korea accounts for two-thirds of global AI-related patents (Kovacs, 2022 ). European dominance is associated with the origins of the concept of SI that allegedly started in the UK and with the interest of the European Commission that funded numerous studies for the development and growth of DSI in Europe (Bria et al., 2015 ; do Adro & Fernandes, 2020 ; Haxeltine et al., 2016 ).

Articles by country

The keyword network (Fig.  4 ) was used to identify the associations between social innovation and the digital transformation ecosystem’s concept based on the strength of the ties between the keywords, which corresponds to the intensity of their relationship. The analysis observed five clusters: namely, the first and largest cluster is named ‘Social Innovation’ as it concentrates on social innovation and its direct connections with its areas of influence such as education, e-participation, society 4.0, innovation ecosystem, open innovation, and smart cities. The second cluster ‘Artificial Intelligence’ reflects important concepts from the nine pillars of the technological advancement towards Industry 4.0 (Russmann et al., 2015 ) in connection with potential ramifications and applications such as sensor networks and human–machine interface, smart cities, and social design. The third cluster named ‘Social Impact’ and the fourth cluster ‘Industry 4.0’ connect digital transformations such as automation, big data, and mobile technologies with social impact, active citizenship, Environment, social & Governance (ESG), society 4.0, and society 5.0, reinforcing the collaborative characteristic of digital social innovations, and its potential to develop social innovations. The last cluster named ‘Co-creation’ concentrates on processes such as participation, smart systems, socio-digital and technological innovations, establishing the potential results that digital transformation may offer in terms of social innovations, effectively connecting social innovations and digital transformations (AI and machine learning) – clusters one and two. This map supports the concept of an innovation ecosystem that provides an opportunity to modernize existing solutions and adopt new ones, validated through the involvement of multiple stakeholders, deploying, testing, and evaluating solutions under real conditions. The literature claims that this technology has the potential to impact urban city services positively, such as by waste or transportation management, energy savings, health care, and many other activities (Ghazal et al., 2021 ; Gobbi & Spina, 2013 ; Gutiérrez et al., 2016 ).

VOS viewer keyword network

The map also provides a clear view of the multidimensional perspective of the connections between social innovation and DT, which is a compulsory prerequisite to attain sustainable social, environmental, and economic development and improve the living standards of society (Bokhari & Myeong, 2022 ). This map provides a distinct vision that social innovation is the converging concept at the center of the map, connected with other clusters representing DTs and its main concepts, including AI, machine learning, big data, and augmented reality.

In summary, the research pointed to an increase of studies that connect SI and DT in a wide variety of areas such as health care, architecture, energy, and education, connected with SDGs and with many different ramifications among the different technologies, promoting the development of an ecosystem that will be able to implement new and innovative solutions of existing problems.

4.2 Content analysis

In this section, we present the findings of our content analysis, with focus on the relationship between DSI and SDGs, and on the opportunities and threats that impact these fields. The first step of our content analysis was to use the DT keywords from our systematic literature review as codes to evaluate the frequency with which they appear in our sample literature. Our initial analysis indicated three primary themes that connected the three dimensions we aim to explore in our study: 1) the predominance of each technology in the literature; 2) which areas DSIs are the most present, and how they address SDGs; and 3) what are the main opportunities and threats DSIs face (Fig.  5 ).

Content analysis process

In the analysis of the first dimension, we observe that four leading subjects account for 75% of the frequency in the literature, namely blockchain, IoT, AI, and autonomous robots (Fig.  6 ). Another smaller group is represented by big data, machine learning, digital transformation, and Industry 4.0, which accounts for 22%, with the difference considering the remaining terms we used in our literature review.

Predominant technologies

Thus, we connected the other two dimensions – SDGs and DSIs – with each of the most relevant technologies to understand the areas in which they are generating SIs, their relationship with SDGs, and the main opportunities and threats they face.

4.2.1 Blockchain

Blockchain technology is still in an early stage of development and requires improvements in the standardization of developments, security, and regulation. There is a significant disparity between the existing blockchains, so standardization will facilitate their interaction, providing efficiency to these interactions and the development of applications (Reepu, 2019 ). Nevertheless, the European Economic and Social Committee (EESC) highlighted that the characteristics of this technology have an adequate fit in the Social Economy, by stating that some characteristics of these technologies make blockchain a digital infrastructure that could be profitably used to improve the fulfilment of social goals, increasing the capacity to generate a positive social impact and promote social innovation (Sempere & Moreno, 2021 ). The literature claims that this technology’s applications and impact on social good are the ones generating the greatest expectations, as they could pave the way for the achievement of the sustainable development goals (SDGs) of the 2030 Agenda, by putting decentralized tools in the hands of civil society allowing it to meet the demands for action, necessary for its success. Blockchain was selected as one of the core technologies leading the Fourth Industrial Revolution as it has many advantages, such as security, transparency, disintermediation, decentralization, and speed. In addition, some researchers posit that blockchain has infinite potential and widespread influence to drastically improve the paradigm of existing industry innovations and is poised to lead a societal transformation that is super-connected, super-intelligent, and super-converged (Bartoletti et al., 2018 ; Kim, 2021 ; Sempere & Moreno, 2021 ).

There are cases in the literature that demonstrate the uses of blockchain in health, whether for public health management, medical research based on patients’ personal information, or quality assurance in drug manufacturing (SDG # 3). There are also examples of blockchain usage to gather environmental data to help farmers improve yields (SDGs # 2 and 12), to help track carbon footprint (SDG # 13), to store contracts of migrant workers, and to help reduce abuse (SDG # 8). Blockchain technology has revolutionized the reliability of the information in a distributed and decentralized network, which we believe can be a way to build trust in different environments without relying on a third-party entity, which would be even more impactful in emerging economies (Bartoletti et al., 2018 ; Szoniecky & Amri, 2020 ). The main opportunity for the development of this technology is the lack of a legal framework that creates a situation of legal insecurity, which occurred in the preliminary stages of the development of participatory financing or crowdfunding platforms, which is a conditioning factor for their development. The generation of this entire regulatory framework is going to be decisive for the more agile deployment of this technology in an atmosphere of security, generating trust for the different interest groups that arise around each of the proposals that appear, allowing it to fully promote social innovations (Sempere & Moreno, 2021 ). On the other side, skeptics believe that most of the disruptive potential of these technologies is just hype, as no convincing case has been found yet (Bartoletti et al., 2018 ).

4.2.2 Internet of things (IoT)

Internet of Things (IoT) is defined as a system of interconnected computing devices, digital and mechanical machines, objects, animals, or people that can transfer the data over a network without requiring human-to-human or human-to-computer interaction (Patil et al., 2019 ). Like other innovative technologies, IoT has many practical applications; in our research, we found examples in health care (SDG # 3) through the use of sensors in both patients and operating rooms providing physicians with real-time support during procedures (Ghazal et al., 2021 ), smart cities (SDG # 11) with the implementation of IoT devices that provide integrated monitoring of physical spaces improving security and time of response (Hanaoka et al., 2016 ), and food development (SDGs # 2 and 15), with the spreading use of IoT agriculture gadgets that allow farmers to have better control over the process of raising crops, increasing crop productivity and soil health through precision farming (Patil et al., 2019 ).

Vantage Market Research’s recent analysis of IoT in Healthcare found a growing adoption of these technologies and solutions that is estimated to reach USD 190 billion by 2028, up from USD 73.5 billion in 2021, at a compound annual growth rate (CAGR) of 25.9% (Vantage_Market_Research, 2022 ). Another study found that about 27% of health-care organizations are thinking of implementing devices such as smart wearables, portable smart monitors, and other health status-checking smart devices (UnivDatos_Market_Insights, 2021 ). The benefits of IoT in health care include improved disease management, improved patient outcomes, better diagnosis and treatment, improved management of medical records, etc. It is estimated that IoT in health care will save around US$ 300 billion in expenses annually (Ghazal et al., 2021 ; UnivDatos_Market_Insights, 2021 ). Smart cities have the vision to achieve a sustainable urban environment and overcome problems by utilizing technology based on smart mobility, smart environment, smart people, smart living, and smart governance. All aspects of smart city architecture, services, and applications in real-world environments must be integrated to produce a secure and sustainable environment, through the large-scale use of IoT infrastructure, key for the development of this new concept of urban environments, in initiatives that are at the center of achieving SDGs, including energy savings (SDG # 7), gender equality and empowerment for women (SDG # 5), transportation (SDG # 9) and safety (SDG # 16) (Asteria et al., 2020 ; Ghazal et al., 2021 ; Gutiérrez et al., 2016 ). IoT technology is extremely helpful in agriculture processes such as cultivation. In precision farming, IoT can modernize agriculture and initiate exponential growth in the sector, directly addressing SDGs #2 and #15. Precision farming using IoT improves efficiency and productivity through constant identification and maintenance of soil health, check of soil nutrients, and prediction of frost, which improves fertility, and plant growth, and optimizes crop yield (Patil et al., 2019 ).

The main issues surrounding the increasing use of IoT concern the widespread availability of enormous amounts of data and the ethical complications it involves in terms of privacy, data quality, and intellectual property, identified as the three main issues by researchers (Marti et al., 2016 ; Scheibner et al., 2021 ).

4.2.3 Artificial intelligence (AI)

Artificial intelligence (AI) has proven to be beneficial in various industries. As AI has grown in popularity due to the availability of relevant data, computational ability, and enhanced algorithms, it is beginning to live up to its promises of delivering real value (Battisti et al., 2022 ; Bokhari & Myeong, 2022 ; Mhlanga, 2021 ). Artificial intelligence describes the work processes of machines that would require intelligence if performed by humans (Bokhari & Myeong, 2022 ). The term represents the investigation of intelligent problem-solving behavior and the creation of intelligent computer systems, of two kinds: one where the computer is merely an instrument for investigating cognitive processes (the computer simulates intelligence), and another where the processes in the computer are intellectual self-learning processes. Well-known examples of AI are smart factories, driverless cars, delivery drones, or 3D printers, which can produce highly complex things without changes in the production process or human action in any form (Bokhari & Myeong, 2022 ; Wisskirchen et al., 2017 ).

In this sense and usually combined with other technologies such as IoT, AI is becoming a need for daily life and organizational procedures as technology has taken great dives in empowering its advancement. We observed in our analysis that AI is supporting the development of smart cities (SDG #11), improving agriculture (SDG #2 and 15), and health care (SDG # 3). AI contributes to the development of smart cities because they utilize a systematic and organized approach to collect data and apply rational decision-making systems to attain sustainable social, environmental, and economic development by improving living standards. For instance, the South Korean government used AI to combat the COVID-19 epidemic by encouraging proactive information exchange, assisting citizens in understanding the issue, and implementing safety protocols (Bokhari & Myeong, 2022 ). AI is also accelerating changes in the food industry worldwide, moving toward a high-productivity and interconnected manufacturing industry, by using AI to forecast high-impact weather, incorporating technology into food production, storage, and packaging, completely disrupting the food production chain (Bokhari & Myeong, 2022 ; Chapman et al., 2020 ). In the health-care sector, AI can be used in diverse ways. It helps with treatment and operations and supports medical staff diagnostics and prevention. In some cases, operating rooms have all steps of a procedure meticulously recorded, and physicians are given detailed support in deciding on the next steps and even individual incisions to make. The patient is tracked at all times by sensors built into the room and thus optimally cared for (Ghazal et al., 2021 ).

One study found a positive and significant correlation between artificial intelligence and social innovation, which reinforces the power of AI to provide social impact; nevertheless, one main challenge is that artificial intelligence has received minimal attention in the public sector, even though many studies and reports have highlighted the many benefits and potential of AI within numerous government departments (Bokhari & Myeong, 2022 ). There is also a threat concerning a mistaken prejudice regarding job loss and many unknowns about how work will be transformed; nonetheless, history shows that although technology causes varied effects on the job force, demanding developments, and adaptations, it usually creates entirely new jobs and sectors. Therefore the public sector must promote and invest in the development of this technology (Chapman et al., 2020 ).

4.2.4 Autonomous robot

An autonomous robot is a robot that acts without recourse to human control, based on two mechanisms: they must estimate what users are thinking and present their ideas in an easy-to-understand format for users. These mechanisms have a major role in the development of autonomous robots and their integration into human society (Sakai & Nagai, 2022 ). Mhlanga ( 2021 ) posits that using robotics is an essential variable in solving world hunger (SDG # 2), especially with the progress promoted in farming. Robotics is also considered among the technologies that can play a crucial role in improving health (SDG # 3), developing smart cities, especially with the broadened use of autonomous driving and vehicles (SDG # 11), and safety conditions of workplaces (SDG # 8), where autonomous robots could overtake the most cumbersome, repetitive and potentially harmful operations (Colla et al., 2021 ; Zavratnik et al., 2019 ).

In the workplace, autonomous robots would significantly improve workers’ health and safety conditions by eliminating cumbersome operations and reducing exposure to sparks and elevated temperatures, where the robot’s main tasks would be performed, especially the most hazardous and heavy ones. Results show that the robot can handle all the heavyweight procedures and be exposed to high temperatures, decreasing risk by two to four times, compared to current full human procedures (Colla et al., 2021 ; Kohlgrüber et al., 2021 ). In health care, autonomous robots can perform minimally invasive operations with AI support, allowing a tremor-free operation with the highest precision. The literature claims that in the near future, robots will be equipped with image and sensor evaluation techniques that could be further trained and improved, permitting them to carry out sub-steps of an operation, for example, in body regions with many vessels, with a greater degree of autonomy, always under the supervision of experts (Ghazal et al., 2021 ).

Again, the main challenge faced by this technology concerns the wrongful idea that continuous growth in automation, robots, and computers will take the jobs of workers in many industries, with the most worrying concern of the disappearance of low-skill/low-pay jobs, which would impact the more vulnerable, with the possibility of a rise in social tensions. However, Schwab (2016) posits that “we can reasonably assume that demand will increase for skills that enable workers to design, build and work alongside technological systems, or in areas that fill the gaps left by these technological innovations” (p. 46). He argues that the changes promoted by DT will fundamentally change the relationship at work, the development of which will decrease employment hassles and regulations, where workers will have more freedom, less stress, and greater job satisfaction (Schwab, 2017 ).

4.2.5 Big data

The literature claims that the way we understand and embrace the data movement will shape our futures, lives, economies, societies, and the choices we make, promoting changes that will be for the better (Marti et al., 2016 ). The realm of big data involves several areas of applications, a new generation of products, systems, and services, that will increase its presence in our quotidian, promoting more smart interactions to improve our lives, enabling the understanding of consumer needs, prediction of trends, and maximizing the decision-making in the face of change and increasing challenges, in a market expected to reach $229.4 billion by 2025, impacting various industries such as health care, transportation, manufacturing, and entertainment (Chapman et al., 2020 ; Deepa et al., 2022 ; Marti et al., 2016 ; Wehn & Evers, 2015 ). Big data is recognized as one of the main technologies of the Fourth Industrial Revolution, and despite the lack of a precise definition, it can be identified as a new generation of technologies that investigate and analyze a large amount of data and capture its main characteristics (Deepa et al., 2022 ; Kim, 2021 ).

Big data have been applied extensively across diverse fields such as smart cities (SDG #11), smart health care (SDG # 3), food industry management practices and efficient production (SDG # 2), energy transmission (SDG # 7), and industrial transformation (SDG # 9), becoming a crucial measure for the development, promotion, and management of a strategic new environment (Chapman et al., 2020 ; Deepa et al., 2022 ; Hajjaji et al., 2021 ; Marti et al., 2016 ). Although significant progress has been achieved in varied applications, it can still be a challenging task to capture, store, process, and analyze the vast volumes of data computational intelligence techniques, still requiring huge processing power over high-speed data streams, which in turn requires fast, efficient, and large-scale capacity (Deepa et al., 2022 ).

4.2.6 Machine learning

Machine learning is a key technology in artificial intelligence that requires a large amount of data and special algorithms to develop models using pattern recognition to perform meaningful real-time analysis, predict events before they happen, and support the decision-making process (Ghazal et al., 2021 ; López-Martínez et al., 2020 ). Usually connected with AI and IoT, machine learning algorithms can be applied in the improvements of processes related to water (SDG # 6), energy (SDG # 7), agriculture (SDG # 2), health care (SDG # 3), education (SDG # 4), among many others, being a key concept for the development of smart cities (Ghazal et al., 2021 ).

The success of machine learning applications depends on the accuracy of the models and their interpretability, thus one of the most critical challenges requires algorithms that can answer causal questions. These questions are beyond classical machine learning algorithms because they require a formal model of therefore the analytical component of the platform, need to learn from data differently, and to gain knowledge in causal models to understand how machine learning algorithms need to be trained. Another challenge is to create reliable outcomes from heterogeneous data sources, whose interpretation depends on the criteria and context defined, so engineers should work together on model interpretability and applicability (López-Martínez et al., 2020 ).

4.2.7 Digital transformation

Digital transformation is defined as “the process of exploiting digital technologies and supporting capabilities to create a robust new digital business model” (Chierici et al., 2020 , p. 612). The existing literature has underlined many aspects of digital transformation, such as the organizational dimension, stressing the integration of digital technologies and business processes in a digital economy, the changes brought to business models, in the form of product innovation or organizational structures or the automation of processes, the use of new digital technologies to enhance customer experience, and streamline operations, and most importantly the change in the strategic vision of businesses, especially because digital transformation is leading to high changes and demands for future working practices (Chierici et al., 2020 ; Kohlgrüber et al., 2021 ). The concept embraces all relevant technologies and has the power to impact most of the SDGs.

Among applications of DT, there is the case of small farms, where social enterprises deploy ICT platforms to build sustainable ecosystems for addressing their challenges, providing scalable and accelerated transformative change in their businesses, collaborating to reduce poverty (SDG #1) and inequalities (SDG #10), and improving production methods (SDG #12). In this case, ICT facilitates the co-creation of public services, which empowers NGOs, social enterprises, and other relevant private companies to participate in offering public services (SDG #17), demonstrating the full power of digital transformation (Soni et al., 2021 ). Another case shows the integrating power of digital transformations (SDG # 17), where the rapid and wide diffusion of information and communication technologies (ICTs) allowed the development of a platform of electronic participation (SDG # 11), which, with the use of monitoring technologies together with other interactive apps and social media, implemented citizen observatories to prevent flood risk management, where citizens, public and private sector work together to prevent damage in both fluvial and marine ecosystems (SDG #14), raising awareness on flood risk and its management, with insights and collective knowledge of communities (Wehn & Evers, 2015 ).

4.2.8 Industry 4.0

According to Schwab (2016), the Fourth Industrial Revolution began at the turn of the twentieth century and builds on the digital revolution, generating breakthroughs in areas ranging from gene sequencing to nanotechnology, from renewables to quantum computing. The interaction of these technologies across the physical, digital, and biological domains, makes the Fourth Industrial Revolution fundamentally different from previous revolutions, with broad-based innovations diffusing much faster and more widely than before, unfolding in all parts of the world. The concept of Industry 4.0 led to the advancement of further novel concepts, such as Society 5.0 and Industry 5.0, which place human beings at the midpoint of innovation, exploiting the impact of technology and Industry 4.0 results to improve quality of life, social responsibility, and sustainability, aligned with many common points with the objectives of the United Nations Sustainable Development Goals (Carayannis & Morawska-Jancelewicz, 2022 ).

Our analysis is summarized in Table 3 , which leads us to conclude that the spread of digital transformations is directly connected with social innovations, promoted throughout society in many ways. We observe a predominance of the impact of DSI in SDGs related to hunger (2), healthcare (3), and sustainable cities (11), which are considered the main social problems faced by the world today (Nuren, 2021 ). But overall, we can observe the potential of DSI to solve social–environmental problems and its role in addressing SDGs and the Agenda 2030.

5 Concluding remarks

This study aimed to identify the connection between digital transformation and social innovation to solve social problems, developing the concept of digital social innovation (DSI) and its relationship with the UN’s SDGs, which explicitly define the Science, Technology, and Innovation (STI) policy as a key implementation mechanism, that has a transformative mechanism to tackle the 17 SDGs and support their transformation potential. As a result of a systematic literature review, using bibliometric and content analysis, we provide relevant findings on the evolution of studies from these concepts, establish the connection between digital social innovations and SDGs, and the opportunities and threats they face. We believe that we contribute to the literature by indicating the growing connection between DT and SI, which combined demonstrates the power to address varied SDGs, and actively collaborate for the success of Agenda 2030. We also expect to offer a practical contribution by promoting the continuous adoption of digital transformations and widespread social innovations, emphasizing the necessary engagement and collaboration between the different stakeholders involved.

Considering the quick evolution and massive adoption of DSIs and our objective to further explore this concept, we used our bibliometric analysis that searches to understand the evolution of the literature of digital social innovations and identify gaps and trends to thus propose a research agenda for future studies. We observed that some streams of research showed more incidence of DSIs, namely smart cities, healthcare, and food production need to be further explored (Table 4 ). We also noticed that the main four technologies identified in the content analysis—blockchain; IoT; artificial intelligence; and autonomous robots—are increasing their role and presence exponentially, completely changing the current way of doing things, and most of the time working in combination to offer a dramatic evolution in these different segments, and further address SDGs. There are many opportunities for the development and widespread use of innovative technologies, while we identified many threats concerning ethics, especially with the increased use of public data, and concerns about the impacts on the labor force and the possible instability and impact it may cause in low-skill/low-pay jobs.

This systematic review provides an overall idea of the evolution, connections, development, opportunities, and threats of digital social innovations. However, we strongly suggest in-depth studies, based on case studies and/or longitudinal studies, not only in the segments presented in our study but also in other segments that may benefit from DSI and DT, such as education or energy, which would offer a more practical perspective of the impact of these concepts.

Availability of data and material

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Appendix 1 : United Nation’s sustainable development goals

GOAL 1: No poverty.

GOAL 2: Zero hunger.

GOAL 3: Good health and well-being.

GOAL 4: Quality education.

GOAL 5: Gender equality.

GOAL 6: Clean water and sanitation.

GOAL 7: Affordable and clean energy.

GOAL 8: Decent work and economic growth.

GOAL 9: Industry, innovation, and infrastructure.

GOAL 10: Reduced inequality.

GOAL 11: Sustainable cities and communities.

GOAL 12: Responsible consumption and production.

GOAL 13: Climate action.

GOAL 14: Life below water.

GOAL 15: Life on land.

GOAL 16: Peace and justice strong institutions.

GOAL 17: Partnerships to achieve the goal.

Appendix 2 : List of 45 articles in the systematic literature review

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Dionisio, M., de Souza Junior, S.J., Paula, F. et al. The role of digital social innovations to address SDGs: A systematic review. Environ Dev Sustain 26 , 5709–5734 (2024). https://doi.org/10.1007/s10668-023-03038-x

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• Experts Predict More Digital Innovation by 2030 Aimed at Enhancing Democracy
• 5. Tech causes more problems than it solves

Table of Contents

• 1. The innovations these experts predict by 2030
• 2. Tech is (just) a tool
• 3. Power dynamics play a key role in problems and innovation
• 4. It’s all just history repeating itself
• 6. The net effects in 10 years will be negligible
• About this canvassing of experts
• Acknowledgments

A number of respondents to this canvassing about the likely future of social and civic innovation shared concerns. Some said that technology causes more problems than it solves. Some said it is likely that emerging worries over the impact of digital life will be at least somewhat mitigated as humans adapt. Some said it is possible that any remedies may create a new set of challenges. Others said humans’ uses and abuses of digital technologies are causing societal harms that are not likely to be overcome.

The following comments were selected from among all responses, regardless of an expert’s answer to this canvassing’s main question about the impact of people’s uses of technology. Some of these remarks of concern happen to also include comments about innovations that may emerge. Concerns are organized under four subthemes: Something is rotten in the state of technology; technology use often disconnects or hollows out a community; society needs to catch up and better address the threats and opportunities of tech; and despite current trends, there is reason to hope for better days.

The chapter begins with some overview insights:

Larry Masinter , internet pioneer, formerly with Adobe, AT&T Labs and Xerox PARC, who helped create internet and web standards with IETF and W3C, said, “Technology and social innovation intended to overcome the negatives of the digital age will likely cause additional negative consequences. Examples include: the decentralized web, end-to-end encryption, AI and machine learning, social media.”

James Mickens , associate professor of computer science at Harvard University, formerly with Microsoft, commented, “Technology will obviously result in ‘civic innovation.’ The real question is whether the ‘innovation’ will result in better societal outcomes. For example, the gig economy is enabled by technology; technology finds buyers for workers and their services. However, given the choice between an economy with many gig workers and an economy with an equivalent number of traditional middle-class jobs, I think that most people would prefer the latter.”

Michael Aisenberg , chair, ABA Information Security Committee, wrote, “Misappreciation of limits and genesis of, e.g., AI/machine learning will produce widely disparate results in deployment of tech innovations. Some will be dramatically beneficial; some may enable abuse of law enforcement, economic systems and other fundamental civic institutions and lead to exacerbation of gaps between tech controllers/users and underserved/under- or mis-skilled populations (‘digital divide’) in what may be a significant (embed limitations on career/economic advancement) or even life-threatening (de facto health care or health procedure rationing) manner.”

The problem is that we are becoming more and more dependent on machines and hence more susceptible to bugs and system failures. Yaakov J. Stein

Peter Lunenfeld , a professor of design, media arts and digital humanities at the University of California, Los Angeles, and author of “Tales of the Computer as Culture Machine,” predicted, “We will use technology to solve the problems the use of technology creates, but the new fixes will bring new issues. Every design solution creates a new design problem, and so it is with the ways we have built our global networks. Highly technological societies have to be iterative if they hope to compete, and I think that societies that have experienced democracy will move to curb the slide to authoritarianism that social media has accelerated. Those curbs will bring about their own unintended consequences, however, which will start the cycle anew.”

Yaakov J. Stein , chief technology officer of RAD Data Communications, based in Israel, responded, “The problem with AI and machine learning is not the sci-fi scenario of AI taking over the world and not needing inferior humans. The problem is that we are becoming more and more dependent on machines and hence more susceptible to bugs and system failures. This is hardly a new phenomenon – once a major part of schooling was devoted to, e.g., penmanship and mental arithmetic, which have been superseded by technical means. But with the tremendous growth in the amount of information, education is more focused on how to retrieve required information rather than remembering things, resulting not only in less actual storage but less depth of knowledge and the lack of ability to make connections between disparate bits of information, which is the basis of creativity. However, in the past humankind has always developed a more-advanced technology to overcome limitations of whatever technology was current, and there is no reason to believe that it will be different this time.”

A vice president for research and economic development wrote, “The problems we see now are caused by technology, and any new technological fixes we create will inevitably cause NEW social and political problems. Attempts to police the web will cause freedom of speech conflicts, for example.”

Something is rotten in the state of technology

A large share of these experts say among the leading concerns about today’s technology platforms are the ways in which they are exploited by bad actors who spread misinformation; and the privacy issues arising out of the business model behind the systems.

Misinformation – pervasive, potent, problematic

Numerous experts described misinformation and fake news as a serious issue in digital spaces. They expressed concern over how users will sort through fact and fiction in the coming decade.

Stephanie Fierman , partner, Futureproof Strategies, said, “I believe technology will meaningfully accelerate social and civic innovation. It’s cheap, fast and able to reach huge audiences. But as long as false information is enabled by very large websites, such social and civic innovators will be shadow boxing with people, governments, organizations purposely countering truthful content with lies.”

Sam Lehman-Wilzig , a professor of communications at Bar-Ilan University specializing in Israeli politics and the impact of technological evolution, wrote, “The biggest advance will be the use of artificial intelligence to fight disinformation, deepfakes and the like. There will be an AI ‘arms race’ between those spreading disinformation and those fighting/preventing it. Overall, I see the latter gaining the upper hand.”

Greg Shatan , a lawyer with Moses & Singer LLP and self-described “internet governance wonk,” predicted, “I see success, enabled by technology, as likely. I think it will take technology to make technology more useful and more meaningful. Many of us pride ourselves on having a ‘BS-meter,’ where we believe we can tell honestly delivered information from fake news and disinformation. The instinctual BS-meter is not enough. The next version of the ‘BS-meter’ will need to be technologically based. The tricks of misinformation have far outstripped the ability of people to reliably tell whether they are receiving BS or not – not to mention that it requires a constant state of vigilance that’s exhausting to maintain. I think that the ability and usefulness of the web to enable positive grassroots civic communication will be harnessed, moving beyond mailing lists and fairly static one-way websites. Could there be ‘Slack for Community Self-Governance?’ If not that platform, perhaps something new and aimed specifically at these tasks and needs.”

Oscar Gandy , a professor emeritus of communication at the University of Pennsylvania, said, “Corporate actors will make use of technology to weaken the possibility for improvements in social and civic relationships. I am particularly concerned about the use of technology in the communications realm in order to increase the power of strategic or manipulative communications to shape the engagement of members of the public with key actors within a variety of governance relationships.”

An expert in the ethics of autonomous systems based in Europe responded, “Fake news is more and more used to manipulate a person’s opinion. This war of information is becoming so important that it can influence democracy and the opinion of people before the vote in an election for instance. Some AI tools can be developed to automatically recognize fake news, but such tools can be used in turn in the same manner to enhance the belief in some false information.”

A research leader for a U.S. federal agency wrote, “At this point in time, I don’t know how we will reduce the spread of misinformation (unknowing/individual-level) and disinformation (nefarious/group-level), but I hope that we can.”

A retired information science professional commented, “Dream on, if you think that you can equate positive change with everybody yelling and those with the most clout (i.e., power and money) using their power to see their agendas succeed. Minority views will always be that, a minority. At present and in the near future the elites manipulate and control.”

A research scientist for a major technology company whose expertise is technology design said, “We have already begun to see increased protections around personal privacy. At present, it is less clear how we might avoid the deliberate misuse of news or news-like content to manipulate political opinions or outcomes, but this does not seem impossible. The trick will be avoiding government censorship and maintaining a rich, vigorous exchange of opinions.”

Privacy issues will continue to be a hot button topic

Multiple experts see a growing need for privacy to be addressed in online spaces.

Ayden Férdeline , technology policy fellow at the Mozilla Foundation, responded, “Imagine if everyone on our planet was naked, without any clear options for obtaining privacy technology (clothing). It would not make sense to ask people what they’d pay or trade to get this technology. This is a ‘build it and they will come’ kind of scenario. We’re now on the verge, as a society, of appropriately recognizing the need to respect privacy in our Web 2.0 world, and we are designing tools and rules accordingly. Back in 1992, had you asked people if they’d want a free and open internet, or a graphical browser with a walled garden of content, most would have said they prefer AOL. What society needed was not AOL but something different. We are in a similar situation now with privacy; we’re finally starting to grasp its necessity and importance.”

We’re now on the verge, as a society, of appropriately recognizing the need to respect privacy in our Web 2.0 world, and we are designing tools and rules accordingly. Ayden Férdeline

Graham Norris , a business psychologist with expertise in the future of work, said, “Privacy no longer exists, and yet the concept of privacy still dominates social-policy debates. The real issue is autonomy of the individual. I should own my digital identity, the online expression of myself, not the corporations and governments that collect my interactions in order to channel my behaviour. Approaches to questions of ownership of digital identity cannot shift until the realization occurs that autonomy is the central question, not privacy. Nothing currently visible suggests that shift will take place.”

Eduardo Villanueva-Mansilla , an associate professor of communications at Pontificia Universidad Catolica, Peru, and editor of the Journal of Community Informatics, wrote, “I’m trying to be optimistic, by leaving some room to innovative initiatives from civic society actors. However, I don’t see this as necessarily happening; the pressure from global firms will probably too much to deal with.”

An international policy adviser on the internet and development based in Africa commented, “Technology is creating and will continue to evolve and increase the impact of social and civic innovation. With technology we will see new accountability tools and platforms to raise voices to counter societal ills, be it in leadership, business and other faculties. We must however be careful so that these innovations themselves are not used to negatively impact end users, such issues like privacy and use of data must be taken on in a way that users are protected and not exposed to cybercrime and data breaches that so often occur now.”

Jamie Grady , a business leader, wrote, “As technology companies become more scrutinized by the media and government, changes – particularly in privacy rights – will change. People will learn of these changes through social media as they do now.”

Technology use often disconnects or hollows out community

Some respondents commented on rising problems with a loss of community and the need for more-organic, in-person, human-to-human connection and the impact of digital distancing.

Jonathan Grudin , principal researcher at Microsoft, commented, “Social and civic activity will continue to change in response to technology use, but will it change its trajectory? Realignments following the Industrial Revolution resulted from the formation of new face-to-face communities, including union chapters, community service groups such as Rotary Club and League of Women Voters, church groups, bridge clubs, bowling leagues and so on. Our species is designed to thrive in modest-sized collocated communities, where everyone plays a valued part. Most primates become vulnerable and anxious when not surrounded by their band or troop. Digital media are eroding a sense of community everywhere we look. Can our fundamental human need for close community be restored or will we become more isolated, anxious and susceptible to manipulation?”

Rebecca Theobald , an assistant research professor at the University of Colorado, Colorado Springs, said, “Technology seems to be driving people apart, which would lead to fewer connections in society.”

The program director of a university-based informatics institute said, “There is still a widening gap between rural and urban as well as digital ‘haves’ and ‘have nots.’ As well, the ability to interact in a forum in which all members of society have a voice is diminishing as those with technology move faster in the digital forums than the non-tech segment of the population that use non-digital discourse (interpersonal). The idea of social fabric in a neighborhood and neighborly interactions is diminishing. Most people want innovation – it is the speed of change that creates divisions.”

An infrastructure architect and internet pioneer wrote, “The kind of social innovation required to resolve the problems caused by our current technologies relies on a movement back toward individual responsibility and a specific willingness to engage in community. As both of these work against the aims of the corporate and political elite as they exist today, there is little likelihood these kinds of social innovations are going to take place. The family and church, for instance, which must be the core institutions in any rebuilding of a culture that can teach the kind of personal responsibility required, were both hollowed out in the last few decades. The remaining outward structures are being destroyed. There is little hope either families or churches will recover without a major societal event of some sort, and it will likely take at least one generation for them to rebuild. The church could take on the task of helping rebuild families, but it is too captured in attempts to grow ever larger, and consume or ape our strongly individualistic culture, rather than standing against it.”

A researcher based in North America predicted a reining in of the digital in favor of the personal: “Between email and phones, I think we’re close to peak screen time, a waste of time, and it’s ruining our eyes. Just as we have forsaken our landlines, stopped writing letters, don’t answer our cellphones, a concept of an average daily digital budget will develop, just as we have a concept of average daily caloric intake. We’ll have warning labels that rate content against recommended daily allowances of different types of content that have been tested to be good for our mental health and socialization, moderately good, bad, and awful – the bacon of digital media. And people who engage too much will be in rehab, denied child custody and unemployable. Communities, residences and vacation areas will promote digital-free, mindfulness zones – just as they have quiet cars on the train.”

Society needs to catch up and better address the threats and opportunities of tech

Some of these experts said that the accelerating technological change of the digital age is making it difficult for humans to keep up and respond to emerging challenges.

A chair of political science based in the American South commented, “Technology always creates two new problems for every one it solves. At some point, humans’ cognitive and cooperative capacities – largely hard-wired into their brains by millennia of evolution – can’t keep up. Human technology probably overran human coping mechanisms sometime in the later 19th century. The rest is history.”

There is a gap between the rate at which technology develops and the rate at which society develops. We need to take care not to fall into that gap. Louisa Heinrich

Larry Rosen , a professor emeritus of psychology at California State University, Dominguez Hills, known as an international expert on the psychology of technology, wrote, “I would like to believe that we, as citizens, will aid in innovation. Smart people are already working on many social issues, but the problem is that while society is slow to move, tech moves at lightning speed. I worry that solutions will come after the tech has either been integrated or rejected.”

Louisa Heinrich , a futurist and consultant expert in data and the Internet of Things, said, “There is a gap between the rate at which technology develops and the rate at which society develops. We need to take care not to fall into that gap. I hope we will see a shift in governance toward framework-based regulation, which will help mitigate the gap between the pace of change in technology and that in government. At the very least, we need to understand the ways in which technology can extend or undermine the rules and guidelines we set for our businesses, workplaces, public spaces and interactions. To name just one common example, recruitment professionals routinely turn to Facebook as a source of information on prospective employees. This arguably violates a number of regulations designed to protect people from being denied work based on personal details not relevant to that work. How do we unravel this conundrum, bearing in mind that there will always be another social network, another digital source to mine for information about people? Taken from another angle, there is a significant gap between what users understand about certain bits of technology and the risks they take using them. How can we educate people about these risks in a way that encourages participation and co-creation, rather than passivity? As the so-called Gen Z comes of age, we will see a whole generation of young adults who are politically engaged at a level not seen in several generations, who are also native users of technology tools. This could bring about a positive revolution in the way technology is used to facilitate civic engagement and mutually empower and assist citizens and government. Technology provides us with powerful tools that can help us advance socially and civically, but these tools need to be thoughtfully and carefully put to use – when we encode barriers and biases into the applications that people need to use in daily life, whether intentionally or no, we may exclude whole segments of society from experiencing positive outcomes. We are living through a time of rapid and radical change – as always, the early stages feel uncomfortable and chaotic. But we can already see the same tools that have been used to mislead citizens being used to educate, organise, motivate and empower them. What’s needed is a collective desire to prioritise and incentivise this. New Zealand is leading the way with the world’s first ‘well-being’ budget.”

Bulbul Gupta , founding adviser at Socos Labs, a think tank designing artificial intelligence to maximize human potential, responded, “Until government policies, regulators, can keep up with the speed of technology and AI, there is an inherent imbalance of power between technology’s potential to contribute to social and civic innovation and its execution in being used this way. If technology and AI can make decisions about people in milliseconds that can prevent their full social or civic engagement, the incentive structures to be used toward mitigating the problems of the digital age cannot then be solved by technology.”

Gene Policinski , a journalist and First Amendment law expert at the Freedom Forum Institute, observed, “We forget how new the ‘tech revolution’ really is. As we move forward in the next decade, the public’s awareness of the possibilities inherent in social and civic innovation, the creativity of the tech world working with the public sector and public acceptance of new methods of participation in democratic processes will begin to drown out and eventually will surpass the initial problems and missteps.”

Gabriel Kahn , former bureau chief for The Wall Street Journal, now a professor of journalism researching innovation economics in emerging media at the University of Southern California, wrote, “We are not facing a ‘Terminator’-like scenario. Nor are we facing a tech-driven social utopia. Humans are catching up and understanding the pernicious impact of technology and how to mitigate it.”

Kathee Brewer , director of content at CANN Media Group, predicted, “Much like society developed solutions to the challenges brought about by the Industrial Revolution, society will find solutions to the challenges of the Digital Revolution. Whether that will happen by 2030 is up for debate. Change occurs much more rapidly in the digital age than it did at the turn of the 20th century, and for society to solve its problems it must catch up to them first. AND people, including self-interested politicians, must be willing to change. Groups like the Mozilla Foundation already are working on solutions to invasions of privacy. That work will continue. The U.S. government probably won’t make any major changes to the digital elections framework until after the 2020 election, but changes will be made. Sadly, those changes probably will result from some nastiness that develops due to voters of all persuasions being unwilling to accept electoral results, whatever the results may be.”

Valerie Bock of VCB Consulting, former Technical Services Lead at Q2 Learning, responded, “I think our cultures are in the process of adapting to the power our technologies wield, and that we will have developed some communal wisdom around how to evaluate new ones. There are some challenges, but because ordinary citizens have become aware that images can be ‘photoshopped’ the awareness that video can be ‘deepfaked’ is more quickly spreading. Cultural norms as well as technologies will continue to evolve to help people to apply more informed critiques to the messages they are given.”

Bach Avezdjanov , a program officer with Columbia University’s Global Freedom of Expression project, said, “Technological development – being driven by the Silicon Valley theory of uncontrolled growth – will continue to outpace civic and social innovation. The latter needs to happen in tandem with technological innovation, but instead plays catch-up. This will not change in the future, unless political will to heavily regulate digital tools is introduced – an unlikely occurrence.”

A computing science professor emeritus from a top U.S. technological university commented, “Social/civic innovation will occur but most likely lag well behind technological innovation. For example, face-recognition technology will spread and be used by businesses at a faster pace than social and legal norms can develop to protect citizens from any negative effects of that technology. This technology will spread quickly, due to its various positives (increased efficiencies, conveniences and generation of profits in the marketplace) while its negatives will most likely not be countered effectively through thoughtful legislation. Past Supreme Court decisions (such as treating corporations as persons, WRT unlimited funding of political candidates, along with excessive privacy of PACs) have already undermined U.S. democracy. Current populist backlashes, against the corruption of the Trump government, may also undermine democracy, such as the proposed Elizabeth Warren tax, being not on profits, but upon passive wealth itself – a tax on non-revenue-producing illiquid assets (whose valuation is highly subjective), as in her statement to ‘tax the jewelry of the rich’ at 2% annually. Illiquid assets include great private libraries, great private collections of art, antiques, coins, etc. – constituting an assault on the private sector, that if successful, will weaken democracy by strengthening the confiscatory power of government. We could swing from current excesses of the right to future excesses of the left.”

Despite current trends, there is reason to hope for better days

Many of the experts in this canvassing see a complicated and difficult road ahead, but express hope for the future.

Cheryl B. Preston , an expert in internet law and professor at Brigham Young University Law School, said, “Innovation will bring risk. Change will bring pain. Learning will bring challenges. Potential profits will bring abuse. But, as was the decision of Eve in the Garden of Eden, we need to leave the comfortable to learn and improve. If we can, by more informed voting, reduce the corruption in governmental entities and control corporate abuse, we can overcome difficulties and advance as a society. These advances will ultimately bring improvement to individuals and families.”

John Carr , a leading global expert on young people’s use of digital technologies, a former vice president of MySpace, commented, “I know of no proof for the notion that more people simply knowing more stuff, even stuff that is certifiably factually accurate, will necessarily lead to better outcomes for societies. But I do harbour a hope that if, over time, we can establish the idea that there are places on the internet that are reliable sources of information, it will in the medium to longer term help enough people in enough countries to challenge local demagogues and liars, making it harder for the demagogues and liars to succeed, particularly in times of national crisis or in times when war might be on the visible horizon. I used to think that if the internet had been around another Hitler would be impossible. Recently I have had a wobble on that but my optimism ‘trumps’ that gloomy view.”

Mike Douglass , an independent developer, wrote, “There is a significant realization that a stampede to create connections between anonymous people and devices was a bad idea. It’s up to the technologists and – more importantly – those who want to make money out of technology – to come up with a more measured approach. There’s a reason why gentlemen obtained letter of introduction to other gentlemen – one shouldn’t trust some random individual turning up on your doorstep. We need the equivalent approach. I’ve no idea what new innovations might turn up. But if we don’t get the trust/privacy/security model right we’ll end up with more social media disasters.”

Hume Winzar , an associate professor and director of the business analytics undergraduate program at Macquarie University, Sydney, Australia, predicted, “With more hope than evidence, I’d like to think that reason will eventually overcome the extraordinary propaganda machines that are being built. When the educated upper-middle classes realise that the ‘system’ is no longer serving them, then legal and institutional changes will be necessary. That is, only when the managers who are driving the propaganda machine(s) start to feel that they, personally, are losing privacy, autonomy, money and their children’s future, then they will need to undermine the efforts of corporate owners and government bureaucrats and officials.”

Carolyn Heinrich , a professor of education and public policy at Vanderbilt University, said, “My hope (not belief) is that the ‘techlash’ will help to spur social and civic innovations that can combat the negative effects of our digitization of society. Oftentimes, I think the technology developers create their products with one ideal in mind of how they will be used, overlooking that technology can be adapted and used in unintended and harmful ways. We have found this in our study of educational technology in schools. The developers of digital tools envision them as being used in classrooms in ‘blended’ ways with live instructors who work with the students to help customize instruction to their needs. Unfortunately, more often than not, we have seen the digital tools used as substitutes for higher-quality, live instruction and have observed how that contributes to student disengagement from learning. We have also found some of the content lacking in cultural relevance and responsiveness. If left unchecked, this could be harmful for far larger numbers of students exposed to these digital instructional programs in all 50 states. But if we can spur vendors to improve the content, those improvements can also extend to large numbers of students. We have our work cut out for us!”

In the field I follow, artificial intelligence, the numbers of professionals who take seriously the problems that arise as a consequence of this technology are reassuring. Pamela McCorduck

Heywood Sloane , entrepreneur and banking and securities consultant, wrote, “I’m hopeful the it will be a positive contributor. It has the ability to alter the way we relate to our environment in ways that shrink the distances between people and help us exercise control over our personal and social spaces. We are making substantial progress, and 5G technology will accelerate that. On the flip side, we need to find mechanisms and processes to protect our data and ourselves. They need to be strong, economic and simple to deploy and use. That is going to be a challenge.”

Pamela McCorduck , writer, consultant and author of several books, including “Machines Who Think,” commented, “I am heartened by the number of organizations that have formed to enhance social and civic organization through technology. In the field I follow, artificial intelligence, the numbers of professionals who take seriously the problems that arise as a consequence of this technology are reassuring. Will they all succeed? Of course not. We will not get it right the first time. But eventually, I hope.”

Yoshihiko Nakamura , a professor of mechno-informatics at the University of Tokyo, observed, “The current information and communication technology loses diversity because it is still insufficient to enhance the affectivity or emotion side of societies. In this sense I can see the negative side of current technology to human society. However, I have a hope that we can invent uses of technology to enhance the weaker side and develop tomorrow’s technology. The focus should be on the education of society in the liberal arts.”

Ryan Sweeney , director of analytics at Ignite Social Media, commented, “In order to survive as a functioning society, we need social and civic innovation to match our use of technology. Jobs and job requirements are changing as a result of technology. Automation is increasing across a multitude of industries. Identifying how we protect citizens from these changes and help them adapt will be instrumental in building happiness and well-being.”

Miles Fidelman , founder, Center for Civic Networking and principal Protocol Technologies Group, responded, “We can see clear evidence that the internet is enabling new connections, across traditional boundaries – for the flow of information, culture and commerce. It is strengthening some traditional institutions (e.g., ties between geographically distributed family members) and weakening others (e.g., the press). Perhaps the most notable innovation is that of ad hoc, network-centric organizations – be they global project teams, or crisis response efforts. How much of this innovation will make things better, how much it will hurt us, remains an open question.”

A technology developer active in IETF said, “I hope mechanisms will evolve to exploit the advantages of new tech and mitigate the problems. I want to be optimistic, but I am far from confident.”

A renowned professor of sociology known for her research into online communications and digital literacies observed, “New groups expose the error of false equivalence and continue to challenge humans to evolve into our pre-frontal cortex. I guess I am optimistic because the downside is pretty terrible to imagine. It’s like E.O. Wilson said: ‘The real problem of humanity is the following: We have paleolithic emotions; medieval institutions; and god-like technology. And it is terrifically dangerous, and it is now approaching a point of crisis overall.’”

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That means that the healthcare industry could literally digitize everything it does without privacy and security concerns. The data can then be made available to any physician anywhere in order to get a true picture of a patient's medical and health history without compromising any of the privacy laws in effect that are intended to protect a patient. Every aspect of what a patient goes through with tests, results and procedures can be digitized, stored and shared with approved people. This removes the silos and inefficiencies that currently exist, improving care and reducing costs. This is just one example of how technology can start working on major issues.

An Amnesty International article  noted that technology can help address everything from pollution and poverty to human rights. The article illustrated how everything from app development for tracking human rights violations to smart tracking systems that can measure how changes are positively impacting the environment are the best ways for technology leaders to get involved with helping social problems around the world. Even the development of mobile banking solutions or other ways to conduct financial transactions without a bank account can help with poverty because they are enabling more people in developed countries to create a business and support themselves.

Thought leadership and involvement in United Nations councils or other organizations are other ways that technology leadership can help drive change. Mark Zuckerberg, Bill Gates and others have appeared on the world stage to publicly advocate  internet access for all as a means of equalizing opportunities around the world that could lead to the end of hunger, poverty and human rights violations. While these are recognizable faces, it shouldn't stop entrepreneurs of startups or less recognizable technology leaders to step up and vocalize their advocacy.

Challenges Remain

Technology leaders cannot do it on their own or even as a collaborative group. However, the ability to work with grassroots organizations, community leadership groups and even in partnership with the government may address the remaining challenges. On its own, technology and the leaders who represent those solutions must adhere to existing laws and consider the overall impact that making changes can have on those affected by a particular social issue.

What technology leaders can also do is figure out how their solutions can then empower those involved or affected by these social ills and begin to attack these problems from that angle. However technology leaders decide to get involved, the most important aspect is to do something for good rather than sit back and just hope someone else does it for us all. As someone involved in technology every day, I picked this career because I wanted to do something that would make a difference -- social or otherwise.

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Survey of Adult Skills (PIAAC)

The Survey of Adult Skills, a product of the PIAAC, measures adults’ proficiency in literacy, numeracy and the ability to solve problems in technology-rich environments.

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The Programme for the International Assessment of Adult Competencies (PIAAC) is a programme of assessment and analysis of adult skills. The major product of PIAAC is the Survey of Adult Skills, an international computer-based household survey of adults aged 16-65 years. It is designed as 10-yearly cycles.

The Survey measures adults’ proficiency in key information-processing skills - literacy, numeracy and problem solving – which represent skills needed for individuals to participate in society and for economies to prosper. It also gathers information and data on how adults use their skills at home and at work.

The 1 st Cycle of the Survey of Adult Skills was conducted over three separate rounds between 2011 and 2018 in 39 countries. During the 1 st Cycle, about 245 000 adults were interviewed, representing 1.15 billion people.

The 2 nd Cycle of the Survey of Adults Skills has been conducted in 31 countries and economies so far. A first round of data collection took place in 2022-2023 with results to be released on 10 December 2024.  

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Piaac 1st cycle.

For the PIAAC 1st Cycle, 39 countries/economies participated in the Survey of Adult Skills (PIAAC) between 2011-2018. Data was collected in three different rounds.

Round 1 (2011-2012)

Australia, Austria, Belgium (Flanders), Canada, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Japan, Korea, Netherlands, Norway, Poland, Russian Federation, Slovak Republic, Spain, Sweden, United Kingdom (England and Northern Ireland), United States 

Round 2 (2014-15)

Chile, Greece, Indonesia, Israel, Lithuania, New Zealand, Singapore, Slovenia, Türkiye

Round 3 (2017)

Ecuador, Hungary, Kazakhstan, Mexico, Peru, United States

PIAAC 2nd Cycle

For the PIAAC 2nd Cycle, 31 countries/economies participated in the Survey of Adult Skills (PIAAC) in 2023.

Round 1 (2023)

Austria, Belgium (Flanders), Canada, Chile, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Israel, Italy, Japan, Korea, Latvia, Lithuania, Netherlands, New Zealand, Norway, Poland, Portugal, Singapore, Slovak Republic, Spain, Sweden, Switzerland, United Kingdom (England), United States 

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Countries and economies, adults representing over , 1.15 billion, adults worldwide          .

The 2nd Cycle of the Survey of Adult Skills , a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC), continues to assess literacy and numeracy so that countries can track how the skills of the adult population have evolved. The assessment has been updated to better reflect tasks adults must engage in within modern societies, and it now provides more accurate information on low performers. It also includes a new assessment of Adaptive Problem Solving, a new module of the Background Questionnaire assessing the social and emotional skills of respondents, and revised questions to collect richer information on education and training and on features of the working environment related to skills use and development, among others.

The results from the first round of the 2nd Cycle of the Survey of Adult Skills will be released on 10 December 2024 .

The OECD is now planning a second round of data collection from 2025 to 2029. Round 2 would be our next opportunity to accommodate new countries into PIAAC Cycle 2.

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Countries and economies applying now will be eligible to participate in PIAAC Cycle 2, Round 2 data collection.

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For queries or more information, please contact the PIAAC team at  edu.piaac@oecd.org

Environmental Science: Water Research & Technology

Solving biofouling problem of uranium extraction from seawater by plasma technology.

The effective extraction of uranium (U(VI)) from seawater is critical for the sound development of nuclear energy in near future. Biofouling is one of the core problems of U(VI) extraction from seawater that must be solved soon. In this work, plasma technology is applied to solve biofouling problem of U(VI) extraction from seawater. Experimental results show that reactive oxygen species (ROS) formed during plasma discharging process can effectively kill marine microorganisms in 30 min by destroying its wall membrane structure and remove its extracellular polymers (EPS), which can sound improve its U(VI) adsorption capability. Plasma treatment also has a significant effect on the microorganism compositions in seawater, and can effectively kill Proteobacteria species including V. alginolyticus. In summary, plasma sterilization is a fast, effective, and simple process. It can sound solve the biofouling problem, and simultaneously improve the recovery capability of PAO based materials for U(VI) from seawater.

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X. Zhang and D. Shao, Environ. Sci.: Water Res. Technol. , 2024, Accepted Manuscript , DOI: 10.1039/D4EW00226A

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