review of literature knowledge management

The current understanding of knowledge management concepts: A critical review

Shahram yazdani.

1 Virtual school of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Snor Bayazidi

Amir ali mafi.

2 Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Background: Higher education institutions include experts who are knowledgeable. Knowledge management facilitates institutions to enhance the capacity to collect information and knowledge and apply it to problem-solving and decision making. Through the review of related studies, we observed that there are multiple concepts and terms in the field of knowledge management. Thus, the complexity and variety of these concepts and definitions must be clarified. Considering the importance of clarifying these concepts for utilization by users, this study aimed to examine the concepts related to this filed.

Methods: The methodology used in this study was based on the Carnwell and Daly's critical review method. An extensive search was carried out on various databases and libraries. A critical and profound review was carried out on selected articles. Many wandering concepts were found. Identified concepts were classified into seven categories based on conceptual proximity. Existing definitions and evidence in relation to extracted concepts were criticized and synthesized. The definitional attributes for them were identified and a conceptual identity card was provided for each of the concepts.

Results: Thirty-seven concepts with the most relevance to the field of knowledge management were extracted. There was no clear boundary among them, and they wandered. To avoid more confusion, concepts were classified according to semantic relation. Eight categories were created; each category consisted of a mother concept and several other concepts with similarity and proximity to the meaning of the original concept. Their attributes have been identified, and finally, each of them was presented in the form of a conceptual identity card.

Conclusion: Through critically reviewing the literature in this field, we were able to identify the concepts and realize their attributes. In this way, we came to a new interpretation of the concepts. At the end of the study, we concluded that some of the concepts have not been properly defined and are not properly located in the knowledge management field; also their application is uncertain.

↑ What is “already known” in this topic:

There are numerous and complex concepts in the field of knowledge management that have not been clarified, and most of them are used incorrectly. For example, in many studies, the concept of knowledge management and knowledge translation are used interchangeably, and there is no distinct boundary among concepts.

→ What this article adds:

The identified concepts were wandering. To avoid more confusion, concepts were classified according to semantic relation. Eight categories were created, including a sentinel concept and several other neighbor concepts. Their attributes have been identified, and finally, each of them was presented in the form of a conceptual identity card.

Introduction

The organization in the age of knowledge is an organization that is based on the best available knowledge and information. To succeed in today's challenging organizational environment, organizations need to learn from past mistakes rather than repeating those mistakes. This process occurs through knowledge management ( 1 , 2 ). Knowledge management (KM) is important, especially for organizations that their successes depend on the production, use, and integration of knowledge by professionals and employees. Higher education institutions are made up of experts who are knowledgeable. KM is a new field in the academic environment, and many universities are actively involved in related activities in this field ( 3 ). Conferences and seminars are taking place at the national and international levels in this regard. In the field of education, due to the need to explore the power and intellectual capital available to share experiences, this area has been very much considered ( 4 ). All knowledge production organizations such as research, development centers and higher education institutions from colleges to universities are looking for new concepts in their favorite subject. They also help create knowledge through various programs, considered as "knowledge houses" ( 5 ). So, the knowledge of the professors flows to the students and new knowledge is produced. Information is created in various forms and sources such as books, articles, dissertations, reports, and more. Knowledge management helps these institutions to enhance the capacity to collect information and knowledge and apply it to problem-solving and decision making ( 6 ). Therefore, evidence shows that any academic institution is associated with knowledge. In these institutions, the information and knowledge gained in the scientific community's core area should be disseminated for further growth ( 5 ). But, there are challenges in this direction. Studies have demonstrated that knowledge created in educational institutions is not properly stored and obtained. Most of the time, knowledge created in that system remains unknown and is considered as gray literature ( 6 ). The academic environment is considered as the knowledge houses, but if the generated knowledge in that organization is not properly organized, it will minimize its usefulness and leads to repeat activities ( 7 ). Despite the importance of knowledge management for educational systems, there is still no awareness about its development by academics. There is a need to create a culture of sharing knowledge among professors, staff and students who are still afraid of losing their knowledge through exchange and dissemination ( 8 ). The use of information communication technology and the development of advanced skills in the training of professions for the purpose of participation, communication, acquisition, recording and dissemination of knowledge are used very poorly in universities. Therefore, they need to adopt a policy in this regard ( 7 ). New educational systems are market-oriented and are becoming entrepreneurs. They should be accountable to the academic governance system. Therefore, educational institutions and academics faced with global pressures, research, and interdisciplinary subjects. In the complexity of such as global education market, there is a need for a motivating environment ( 6 ).

We mentioned the importance of knowledge management in the educational system, as well as the existence of challenges in this direction, but although much research has been done in this regard, knowledge workers, those who are willing to do research or scientific activity in this area face difficulties. The main reason for this problem is that there are numerous and complex concepts in this area that have not been clarified, and most of them are used incorrectly. For example, in many studies and even by academics, the concept of knowledge management and knowledge translation are used interchangeably, and there is no distinct boundary among concepts. On the other hand, despite the multiplicity of concepts in this field, the research that has examined all of these concepts together has not been found. Considering the importance of clarifying these concepts for utilization by users, the first step in this direction is to identify and clarify concepts associated with knowledge management. Therefore, in this study, we intend to examine the concepts and definitions related to them through a critical review method, accordingly identify their attributes, and based on the identified attributes, concepts become clear.

The result of this study can help managers, policymakers, professors, students, and researchers who after us, intend to carry out research related to the field of knowledge management.

Our methodology was based on the critical review of the literature introduced by Carnwell and Daly. The following five steps were performed; 1- detremination the scope of the review, 2- identification relevant information resources, 3-literature review, 4-writing the review,5- application of the review results in the study ( 9 ).

The review scoop was theoretical research published in the research journals. An extensive search was carried out on various databases (google scholar, PubMed, Embase, Elsevier, Scopus, Iran Medex, SID, and online libraries and dictionaries). The main keywords in the search were: knowledge management concepts, knowledge management stages, knowledge management implementation, knowledge management in higher education, and knowledge management in medical education. As a result, numerous articles were found. To restrict the search results, we set the inclusion criteria and exclusion criteria. Inclusion criteria were the studies and books related to knowledge management concepts without time limitation. Non-academic research was the exclusion criteria. The articles were examined superficially. Then the primary screening was done on the titles. So, a summary of the articles was studied and those articles that were most closely related to the concepts of knowledge management were selected to study the full text. Priority in reading was based on their relevance to study objectives and literature with more conceptual richness. A critical review was carried on publications with the purpose of clarifying the boundary among concepts. Thirty-seven concepts that were involved in the KM process were extracted. Since there were many wandering concepts in this path, in order to avoid confusion, they were examined based on semantic proximity in separate categories. Each category included a mother concept and other related concepts to it. Then by synthesizing existing definitions and evidence about each of the concepts, we tried to identify the characteristics on which they are defined. Ultimately each of the concepts was presented in the new classification based on these characteristics.

There were many wandering concepts in the field of knowledge management, in order to avoid bewilderment; concepts were examined based on semantic proximity in separate categories. Each category included a mother concept with related concepts to it. By critique and comparing the definitions and evidence about each of the concepts, their attributes were identified. Finally, based on these features, a conceptual identity card for each concept was presented. Our result presented in nine categories: knowledge Generation (knowledge acquisition, knowledge selection, knowledge building, knowledge creation, knowledge capture), Knowledge processing( knowledge synthesis, knowledge integration, knowledge refinement, knowledge tailoring, knowledge customization)knowledge storage (knowledge assimilation, knowledge package, knowledge documentation, knowledge indexing), Knowledge transfer( knowledge sharing, knowledge exchange, knowledge dissemination, knowledge publication), Knowledge capitalization( knowledge commercialization, knowledge valorization), Knowledge brokering, Knowledge utilization(, knowledge adoption, knowledge adaptation, knowledge reuse), Knowledge translation, and Knowledge management.

In the following, the conceptual identity of each of the concepts, which includes the specific features about that concept, is introduced.

Knowledge Generation : Knowledge acquisition, knowledge capture, knowledge selection, knowledge creation, knowledge building.

Knowledge acquisition attributes

Purpose: The purpose of knowledge acquisition is to enhance the organizations' competitive edge through increasing an organization’s operational knowledge base ( 10 ).

Source of obtaining knowledge: The source of obtaining knowledge is internal and external sources ( 10 ).

Type of acquired Knowledge: Type of acquired knowledge can be either tacit or explicit ( 10 ).

Activities: Activities related to knowledge acquisition are identification of knowledge, obtaining the identified knowledge, transferring the knowledge for immediately using or internalization ( 11 ).

Key point: Knowledge acquired can either be tacit or explicit but must add value to the organization ( 10 ).

Knowledge selection attributes

Purpose: The purpose of knowledge selection is Identification the knowledge according to organizational needs in internal sources, Provide knowledge at the appropriate place and by the appropriate form ( 12 ).

Source of obtaining knowledge: Knowledge is obtained from internal sources ( 12 ).

Activities: Knowledge selection activities include the following: identification of knowledge from internal sources, obtaining the identified knowledge from internal sources, transfer the knowledge for immediately using or internalization ( 12 )

Key point: Knowledge selection is the opposite point of knowledge acquisition ( 12 ).

Knowledge capturing attributes

Purpose: The purpose of knowledge capture is to maintain knowledge in order to organizational performance improvement, ensure that knowledge available is stored for future reference ( 13 ).

Form: Knowledge captured in the form of databases or manuals ( 13 ).

Knowledge creation attributes

Context: Knowledge creation occurs through the inference or discovery from knowledge sources ( 12 ).

Purpose: Creating or producing knowledge helps organizations gain a competitive advantage by providing valuable, rare, and inimitable resources ( 14 ). Utilization of complex and discontinuous events and phenomena to Confronting recognized organizational problems ( 15 ).

Activities: Knowledge selection activities include the following: control the organizational knowledge, Control the external environment, Creation knowledge from the existing basic knowledge, Transfer created knowledge for externalization or internalization ( 12 ).

Knowledge creation place: Knowledge is produced in the Research community, Professional Councils, Ministries and governmental organizational, Transfer and innovation centers, Science communities ( 16 ).

Form: Some scientists have defined knowledge creation as a process, output, and outcome ( 15 , 17 ).

Knowl edge building attributes

Context: The term knowledge building first appeared in the learning sciences literature ( 18 ).

Purpose: The purpose of knowledge creation is the creation or modification of public knowledge—knowledge that lives ‘in the world’ and is available to be worked on and used by other people. These pursuits should advance the current understanding of individuals within a group, at a level beyond their initial knowledge level, and should be directed towards advancing the understanding of what is known about that topic or idea ( 19 ).

Steps: Knowledge building consists of the following steps: creation, testing, and improvement of conceptual artifacts ( 19 ).

Requirements: It encompasses the foundational learning, sub-skills, and socio-cognitive dynamics pursued in other approaches, along with the additional benefit of movement along the trajectory to mature education ( 20 ).

Path: Knowledge building can be considered as deep constructivism that involves making a collective inquiry into a specific topic and coming to a deeper understanding through interactive questioning, dialogue, and continuous improvement of ideas. Ideas are thus the medium of operation in KB environments ( 20 ).

Key point: Knowledge building projects focus on understanding rather than on accomplishing tasks, and on collaboration rather than on controversy ( 20 ).

Knowledge processing : Knowledge filtering, knowledge synthesis, knowledge integration, knowledge refinement, knowledge customization.

Knowledge processing attributes

Context: Knowledge processing is a significant factor contributing to socioeconomic sustainability ( 21 ).It is a central problem of Artificial Intelligence ( 22 ).

Purpose: The purpose of Knowledge processing is to understand the relationship among data, information and knowledge and create knowledge structures ( 23 ).

Method: The knowledge processing method is Transformation of data into knowledge, changing the form of knowledge representation, deriving new knowledge from a given knowledge ( 23 ).

Steps: Knowledge processing consists of the following steps: Information storing, information retrieving, and information transferring ( 21 ).

Key point: Knowledge processing is known as the most important factor affecting economic and social sustainability, Derive value from knowledge processing ( 23 ).

Knowledge filtering attributes

Context: Knowledge filtering can be used to facilitate assimilation. Filtering tries to get the right knowledge to the right person at the right time) 24).

Purpose: Filtering is a tool to help people find the most valuable information so that the limited time spent on reading/listening/viewing can be spent on the most interesting and valuable documents. Filters are also used to organize and structure information ( 25 ).

Steps: Knowledge filtering consists of the following steps: Evaluate documents, and puts documents, which are interesting into its structured information database) 25).

Method: The knowledge filtering method is Manual filtering by people, using intelligent agents ( 24 ).

Main actors: Computer-based Approaches, publishers, editors, journalists ( 25 ).

Knowledge synthesis attributes

Context: Knowledge synthesis is the contextualization and integration of research findings of individual research studies within the larger body of knowledge on the topic ( 26 ).

Purpose: Most syntheses are conducted either for the purpose of knowledge support or for decision support ( 27 ).

Steps: Knowledge synthesis consists of the following steps: Stating the objectives of the research, Defining eligibility criteria for studies to be included, Identifying (all) potentially eligible studies, Applying eligibility criteria, Assembling the complete data set feasible including data extraction, quality appraisal of included studies, Analyzing this data set, and Preparing a structured report ( 28 , 29 ).

Method: Knowledge synthesis methods are Systematic review, Realist syntheses, Narrative syntheses, Meta-analyses, Meta-syntheses, Practice guidelines, Consensus conference, or expert panel ( 30 ).

Key point: A synthesis must be reproducible and transparent in its methods ( 26 ).

Knowledge integration attributes

Context: The integration of knowledge is the process of incorporating new information into a body of existing knowledge ( 31 ).

Purpose: The purpose of knowledge integration is to determine how new and existing knowledge interacts and how existing knowledge should be modified to accommodate the new information ( 31 ).

Steps: Knowledge integration consists of the following steps: Dynamic process of linking, connecting, distinguishing, organizing, and structuring ideas about scientific phenomena ( 32 ).

Knowledge refinement attributes

Context: The knowledge refinement process is implemented as part of an organization’s knowledge management efforts ( 33 ).

Purpose: The purpose of knowledge refinement is to optimize content quality ( 33 , 34 ).

Steps: Knowledge refinement refers to the process of evaluating, analyzing and optimizing the knowledge object to be stored in a repository ( 35 , 36 )

Key point: Knowledge refinement effectiveness is defined as the degree to which the refinement process produces quality knowledge ( 37 ). Knowledge refinement process should positively enhance the quality of refined knowledge ( 37 ).

Knowledge customization attributes

Context: Product customization is becoming an increasingly important strategic initiative in knowledge management. Product customization impacts the knowledge management processes of knowledge acquisition, sharing, and transfer ( 38 ).

Purpose: The purpose of customization is configuring a product or service to a buyer’s specifications ( 39 ). The relationships among sales, R&D, and production functions have to strengthen and the KM system has to support such a need ( 38 ).

Steps: Knowledge customization consists of the following steps: Collecting information about the customer, choosing options and/or creating new content, deliberately tailors content ( 40 ).

Key point: Customization emphasizes the user’s role in specifying content; customization is a highly user-driven process of tailoring ( 41 ).

Knowl edge transfer attributes

Context: The transfer of knowledge in the broadest sense refers to the flow of knowledge between and within organizations ( 42 ).

Purpose: The purpose of knowledge transfer is: decision-making, changing individual or organizational behavior, developing policies, problem-solving ( 43 ).

Perspectives about Knowledge Transfer: Health perspective, educational perspective, management perspective.

Health perspective: Use of scientific research findings to improve professional performance ( 44 ).

Educational perspective: Using generated knowledge in a specific context for another context ( 45 ).

Management perspective: utilization of the new knowledge for organizational behaviors ( 46 ).

Form: Knowledge transfer can be done in the form of formal and informal, planned, and unplanned ( 46 ). Planned and unplanned: Knowledge transfer as a process where knowledge is transmitted from one person to another in the form of planned or natural ( 47 ).

Formal and informal: Knowledge transfer as an informal way through networks and social interactions in the workplace or formal way in an organization ( 47 , 48 )

Level: Knowledge transfer is a macro process, at the organizational level ( 42 ).

Steps: Knowledge transfer consists of the following steps: SECI: Socialization, Externalization, Combination, And Internalization ( 49 ).

Areas: Knowledge transfer areas include: Transfer of research findings ( 50 ). Technology transfer ( 51 ). Transfer of learning, Organizational transfer. ( 45 ).

Key point: The concept of knowledge transfer is at the macro level, where knowledge is spreading across sectors, units, or subsets of an organization ( 42 ).

Knowledge sharing attributes

Context: Knowledge sharing is an activity that involves transferring or disseminating knowledge from a person, group, or organization to another.

Purpose: The purpose of knowledge sharing is discovering tools for accessing knowledge inside and outside of organizations with a view to creating more effective management and organizational system ( 52 ).

Level: Knowledge sharing can be At the Individual level and micro ( 53 ). Among researchers, policymakers, service providers, stakeholders ( 54 ).

Activities: Sharing of knowledge is entirely conscious, with a person's desire, without any obligation ( 53 ).

Place for sharing Sharing of knowledge occurs at Conferences, social media, Media relation, Scholarly collaboration networks, Journal publication ( 55 )

Direction: It is a Mono directional process: A person's knowledge transforms into a form that can be understood, absorbed, and used by others. Bidirectional: Share information, ideas, suggestions and related organizational expertise with each other ( 56 ).

Key point: Common purpose and shared experiences between individuals, and Communication with others are taking place ( 56 ).

Kn owledge exchange attributes

Context: In the exchange of knowledge, collaborative problem solving between researchers and decision-makers takes place ( 54 ).

Purpose: The exchange of knowledge is to increase the effectiveness of networks and teams in complex environments ( 54 ). The exchange of knowledge to create new knowledge ( 57 ).

Form: Knowledge exchange is an active process: Researchers make knowledge available to users, and users also transfer knowledge to researchers. It Includes knowledge sharing (what employees give to others) and knowledge search (employees are seeking knowledge from others). ( 57 ).

Direction: There are bidirectional relations between researchers or knowledge producers and users.)58).

Key point: The interactions between researchers and decision-makers take place ( 57 ).

Knowledge dissemination attributes

Means for dissemination: Knowledge can be disseminated through articles, journals, conference lectures and other outputs ( 59 ).

The type of dissemination: Dissemination of knowledge is in the form of Knowledge, interventions and existing or recent methods ( 59 )

Direction: It is mono-directional, from the top to the bottom and from the expert ( 59 ).

Form: Knowledge dissemination is a planned process ( 59 ).

Knowledge publication attribute

Con text: One of the major academic duties to share their findings, and to interact with their peers and the general populace, via literal publication ( 60 ).

Purpose: The purpose of the publication is the Making-public of new knowledge ( 60 ).

Steps: Knowledge publication includes the following steps: Find the right journal, prepare the paper, and submit the paper ( 55 ).

Form: The publication of knowledge is in the form of Letter, rapid or short communications, Review papers, Full articles, Research elements (data, software, methods, Citable articles, in brief) ( 55 )

Key point: The publication is related to academic journals ( 55 ).

Knowledge Brokering

Knowledge brokering attributes.

Context: Knowledge brokering is one of the human forces behind knowledge transfer. It is a dynamic activity that goes well beyond the standard notion of transfer as a collection of activities that helps move information from a source to a recipient ( 61 ).

Purpose: Brokering focuses on identifying and bringing together people interested in an issue, people who can help each other develop evidence-based solutions. It helps build relationships and networks for sharing existing research and ideas and stimulating new work.” ( 62 ). Knowledge brokering encompasses a wide range of processes and practices that aim at establishing relationships and facilitating effective knowledge sharing and exchange ( 61 ).

Form: Knowledge brokering takes place as either formal or informal activities ( 61 ).

Type: Types of knowledge brokers are: Information Intermediary (Help Access to knowledge), Knowledge Intermediary (Help Understand and apply the knowledge), Knowledge Brokering (Help use of knowledge in decision making), Innovation Brokering (Changing Context). ( 61 )

Activities: Knowledge brokering activities are: uncovering the needs, ideas, activities, and processes of different knowledge environments in order to identify the best research, practices and tools that research partners need to capture, transfer, exchange and collaborate around knowledge ( 61 ).

Key point: It engages with obstacles that block the transfer of research into practice ( 61 ).

Knowledge storing: Knowledge assimilation, knowledge package, knowledge indexing, knowledge documentation.

Knowledge storage attributes

Context: Knowledge can be viewed as an item to be stored for future usage ( 34 ).

Purpose: Knowledge storage is In order to facilitate the assimilation of knowledge ( 63 ).

Type: Knowledge is stored in the form of individual and organizational knowledge, soft or hardstyle recording and retention ( 49 , 64 )

Form: Knowledge store as the form of documents, rules, cases, and diagrams ( 65 )

Method: Technical infrastructure such as modern informational hardware and software, human processes are necessary for storing knowledge ( 49 ).

Steps: Knowledge storage steps are: identify the knowledge in an organization, convert the identified knowledge to code, and index the identified knowledge for later retrieval ( 49 , 64 ).

Knowledge assimilation attributes

Context: A critical aspect of knowledge management is that of assimilation ( 66 , 67 ).

Purpose: 1. To take in and incorporate as one’s own; absorb 2. To bring into conformity with the customs, attitudes, etc. of a group 3. To convert to substances suitable for incorporation.

Steps: Knowledge assimilation steps are: Storage, massaged, organized, integrate, filtered, navigate ( 66 , 67 ).

Key point: Knowledge can be captured or created, but until it is assimilated it is not likely to receive extensive use ( 64 ).

Knowledge package attributes

Purposes : The purpose of the knowledge package is culling, cleaning and polishing, structuring, formatting, and/or indexing documents against a classification scheme ( 68 ).

Activities: Knowledge package activities include Authoring knowledge content, codifying knowledge into “knowledge objects” by adding context, developing local knowledge into “boundary objects” by deleting context, filtering and pruning content, and developing classification schemes ( 68 ).

Knowledge indexing attributes

Context: Knowledge index is to provide a summary about subject content; Indexing activity should be done as a pre analyzing process ( 69 ).

Purpose: The purpose of indexing is: organizing the Information in order to effectively use of information ( 70 ).

Steps: Knowledge index steps are: Review of documentation and establishment of subject matter, identify the core concept in documents, Referencing selected concepts by the terms of the indexing language ( 71 ).

Main actors: Librarian and intermediaries are the main actors for indexing of knowledge ( 71 ).

Knowle dge documentation attributes

Context: Preservation and documentation are ways to ensure the future existence of indigenous knowledge, which today is under threat of extinction ( 72 ). Facilitating re¬trieval knowledge is to take place from an organized data set (WIPO, 2016).

Purpose: The aim of documentation is to ensure the maintenance, use, and development of knowledge by present and future generations of peoples and communities ( 73 ).

Steps: Knowledge documentation steps are Knowledge identification, Knowledge fixation, and Knowledge classification ( 73 ).

Methods: The methods for documentation are Paper files, digital databases, archives, or libraries ( 73 ).

Main actors: Librarian and information professionals are the main actors for knowledge documentation ( 74 ).

Knowledge transfer : Knowledge sharing, knowledge exchange, knowledge dissemination, knowledge publication.

Knowledge capitalization : Knowledge commercialization, knowledge valorization.

Knowledge capitalization attributes

Context: Knowledge capitalization is the most important part of KM ( 75 ).

Purpose: It aims at building organizational memories that represent several views on expertise or activity (75.)

Activities: Capitalization is the process by which members of the community can identify, locate, model, store, access, use/reuse, share, update, and know-how to communicate the knowledge of the community ( 75 ).

Steps: Knowledge capitalization steps are: Knowledge extraction and formalization, Knowledge sharing, Knowledge reuse and appropriation, Memory evolution ( 75 ).

Form: Knowledge capitalization happens in the form of: Knowledge locate (identifying, localizing, characterizing, mapping, estimating, prioritizing), knowledge preserve (acquiring, modeling, formalizing, conserving), knowledge enhanced (accessing, disseminating, sharing, using more effectively, combining, and creating), knowledge actualized (appraising, updating, standardizing, enriching, knowledge managed (elaborate a vision: promote, inform, train, facilitate, organize, coordinate, encourage, motivate, measure, and follow up) ( 76 ).

Knowledge commercialization attributes

Context: Commercialization of knowledge is the Third mission of the university, Transfer of knowledge to industry ( 77 ).

Purpose: The purpose of commercialization is: Decrease independency to the public sector, Make commercial profit ( 78 ).

Direction: At the commercialization level Corporation between education and industry, dynamic improvement of production, and the economy system is taking place ( 78 ).

Steps: Knowledge commercialization steps include flowing: Idea generation, Idea evaluation, Idea development, Commercial analysis of the product, Market assessing, Commercialization ( 79 ).

Key point: Commercialization is not a linear process; it is a complex process ( 79 ).

Knowledge valorization attributes

Context: Valorization is a word of French origin translated as a “surplus value”. Valorization was framed in the context of the discourse of academic capitalism ( 80 ).

Purpose: The purpose of valorization is to transfer knowledge from one part to another for economic benefit” ( 81 ).

Path: The process of knowledge valorization is a long route that starts at universities ( 81 ). Valorization not only contributes to the availability of the results of academic research beyond academia but also involves the co-production of knowledge by academics and representatives of business ( 80 ).

Types: Types of valorization are societal (social) and economic ( 81 ).

Main actors: “Valorization is a cooperation between higher education institutions, government, and business players to agree on targeted investments in a number of key areas of regional innovation” ( 82 ).

Steps: Knowledge valorization steps are: Knowledge acquisition; amassing the relevant internal and external information required for the transfer of knowledge is collected and quickly deploying this information to its potential users, Knowledge processing; assess the market value of the relevant research and package the knowledge with market potential for business requirements, Knowledge dissemination; delivering of the knowledge package to business and assisting in the technology deployment ( 83 ).

Areas: Knowledge valorization areas include: education, Cooperation, contract research, R&D cooperation, and knowledge, and technology transfer, “entrepreneurship, “the production of successful high-tech start-ups” ( 84 ).

Key point: Knowledge-Economy Index which takes into account whether the environment is conducive for knowledge to be used effectively for economic development and Knowledge Index which measures a country’s ability to generate, adapt and diffuse knowledge ( 52 ). Valorization is broader than commercialization that is focused primarily on making a commercial profit ( 80 ).

Knowledge utilization: Knowledge adoption, knowledge adaptation, knowledge reuse.

Knowledge adoption attributes

Context: The adoption of knowledge is carried out in the field of innovation ( 46 ).

Purpose: Adoption is taking place in order to decision making about accept or refuse of innovation ( 46 ).

Steps: Knowledge adoption steps include: awareness about new knowledge, attitude formation, and decision about accept or refuse of innovation or new knowledge, implement a new idea or confirm accepted decision ( 46 ).

Key point: User motivation for use or rejection, resistance rate about new knowledge, consistency to the policy is determining factors in the knowledge adoption process ( 85 ).

Knowledge adaptation attributes

Context: The adaptation of knowledge is related to the results of the research, and this step is critical to the success of the knowledge transfer process ( 86 , 87 ).

Purpose: The goal is to make the results accessible and understandable by the users ( 86 , 87 ).

Key point: This step affects the user's decision to accept the knowledge generated by the researchers. Also, the availability of research results does not necessarily guarantee acceptance and use by users. Many authors have argued that the form of presentation of research results can be a motivation or obstacle to accepting knowledge in the educational community ( 87 ).

Knowledge reuse attributes

Context: It is a central issue for companies in order to avoid reinventing the wheel over and over again ( 89 ). The effective reuse of knowledge is arguably a more frequent organizational concern and one that is clearly related to organizational effectiveness ( 89 ).

Purpose: Knowledge reuse is taking place for sharing best practices or helping others solve common technical problems ( 88 ).

Steps: Knowledge reuse steps include: Capturing or documenting knowledge, packaging knowledge for reuse, Distributing or disseminating knowledge (providing people with access to it), and Reusing knowledge ( 35 ).

Activities: Knowledge reuse activities are followings: recall (that information has been stored, in what location, under what index or classification scheme) and recognition (that the information meets the users’ needs), as well as actually applying the knowledge ( 90 ).

Agent: There are three major roles in the knowledge reuse process: knowledge producer—the originator and documenter of knowledge, who records explicit knowledge or makes tacit knowledge explicit, knowledge intermediary—who prepares knowledge for reuse by eliciting it, indexing it, summarizing it, sanitizing it, packaging it, and who performs various roles in dissemination and facilitation, and knowledge consumer—the knowledge reuser, who retrieves the knowledge content and applies it in some way ( 91 ).

Key point: Successful knowledge transfer or reuse requires a complete solution. It is not just a matter of providing access to information technology and repositories. It also means careful attention to the design of incentives for contributing to and using repositories and to the roles of intermediaries to develop and maintain repositories and to facilitate the process of reuse ( 89 ).

Knowledge translation attributes

Context: The translation is the process of putting research findings and the products of research into the hands of key audiences. It is the art of weaving together processes of research and practice ( 92 ).

Purpose: Knowledge Translation is impact-oriented- the overarching goal of KT is to improve systems, practices, and ultimately lead to better outcomes ( 93 ).

Activity: Knowledge Translation includes multiple activities- Researchers need to go beyond mere dissemination and publication of results to multiple engagements to effect knowledge uptake ( 93 ).

Direction: Knowledge translation is a nonlinear process- it is also a complex process with multiple players, it also needs multidirectional communications ( 93 ).

Agent: Knowledge translation is an interactive process- the interactions between knowledge producers and knowledge users should be continuous. KT requires ongoing collaborations among relevant parties- collaboration, relationships, and trust among parties ( 92 ).

Steps: Knowledge translation includes all steps between the creation of new knowledge and its application ( 93 ).

Key point: It emphasizes the use of research-generated knowledge ( 93 ).

Knowledge management attributes

Context: Knowledge management is the process of transferring information and intellectual assets to a stable value. And it is related to making knowledge suitable for the correct processor, such as a human being or a computer, at the right time and at the right cost ( 94 ).

Purpose: The purpose of knowledge management is to create the knowledge that can be used by more than one person, for example, for the organization as a whole, or sharing knowledge between its members ( 94 ). Help to promote the use and sharing of data and information in decision making ( 95 ).

Activity: Knowledge management involves planning, organizing, and controlling individuals, processes, and systems to ensure that knowledge capital is promoted and applied effectively ( 33 ).

Type: Knowledge management has multidisciplinary nature, which includes: organizational science, cognitive science, information technology, linguistics, technical writing, ethnology and sociology, teaching, Communication studies, collaborative technologies such as computer-based collaborative activities, intranets, extranets, portals, and other network technologies ( 96 ).

Path: Under the knowledge management, the information becomes applicable to the knowledge and is applicable to the people who can use it ( 97 ).

Steps: Knowledge management steps involve: obtaining, organizing, managing, and disseminating knowledge in an organization in order to perform tasks faster, reuse best practices, and reduce costs twice ( 49 ). The process of finding, selecting, organizing, importing, and providing information in order to help raise the understanding of employees in a particular area ( 98 ).

Form: Knowledge management has two main aspects: knowledge as an obvious concern that reflects on organizational strategies, policies, and practices. On the other hand, it takes into account the relationships between intellectual capital (both apparently recorded and implicit in the form of personal knowledge) and Positive business results ( 99 ).

Studies have examined one or a few concepts in the field of knowledge management. Through this study, we were able to investigate all of the concepts related to knowledge management as far as possible. By criticizing and comparing the evidence and definitions relating to them, based on semantic proximity, we divided them into related categories and, clarify the boundary among them. We realized that many concepts had not found their appropriate place in the KM process, and there are no proper definitions of them. Therefore, it is necessary to redefine some of the concepts and the correct placement in the structure and operation of knowledge management. We can use the results of this study as the basis and the first step in developing a comprehensive model that includes all the concepts related to knowledge management and for determining the relationship among them and with other educational development concepts.

This study aimed to clarify the concepts in the knowledge management area. Through critically reviewing the literature in this field, we were able to identify the concepts and realize their attributes. Therefore, we came to a new interpretation of the concepts. At the end of the study, we concluded that some of the concepts have not been properly defined and are not properly located in the knowledge management field, and their application is uncertain. Regarding the identified gaps, there is a need to comprehensively study that consider all of these in the direction of knowledge management, show their application in a comprehensive model and, if necessary, redefined them, such as study can complement our work.

Acknowledgment

This article is a part of the dissertation entitled Educational Development with Approach on Knowledge Management. The authors would like to appreciate everyone who assisted them in this research.

Conflict of Interests

The authors declare that they have no competing interests.

Cite this article as: Yazdani Sh, Bayazidi S, Mafi AA. The current understanding of knowledge management concepts: A critical review. Med J Islam Repub Iran. 2020 (28 p);34:127. https://doi.org/10.34171/mjiri.34.127

Conflicts of Interest: None declared

Funding: None

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A Literature Review of Knowledge Management: History, Concept, and Process

The rapid development of science and technology today, making organizational management to face challenges and keep innovating. One of the areas in management field that develops in its implementation is knowledge management. In this paper, we will describe the knowledge management which includes history, concept, and process. Understanding knowledge management can be concluded as the management of intellectual property owned by members of the organization and will be utilized for achieving competitive advantage for the organization. The principal process that will be the conclusion in this article is creating knowledge, processes knowledge, distributing knowledge, and using knowledge.

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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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See an example

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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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.

Adomako S, Tran MD (2022) Local embeddedness, and corporate social performance: the mediating role of social innovation orientation. Corp Soc Responsib Environ Manag 29(2):329–338. https://doi.org/10.1002/csr.2203

Article   Google Scholar  

Agarwal N, Chakrabarti R, Brem A, Bocken N (2018) Market driving at bottom of the pyramid (BoP): an analysis of social enterprises from the healthcare sector. J Bus Res 86(5):234–244. https://doi.org/10.1016/j.jbusres.2017.07.001

Akter S, Wamba SF, Gunasekaran A, Dubey R, Childe SJ (2016) How to improve firm performance using big data analytics capability and business strategy alignment? Int J Prod Econ 182:113–131. https://doi.org/10.1016/j.ijpe.2016.08.018

Al-Khatib AW (2022) Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity. Bus Process Manag J 28(5/6):1446–1471. https://doi.org/10.1108/BPMJ-07-2022-0332

Article   ADS   Google Scholar  

Allal-Cherif O, Guijarro-Garcia M, Ulrich K (2022) Fostering sustainable growth in aeronautics: open social innovation, multifunctional team management, and collaborative governance. Technol Forecast Soc Change 174. https://doi.org/10.1016/j.techfore.2021.121269

Alnuaimi BK, Khan M, Ajmal MM (2021) The role of big data analytics capabilities in greening e-procurement: a higher order PLS-SEM analysis. Technol Forecast Soc Change 169. https://doi.org/10.1016/j.techfore.2021.120808

Andriopoulos C, Lewis MW (2009) Exploitation-exploration tensions and organizational ambidexterity: managing paradoxes of innovation. Organ Sci 20(4):696–717. https://doi.org/10.1287/orsc.1080.0406

Argote L, Hora M (2017) Organizational learning and management of technology. Prod Oper Manag 26(4):579–590. https://doi.org/10.1111/poms.12667

Arias-Pérez J, Velez-Ocampo J, Cepeda-Cardona J (2021) Strategic orientation toward digitalization to improve innovation capability: why knowledge acquisition and exploitation through external embeddedness matter. J Knowl Manag 25(5):1319–1335. https://doi.org/10.1108/JKM-03-2020-0231

Ashaari MA, Singh KSD, Abbasi GA, Amran A, Liebana-Cabanillas FJ (2021) Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: a multi-analytical SEM & ANN perspective. Technol Forecast Soc Change 173. https://doi.org/10.1016/j.techfore.2021.121119

Awan U, Bhatti SH, Shamim S, Khan Z, Akhter P, Balta M (2021) The role of big data analytics in manufacturing agility and performance: moderation-mediation analysis of organizational creativity and of the involvement of customers as data analysts. Br J Manag 33(3):1200–1220. https://doi.org/10.1111/1467-8551.12549

Bag S, Wood LC, Xu CL, Dhamija P, Kayikci Y (2020) Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resour Conserv Recycling 153. https://doi.org/10.1016/j.resconrec.2019.104559

Barton D, Court D (2012) Making advanced analytics work for you. Harv Bus Rev 90(10):78–83

PubMed   Google Scholar  

Batko KM (2023) Digital social innovation based on Big Data Analytics for health and well-being of society. J Big Data 10:1–34. https://doi.org/10.1186/s40537-023-00846-w

Beck R, Pahlke I, Seebach C (2014) Knowledge exchange and symbolic action in social media-enabled electronic networks of practice: a multilevel perspective on knowledge seekers and contributors. MIS Q 38(4):1245–1270. https://doi.org/10.25300/MISQ/2014/38.4.14

Benitez J, Castillo A, Llorens J, Braojos J (2018) IT-enabled knowledge ambidexterity and innovation performance in small U.S. firms: the moderator role of social media capability. Inf Manag 55(1):131–143. https://doi.org/10.1016/j.im.2017.09.004

Bhatti SH, Ahmed A, Ferraris A, Hussain WMHW, Wamba SF (2022) Big data analytics capabilities and MSME innovation and performance: a double mediation model of digital platform and network capabilities. Ann Oper Res https://doi.org/10.1007/s10479-022-05002-w

Bollen KA (1989) Structural equations with latent variables. John Wiley & Sons, New York

Bonina C, López-Berzosa D, Scarlata M (2021) Social, commercial, or both?An exploratory study of the identity orientation of digital social innovations. Inf Syst J 31(5):695–716. https://doi.org/10.1111/isj.12290

Calic G, Ghasemaghaei M (2021) Big data for social benefits: innovation as a mediator of the relationship between big data and corporate social performance. J Bus Res 131:391–401. https://doi.org/10.1016/j.jbusres.2020.11.003

Carayannis EG, Grigoroudis E, Stamati D, Valvi T (2020) Social business model innovation: a quadruple/quintuple helix-based social innovation ecosystem. IEEE Trans Eng Manag 68(1):235–248. https://doi.org/10.1109/tem.2019.2914408

Castillo A, Benitez J, Llorens J, Braojos J (2021) Impact of social media on the firm’s knowledge exploration and knowledge exploitation: the role of business analytics talent. J Assoc Inf Syst 22(5):1472–1508. https://doi.org/10.17705/1jais.00700

Cegarra-Navarro JG, Sanchez-Vidal ME, Cegarra-Leiva D (2011) Balancing exploration and exploitation of knowledge through an unlearning context An empirical investigation in SMEs. Manag Decis 48(7-8):1099–1119. https://doi.org/10.1108/00251741111151163

Chen Y, Wang Y, Nevo S, Benitez J, Kou G (2017) Improving strategic flexibility with information technologies: insights for firm performance in an emerging economy. J Inf Technol 32(1):10–25. https://doi.org/10.1057/jit.2015.26

Cheng CCJ, Sheu C (2023) Social media analytics and product innovation: mediating effects of knowledge exploration and exploitation competence. Int J Oper Prod Manag https://doi.org/10.1108/IJOPM-08-2022-0537

Ciampi F, Demi S, Magrini A, Marzi G, Papa A (2021) Exploring the impact of big data analytics capabilities on business model innovation: the mediating role of entrepreneurial orientation. J Bus Res 123:1–13. https://doi.org/10.1016/j.jbusres.2020.09.023

Conboy K, Dennehy D, O’Connor M (2020) ‘Big time’: an examination of temporal complexity and business value in analytics. Inf Manag 57(1). https://doi.org/10.1016/j.im.2018.05.010

Crossan MM, Lane HW, White RE (1999) An organizational learning framework: from intuition to institution. Acad Manag Rev 24(3):522–537. https://doi.org/10.5465/amr.1999.2202135

Deng C, Li H, Wang Y, Zhu R (2024) The double-edged sword in the digitalization of human resource management: Person-environment fit perspective. J Business Res 180:114738. https://doi.org/10.1016/j.jbusres.2024.114738

Desmarchelier B, Djellal F, Gallouj F (2020) Mapping social innovation networks: Knowledge intensive social services as systems builders. Technol Forecast Soc Change 157. https://doi.org/10.1016/j.techfore.2020.120068

Dezi L, Ferraris A, Papa A, Vrontis D (2021) The role of external embeddedness and knowledge management as antecedents of ambidexterity and performances in Italian SMEs. IEEE Trans Eng Manag 68(2):360–369. https://doi.org/10.1109/TEM.2019.2916378

Dubey R, Gunasekaran A, Childe SJ, Roubaud D, Wamba SF, Giannakis M, Foropon CR (2019) Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. Int J Prod Econ 210:120–136. https://doi.org/10.1016/j.ijpe.2019.01.023

Ferraris A, Mazzoleni A, Devalle A, Couturier J (2019) Big data analytics capabilities and knowledge management: impact on firm performance. Manag Decis 57(8):1923–1936. https://doi.org/10.1108/MD-07-2018-0825

Ferreira J, Coelho A, Moutinho L (2020) Strategic alliances, exploration and exploitation and their impact on innovation and new product development: the effect of knowledge sharing. Manag Decis 59(3):524–567. https://doi.org/10.1108/MD-09-2019-1239

Fleming L, Sorenson O (2001) Technology as a complex adaptive system: evidence from patent data. Res Policy 30(7):1019–1039. https://doi.org/10.1016/S0048-7333(00)00135-9

Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50. https://doi.org/10.1177/002224378101800104

Fursov K, Linton J (2022) Social innovation: integrating product and user innovation. Technol Forecast Soc Change 174. https://doi.org/10.1016/j.techfore.2021.121224

Galbraith JR (1974) Organization design: an information processing view. Interfaces 4(3):28–36. https://www.jstor.org/stable/25059090

Gasparin M, Green W, Lilley S, Quinn M, Saren M, Schinckus C (2021) Business as unusual: a business model for social innovation. J Bus Res 125:698–709. https://doi.org/10.1016/j.jbusres.2020.01.034

Gebauer H, Fleisch E, Lamprecht C, Wortmann F (2020) Growth paths for overcoming the digitalization paradox. Bus Horiz 63(3):313–323. https://doi.org/10.1016/j.bushor.2020.01.005

Ghasemaghaei M, Calic G (2019) Does big data enhance firm innovation competency? The mediating role of data-driven insights. J Bus Res 104:69–84. https://doi.org/10.1016/j.jbusres.2019.07.006

Guerrero M, Urbano D (2020) Institutional conditions and social innovations in emerging economies: insights from Mexican enterprises’ initiatives for protecting/preventing the effect of violent events. J Technol Transf 45:929–957. https://doi.org/10.1007/s10961-020-09783-9

Gupta A, Smith K, Shalley C (2006) The interplay between exploration and exploitation. Acad Manag J 49(4):693–706. https://doi.org/10.5465/amj.2006.22083026

Gupta M, George JF (2016) Toward the development of a big data analytics capability. Inf Manag 53(8):1049–1064. https://doi.org/10.1016/j.im.2016.07.004

Gupta S, Kumar S, Kamboj S, Bhushan B, Luo ZW (2019) Impact of IS agility and HR systems on job satisfaction: an organizational information processing theory perspective. J Knowl Manag 23(9):1782–1805. https://doi.org/10.1108/JKM-07-2018-0466

Harman HH (1976) Modern factor analysis. University of Chicago Press

He W, Wu H, Yan G, Akula V, Shen JC (2015) A novel social media competitive analytics framework with sentiment benchmarks. Inf Manag 52(7):801–812. https://doi.org/10.1016/j.im.2015.04.006

Herrera MEB (2015) Creating competitive advantage by institutionalizing corporate social innovation. J Bus Res 68(7):1468–1474. https://doi.org/10.1016/j.jbusres.2015.01.036

Ho JY, Yoon S (2022) Ambiguous roles of intermediaries in social entrepreneurship: the case of social innovation system in South Korea. Technol Forecast Soc Change 175. https://doi.org/10.1016/j.techfore.2021.121324

Jarjabka Á, Sipos N, Kuráth G (2024) Quo vadis higher education? Post-pandemic success digital competencies of the higher educators- a Hungarian university case and actions. Humanit Soc Sci Commun 11(1). https://doi.org/10.1057/s41599-024-02809-9

Javed S, Rashidin MDS, Xiao Y (2022) Investigating the impact of digital influencers on consumer decision-making and content outreach: using dual AISAS model. Econ Res Ekonomska Istraživanja 35(1):1183–1210. https://doi.org/10.1080/1331677X.2021.1960578

Kiron D, Ferguson RB, Prentice PK (2013) From value to vision: reimagining the possible with data analytics. MIT Sloan. Manag Rev 54(3):1–19

Google Scholar  

Koryak O, Lockett, Hayton JC, Nicolaou N, Mole K (2018) Disentangling the antecedents of ambidexterity: exploration and exploitation. Res Policy 47(2):413–427. https://doi.org/10.1016/j.respol.2017.12.003

Krishnamurthy R, Desouza KC (2014) Big data analytics: the case of social security administration. Inf Polity 19(3):165–178. https://doi.org/10.3233/IP-140337

Krlev G, Bund E, Mildenberger G (2014) Measuring What Matters—Indicators of Social Innovativeness on the National Level. Inf Syst Manag 31(3):200–224. https://doi.org/10.1080/10580530.2014.923265

Lavalle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N (2011) Big data, analytics and the path from insights to value. MIT Sloan. Manag Rev 52(2):21–31. https://doi.org/10.0000/PMID57750728

Li DH, Lin J, Cui WT, Qian YJ (2018) The trade-off between knowledge exploration and exploitation in technological innovation. J Knowl Manag 22(4):781–801. https://doi.org/10.1108/JKM-09-2016-0401

Liu Y, Fang W, Feng TW, Gao N (2022) Bolstering green supply chain integration via big data analytics capability: the moderating role of data-driven decision culture. Ind Manag Data Syst 122(11):2558–2582. https://doi.org/10.1108/IMDS-11-2021-0696

Ma Z, Yu M, Gao C, Zhou J, Yang Z (2015) Institutional constraints of product innovation in China: evidence from international joint ventures. J Bus Res 68(5):949–956. https://doi.org/10.1016/j.jbusres.2014.09.022

Maalaoui A, Le Loarne-Lemaire S, Razgallah M (2020) Does knowledge management explain the poor growth of social enterprises? Knowledge exploitation insights from a systematic literature review on knowledge management and social entrepreneurship. J Knowl Manag 24(7):1513–1332. https://doi.org/10.1108/JKM-11-2019-0603

Maiolini R, Marra A, Baldassarri C, Carlei V (2016) Digital technologies for social innovation: an empirical recognition on the new enablers. J Technol Manag Innov 11(4):22–28. https://doi.org/10.4067/S0718-27242016000400004

Mani V, Delgado C, Hazen BT, Patel P (2017) Mitigating supply Chain risk via sustainability using big data analytics: evidence from the manufacturing supply chain. Sustainability 9(4). https://doi.org/10.3390/su9040608

March JG (1991) Exploration and exploitation in organizational learning. Organ Sci 2:71–87. https://doi.org/10.1287/orsc.2.1.71

Mikalef P, Pappas IO, Krogstie J, Giannakos M (2018) Big data analytics capabilities: a systematic literature review and research agenda. Inf Syst e-Bus Manag 16(3):547–578. https://doi.org/10.1007/s10257-017-0362-y

Mikalef P, Boura M, Lekakos G, Krogstie J (2019) Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment. Br J Manag 30(2):272–298. https://doi.org/10.1111/1467-8551.12343

Mikalef P, Krogstie J, Pappas IO, Pavlou P (2020) Exploring the relationship between big data analytics capability and competitive performance: the mediating roles of dynamic and operational capabilities. Inf Manag 57(2). https://doi.org/10.1016/j.im.2019.05.004

Mirvis P, Baltazar HME, Googins B, Albareda L (2016) Corporate social innovation: how firms learn to innovate for the greater good. J Bus Res 69(11):5014–5021. https://doi.org/10.1016/j.jbusres.2016.04.073

Müller Oliver FayM, Vom BJ (2018) The effect of big data and analytics on firm performance: an econometric analysis considering industry characteristics. J Manag Inf Syst 35(2):488–509. https://doi.org/10.1080/07421222.2018.1451955

Munodawafa RT, Johl, SK (2019) Big data analytics capabilities and eco-innovation: a study of energy companies. Sustainability 11(15). https://doi.org/10.3390/su11154254

Ndou V, Schiuma G (2020) The role of social innovation for a knowledge-based local development: insights from the literature review. Int J Knowl Based Dev 11(1):6–25. https://doi.org/10.1504/IJKBD.2020.106841

Netemeyer RG, Bearden WO, Sharma S (2003) Scaling procedures: Issues and applications. SAGE publications

Neumeier S (2012) Why do social innovations in rural development matter and should they be considered more seriously in rural development research? Proposal for a stronger focus on social innovations in rural development research. Socio Ruralis 52(1):48–69. https://doi.org/10.1111/j.1467-9523.2011.00553.x

Nicolopoulou K, Karataş-Özkan M, Vas C, Nouman M (2017) An incubation perspective on social innovation: the London Hub-a social incubator. RD Manag 47(3):368–384. https://doi.org/10.1111/radm.12179

Nunnally JC (1994) Psychometric Theory (3rd ed). University of Michigan Ann Arbor MI

Nwankpa JK, Roumani Y, Datta P (2022) Process innovation in the digital age of business: the role of digital business intensity and knowledge management J Knowl Manag 26(5):1319–1341. https://doi.org/10.1108/JKM-04-2021-0277

O’Reilly CAI, Tushman ML (2013) Organizational ambidexterity: past, present and future. Acad Manag Perspect 27(4):324–338. https://doi.org/10.5465/amp.2013.0025

Oeij PRA, Wouter VDT, Vaas F, Dhondt S (2019) Understanding social innovation as an innovation process: applying the innovation journey model. J Bus Res 101:243–254. https://doi.org/10.1016/j.jbusres.2019.04.028

Okorie O, Salonitis K, Charnley F, Moreno M, Turner C, Tiwari A (2018) Digitisation and the circular economy: a review of current research and future trends. Energies 11(11). https://doi.org/10.3390/en11113009

Olszak CM (2014) Towards an understanding business intelligence a dynamic capability-based framework for business intelligence. Proceedings of 2014 Federated conference on computer science and information systems (FedCSIS) 1103–1110. https://doi.org/10.15439/2014F68

Onsongo E (2019) Institutional entrepreneurship and social innovation at the base of the pyramid: the case of M-Pesa in Kenya. Ind Innov 26(4):360–390. https://doi.org/10.1080/13662716.2017.1409104

Pauleen D (2009) Personal knowledge management: putting the ‘person’ back into the knowledge equation. Online Inf Rev 33(2):221–224. https://doi.org/10.1108/14684520910951177

Rashidin MS, Gang D, Javed S, Hasan MM (2021) The role of artificial intelligence in sustaining the E-Commerce Ecosystem: Alibaba vs. Tencent. J Glob Inf Manag 30:1–25. https://doi.org/10.4018/JGIM.304067

Real JC, Leal A, Roldan JL (2006) Information technology as a determinant of organizational learning and technological distinctive competencies. Ind Mark Man 35:505–521. https://doi.org/10.1016/j.indmarman.2005.05.004

Rialti R, Zollo L, Ferraris A, Alon I (2019) Big data analytics capabilities and performance: evidence from a moderated multimediation model. Technol Forecast Soc Change 149. https://doi.org/10.1016/j.techfore.2019.119781

Said F, Zainal D, Jalil AA, Wright LT, Nisar T (2023) Big data analytics capabilities and sustainability reporting on Facebook: does tone at the top matter? Cogent Bus Manag 10(1):1–20. https://doi.org/10.1080/23311975.2023.2186745

Saka-Helmhout A, Chappin MMH, Rodrigues SB (2022) Corporate social innovation in developing countries. J Bus Ethics 181:589–605. https://doi.org/10.1007/s10551-021-04933-x

Sandberg B, Aarikka-Stenroos L (2014) What makes it so difficult? A systematic review on barriers to radical innovation. Ind Mark Manag 43(8):1293–1305. https://doi.org/10.1016/j.indmarman.2014.08.003

Shamim S, Yang YM, Ul ZN, Shah MH (2021) Big data management capabilities in the hospitality sector: service innovation and customer generated online quality ratings. Comput Hum Behav 121. https://doi.org/10.1016/j.chb.2021.106777

Shanock LR, Baran BE, Gentry WA, Pattison SC, Heggestad ED (2010) Polynomial regression with response surface analysis: a powerful approach for examining moderation and overcoming limitations of difference scores. J Bus Psychol 25(4):543–554. https://doi.org/10.1007/s10869-010-9183-4

Srinivasan R, Swink M (2018) An investigation of visibility and flexibility as complements to supply chain analytics: an organizational information processing theory perspective. Prod Oper Manag 27(10):1849–1867. https://doi.org/10.1111/poms.12746

Su XF, Zeng WP, Zheng MH, Jiang XL, Lin WH, Xu AX (2022) Big data analytics capabilities and organizational performance: the mediating effect of dual innovations. Eur J Innov Manag 25(4):1142–1160. https://doi.org/10.1108/EJIM-10-2020-0431

Sun Y, Liu J, Ding Y (2019) Analysis of the relationship between open innovation, knowledge management capability and dual innovation. Technol Analysis Strategic Manage 32:15–28

Suseno Y, Abbott L (2021) Women entrepreneurs’ digital social innovation: linking gender, entrepreneurship, social innovation and information systems. Inf Syst J 31(5):717–744. https://doi.org/10.1111/isj.12327

Tjörnbo O, McGowan K (2022) A complex-systems perspective on the role of universities in social innovation. Technol Forecast Soc Change 174. https://doi.org/10.1016/j.techfore.2021.121247

Unceta A, Castro-Spila J, Fronti JG (2016) Social innovation indicators. Innov Eur J Soc Sci Res 29(2):192–204. https://doi.org/10.1080/13511610.2015.1127137

Verhoef P, Kooge E, Walk N (2016) Creating value with big data analytics: Making smarter marketing decisions. Routledge. https://doi.org/10.4324/9781315734750

Wamba SF, Gunasekaran A, Akter S, Ren SJF, Dubey R, Childe SJ (2017) Big data analytics and firm performance: effects of dynamic capabilities. J Bus Res 70:356–365. https://doi.org/10.1016/j.jbusres.2016.08.009

Wang N, Wan J, Ma Z, Zhou Y, Chen J (2023a) How digital platform capabilities improve sustainable innovation performance of firms: the mediating role of open innovation. J Bus Res 167:114080. https://doi.org/10.1016/j.jbusres.2023.114080

Wang N, Xie W, Huang Y, Ma Z (2023b) Big Data capability and sustainability oriented innovation: The mediating role of intellectual capital. Business Strategy Environ 32(8):5702–5720

Wang Y, Hajli N (2017) Exploring the path to big data analytics success in healthcare. J Bus Res 70:287–299. https://doi.org/10.1016/j.jbusres.2016.08.002

Wang Y, Kung LA, Byrd TA (2018) Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast Soc. Change 126:3–13. https://doi.org/10.1016/j.techfore.2015.12.019

Xiao S, Oh KS (2021) Unraveling the underlying mechanisms of new product development in high-technology emerging-market multinationals. Manag Decis 59(1):68–83. https://doi.org/10.1108/MD-02-2019-0224

Xie X, Wu Y, Palacios-Marqu´es D, Ribeiro-Navarrete S (2022) Business networks and organizational resilience capacity in the digital age during COVID-19: a perspective utilizing organizational information processing theory. Technol Forecast Soc Change 177. https://doi.org/10.1016/j.techfore.2022.121548

Xu D, Yan S, Zhang Y, Zhang S, Nakamori Y, Chen L (2022) Knowledge management for extreme public health events COVID-19: based on Tiktok data. J Knowledge Manag 26(9):2354–2369. https://doi.org/10.1108/JKM-06-2021-0450

Yao A, Ma Z (2023) Toward a holistic perspective of congruence research with the polynomial regression model. J Appl Psychol 108(3):446–465. https://doi.org/10.1037/apl0001028

Article   PubMed   Google Scholar  

Yu W, Zhao G, Liu Q, Song YT (2021) Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: an organizational information processing theory perspective. Technol Forecast Soc Change 163. https://doi.org/10.1016/j.techfore.2020.120417

Zeng SX, Xie XM, Tam CM (2010) Relationship between cooperation networks and innovation performance of SMEs. Technovation 30(3):181–194. https://doi.org/10.1016/j.technovation.2009.08.003

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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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The Journal of Innovation and Knowledge (JIK) focuses on how we gain knowledge through innovation and how knowledge encourages new forms of innovation. Not all innovation leads to knowledge. Only enduring innovation that can be generalized across multiple fields creates theory and knowledge. JIK welcomes papers on innovations that improve the quality of knowledge or that can be used to develop knowledge. Innovation is a broad concept, covering innovation processes, structures, outcomes, antecedents, and behaviors at the organizational level in the private and public sectors as well as at the individual, national, and professional levels. JIK articles explore knowledge-related changes that introduce or encourage innovation to promote best practices within society. JIK provides an outlet for high-quality studies that have undergone double-blind peer review. In doing so, JIK ensures that such studies reach a global readership of scholars, consultants, practitioners, international leaders, and policymakers who recognize the importance of innovation and knowledge as economic drivers and who base their decisions on new ideas and findings in innovation and knowledge. JIK publishes content in the form of theoretical articles, empirical studies employing quantitative or qualitative methods, practice-oriented papers, teaching-oriented papers, case studies, book reviews, conference reports, short articles on current trends in science and society, abstracts of recent innovation and knowledge PhDs, and shorter opinion-based and review articles, commentaries, and debates. JIK publishes state-of-the-art research on emerging topics in the world of innovation and knowledge and appeals to a broad readership. The editors welcome suggestions for special issue topics. JIK articles should demonstrate contextual differences, while highlighting lessons for the wider audience. In sum, JIK is an interdisciplinary journal devoted to advancing theoretical and practical innovations and knowledge in a range of fields, including Economics, Business and Management, Engineering, Science, and Education. JIK has a broad scope to the following areas: 1. Innovation (including but not limited to: open innovation, innovation adoption and diffusion, organizational behavior and innovation, creativity, improvisation, and individual innovation, innovation in teams and groups, institutional and social innovation, consequences of innovation, critical approaches to innovation or innovation alliances and networks) in relation to knowledge, and vice versa. 2. Knowledge patterns in relation to innovation. 3. Knowledge-related changes that introduce innovations and best practices in society. 4. Globalization in innovation and knowledge. 5. Innovation policies and practices that lead to knowledge. 6. Cross-cultural case studies in knowledge and innovation. 7. New practical models and paradigms for understanding and fostering innovation and knowledge. 8. Knowledge and innovation derived from data. 9. Information systems in knowledge and innovation. 10. Knowledge and innovation in organizations and their behaviors. 11. Knowledge- and innovation-based systems, products, and processes. 12. Issues that affect the developers of education systems and educators who implement and manage innovations and knowledge. 13. Ethics in knowledge and innovation. 14. Knowledge and innovation transfer. 15. Quality in knowledge and innovation.

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• Abbreviations
• Introduction
• Literature review
• Knowledge management
• Individual outcomes
• Team outcomes
• Organizational outcomes
• New product development
• Methodology
• Publication trend
• Future directions
• Limitations
• Acknowledgments
• Bibliography

This research aims to show that knowledge management is integral to business strategy and can lead to more efficient new product development in high-tech companies. Organizations have been increasingly focused on knowledge management methods as they have realized how important it is to manage knowledge to stay competitive in their marketplaces. Knowledge Management (KM) is responsible for a company's efficiency, effectiveness, and innovation. Project outcomes, individual outcomes, and organizational outcomes are linked to knowledge in New Product Development (NPD). More than 28,548 KM papers published in the previous 22 years were examined in this research using Scopus and Web of Science; the original sample was narrowed down to items that contributed to KM literature. The R Studio and VOS Viewer software executed the descriptive statistics and scientific mapping approaches using co-citation analysis. The descriptive analysis involved studying publishing patterns over time, the geographical localization of the contributing institutions, journals, the most prolific authors, the top-performing institutions, and the most-cited papers. Scholars and practitioners have been paying close attention to knowledge management and organizational performance in recent years. Once implemented, the integrated approach may significantly influence organizational processes and performance. This study examines both KM ideas in NDP initiatives. Several intriguing discoveries are presented, with extensive explanations of their future direction, a conceptual framework for the study, and practice based on the literature.

The movement and growth of organizations, particularly in business, determine a country's economy ( Islam & Abd Wahab, 2021 ). According to Belmonte-Ureña et al. (2021) and Panda et al. (2022) , a small and medium-sized enterprise (SME) can significantly impact a country's success. One of an organization's main priorities is its performance ( Hanaysha & Mehmood, 2022 ). While revenues are commonly used to evaluate a company's performance, other indicators beyond income can represent standards for evaluation. Alshurideh et al. (2022) describe that performance is a metric established by management based on the organization's results over time. Moreover, an SME is likely to accomplish success on the financial and non-financial fronts within five (5) to ten (10) years. The concept of organizational performance assessment is formed based on the combined analysis of an organization's assets, i.e., human, physical, and capital, in order to achieve certain goals; determining an SME's performance is not solely financial ( Abubakar et al., 2019 ; Lee et al., 2022 ). Lazzeretti and Capone (2016) argue that an organization's domain matters for innovation. Previous literature points out that groups tend to have better performance and possibilities while embedded in collaboration.

Businesses face numerous obstacles to staying competitive because of globalization ( Katsikeas et al., 2019 ; Mukherjee, 2018 ). They encounter intense competition from other firms and risk losing consumers quickly since most need help recognizing and adapting to rapidly changing market trends. They are now driven to move away from other management approaches and towards knowledge management due to the growing value of knowledge. Knowledge management (KM) is defined as the capacity to manage information, including gathering knowledge from internal and external sources, transforming it into new strategies or ideas, and implementing and preserving it ( de Bem Machado et al., 2022 ). In the early twentieth century, Henry Ford's creative use of the assembly line in the automobile sector aided in the broad adoption of mass production ( Öberg & Alexander, 2019 ), resulting in cheaper manufacturing costs. Manufacturing costs are a point for rivalry. Businesses develop new products and identify new product and delivery techniques for existing products to boost earnings ( Kharub et al., 2022 ). Innovation  is a term used to describe breakthroughs ( Begum et al., 2022 ). By improving an organization's performance (in terms of time, cost, and innovation) and product enhancements and differentiation, KM enables competitive advantage ( Horng et al., 2022 ). KM also facilitates incorporating current knowledge into new and creative products. SMEs have been increasingly focused on KM methods as they realize how important it is to manage knowledge to stay competitive in their marketplaces ( Adam et al., 2022 ; Ma et al., 2022 ). KM is responsible for a company's efficiency, effectiveness, and innovation ( Chen et al., 2022 ). New product development  (NPD) knowledge is linked to design or manufacturing processes ( Idrees et al., 2022 ). NPD refers to all product process phases, from product development through customer service; alternatively, it can be as simple as repositioning an existing product in a new marketplace.

Knowledge generation is the key benefit of a KM approach. Firms must anticipate market surprises, be flexible and adaptable to quick market changes, and overcome product development issues as part of their competitiveness strategy ( Galli & Lopez, 2018 ). Several studies have demonstrated that a KM strategy can help to achieve these objectives ( Adam et al., 2022 ; Chen et al., 2022 ; de Bem Machado et al., 2022 ). Investigating the link between KM capabilities and organizational, individual, and project outcomes is critical because the findings may aid firms in furthering their understanding of KM's repercussions. Practitioners are more likely to pay attention to KM strategies considering the value of knowledge as a strategic resource. According to available literature, firms heavily invest in KM projects to acquire and leverage this strategic resource ( Chen et al., 2022 ). For many firms, capturing the most critical information and successfully disseminating it throughout the company is a crucial challenge. As a result of its relation to various performance measurements, KM has become a top priority for all enterprises ( Zhang et al., 2022 ). The collective knowledge in people's thoughts is a valuable resource for today's business ( Crespo et al., 2022 ).

In the past, the key trends, results, and implications of KM research were analyzed using various methods and methodologies, including bibliometrics. Serenko (2013) , for example, employed a meta-analysis technique to bring together the numerous findings from many KM studies. Several studies have analyzed the most relevant KM papers; however, these studies had a narrow focus and needed to cover a wider variety of KM literature ( Liao, 2003 ; Massaro et al., 2015 ). Literature reviews are frequently used in studies to incorporate the available literature on KM. Wallace et al. (2011) conducted a literature evaluation on a subset of KM research, while Ayatollahi and Zeraatkar (2020) reviewed KM studies in the healthcare industry. According to Massaro et al. (2015) and Ayatollahi et al. (2020) , many KM researchers need to be aware of past publications from a bibliometric viewpoint. Now that KM has its field of study with specialized publications like “ Journal of Innovation & Knowledge ”, it becomes essential to determine the elements that can contribute to its increased visibility in the scholarly community.

This study aims to compile the best KM papers published between 2000 and 2022 and sort them by publication year, number of authors, number of references, page count, keyword density, field of study, and publisher to learn more about the parameters influencing their citation counts. The following parts detail the study's strategy, results, and recommendations for future direction.

The definition of knowledge management has sparked much controversy in literature ( Despres & Chauvel, 1999 ). Much discussion focuses on the distinctions between information and knowledge ( Mårtensson, 2000 ). Although they can be used interchangeably, several writers have indicated that the two notions are separate ( McInerney, 2002 ). Information is commonly asserted to be a component of, but not entirely of, knowledge. Knowledge is a considerably broader phrase that encompasses information-based beliefs ( Maier & Hadrich, 2011 ). It also depends on the individual's commitment and knowledge of these ideas, influenced by interpersonal interactions and the development of judgment, conduct, and attitude  ( Almashari et al., 2002 ).

On the other hand, organizational knowledge comprises corporate expertise and common understandings and shares many of the same features as personal knowledge ( Martins et al., 2019 ). Organizational learning is linked to activities and is developed inside the firm through information and social interaction, providing growth opportunities ( Awan, 2019 ; Rehman et al., 2021 ). This type of knowledge is the foundation of KM. Progress is accomplished when knowledge flows from an individual's domain to the organization's. As knowledge is difficult to measure or audit, businesses must successfully manage it to fully use the skills and experience inherent in their systems and structures and the tacit knowledge held by their personnel. It is a process that assists organizations in finding, selecting, organizing, disseminating, and transferring vital information and knowledge necessary for operations, according to Di Vaio et al. (2021) .

Ammirato et al. (2021) recently defined KM as the comprehensive process of identifying, organizing, transferring, and utilizing information and skills. According to the survey contracted by Ferreira et al. (2018) , 92.2% of business owners believe that a KM system can influence employee learning and organizational growth; 66.2% say it helps them to work together as a team. Only 91% of those surveyed believe their KM system aids them in developing new training programs based on their expertise. According to Ode and Ayavoo (2020) , more than 50% of KM initiatives fail because firms need a well-developed KM approach. Zaim et al. (2019) , p.XX) explain that "instead of managing relevant knowledge, some businesses end up managing documents … this is a common blunder since many KM technologies are focused on document management rather than knowledge management”.

Because it leads to innovation, knowledge management is an effective technique for NPD ( Lazzeretti et al., 2016 ). It is especially crucial in High Technology (high-tech) organizations since they must deal more with the market's dynamic changes than others ( Islam et al., 2021 ). The short product life cycle necessitates innovation; a high-tech company must anticipate market surprises, overcome the constraints of its own and competitors' goods, and be laser-focused on the demands of its consumers. Individual knowledge sharing is also a central core of knowledge, and it is critical to establish a collaborative organization capable of adapting to market changes quickly and achieving effectiveness ( Haider et al., 2022 ). KM is a practice that encourages an integrated approach to finding, recording, analyzing, retrieving, managing, and sharing an organization's data assets ( Cui et al., 2019 ). These assets include databases, records, regulations, procedures, and employees’ previously untapped skills and experiences. An abundance of limitations confounds information sharing among individuals in an agency ( Obrenovic et al., 2020 ). Obstacles to information-sharing are common to giant organizations and massive multinational businesses and may pose problems for those working in these environments.

When workers recognize that information-sharing is beneficial, they are more likely to engage in it. Employees can accomplish their work more efficiently if they share their expertise ( Haider et al., 2023 ). Furthermore, it aids employee retention, personal growth, and professional advancement and rewards them for completing tasks ( Nguyen, 2020 ). Personal interaction is based on communication between individuals. Problem-solving, task coordination, information exchange, and conflict resolution are facilitated by this collaboration ( Harb et al., 2021 ). This component is crucial for optimal information consumption and leads to new knowledge generation. The efficiency with which embedded knowledge is translated to embodied knowledge is favorably connected to personal interactions ( Usman et al., 2019 ). This engagement must be regular and direct, and informal networks are more important than official ones.

Global Product Development (GPD) emerged in the recent decade. ( Kherbachi et al., 2020 ). It comes with GPD team members that are geographically dispersed, speaking different languages, and from various cultural backgrounds ( Haider et al., 2022 ). They differ from co-located teams that operate in a single location, such as a nation or city region, and speak the same language. According to Cui et al. (2019) , as GPD teams become increasingly common, research is needed to create strategies for GPD teams to reach performance levels comparable to those of their co-located counterparts. A project's success depends on the relationships between the various teams ( Harb et al., 2021 ). Communicating effectively between various NPD teams and reusing existing knowledge within an organization can influence whether a new product is released on schedule and budget. Recreating and recollecting the same information for various projects is expensive and time-consuming. It demonstrates the significance of capturing and distributing pre-existing knowledge among employees so that new knowledge may be built upon, describing innovation.

By incorporating essential departments and participants from the start of the project and anticipating manufacturability concerns, the product development process may accelerate in terms of market time ( Kharub et al., 2022 ). Cross-functional teams allow shared information and choices during design and manufacturing ( Awan & Sroufe, 2020 ). They also consider customers' demands ( Hanaysha & Mehmood, 2022 ). Cross-functional teams are encouraged in the NPD process to reduce misinterpretation and encourage informal sharing. For example, Cooper (2019) defined and measured NPD team effectiveness using a systems perspective to identify a set of inputs that could influence how teams interacted and worked. He found that team inputs and processes significantly impact NPD; because they formed their expertise by integrating separate collections of tacit knowledge, team members who previously worked together were more effective than those who did not. Experience being in the same team breeds efficiency.

Organizational performance measures an organization's capacity to meet the needs of its stakeholders and stay afloat in the market ( George et al., 2019 ). It is the result of the actions or activities carried out by members of an organization to determine how successfully the group has achieved its goals. According to Lee et al. (2022) , organizational performance is a multidimensional construct. Different performance characteristics enable a balanced and comprehensive assessment of an organization's performance ( Hanaysha et al., 2022 ). Organizational success requires integrating systems, operations, people, customers, partners, and management. It is positively related to the ability of KM to produce a competitive advantage ( Latilla et al., 2018 ). Obeso et al. (2020) provide the three "value disciplines," or strategic performance skills for competitive advantage. Gupta and Chopra (2018) identify the influence of KM resources on organizational performance.

New product development (NPD) initiatives are sophisticated business procedures that include people from areas of design, testing, manufacturing, and marketing ( Awan et al., 2018 ; Cooper, 2019 ). For some years, researchers have argued that project failures are partly caused by a lack of a systematic approach to these complex initiatives and have advocated for the adoption of formal process models to aid management decision-making ( Galli et al., 2018 ). An organization must decide the most critical initiatives to pursue and determine a time estimate and implementation sequence . KM enables this using the organization's expertise, including customer, product, market, process, financial, and personal services knowledge ( Haider & Kayani, 2020 ).

An NPD strategy is an information-processing approach that integrates a larger body of knowledge to achieve its objectives. This integration refers to an organization's blend of external and internal knowledge. NPD improves if the integration is good. The efficacy of knowledge management techniques plays a critical part in NPD strategy implementation; organizations that use appropriate knowledge management methods perform better. Organizations are likely to impress NPD performance if they adapt to the changes in the external environment faster than their competitors ( Cui et al., 2019 ), stimulating product research & development (R&D).

Project teams with high levels of shared knowledge in terms of customers, suppliers, and internal capabilities tend to outperform those with low levels of shared knowledge in process performance. Minimizing the impact of hurdles to knowledge exchange in a product development environment is also desirable. Yildirmaz et al. (2018) maintain that knowledge lifecycle management promotes effective information exchange within organizations, particularly project teams. According to Mohammadi Moghadam et al. (2018) , the essence of NPD is the production and use of new knowledge to address organizational problems and introduce new goods to the market. At the same time, an organization's capacity to manage its NPD processes is critical to its long-term viability. Benabdellah et al. (2021) emphasize that project accomplishment comes from practical knowledge-sharing among project team members. Project teams increase cooperation across a project lifecycle through socialization ( Stock et al., 2021 ). They can improve their knowledge-sharing expertise and skillsets over the project lifecycle. Employees use socialization to trade personal or specialized knowledge. Ball et al. (2022) support this by claiming that executives learn tacit knowledge through observation, imitation, and practice in a social setting.

This study involves a bibliometric analysis of current KM research ( Akhavan et al., 2016 ). Two (2) databases take centerstage - Scopus and Web of Science (WoS), ensuring that only high-quality articles are included. They contain the “Emerging Sources Citation Index”, “Social Sciences Citation Index”, “Science Citation Index Expanded”, “Science Citation Index”, and "CPCI-SSH". The time frame for the investigation is 2000–2022. The bibliometric approach is used to analyze and acquire data ( Gupta & Bhattacharya, 2004 ; Moed et al., 2014 ). Scopus and WoS have the most extensive repositories of peer-reviewed social sciences research and are widely used in empirical and quantitative studies ( Li et al., 2017 ). The contribution of authors, countries, the number of publications, and the number of citations of a topic are all quantified by bibliometric research, as indicated in the literature ( Kalantari et al., 2017 ).

KM keywords are combined using Boolean operators (i.e., AND, OR) to find relevant articles. The term "knowledge management" as a keyword returned 18,078 results. In addition, 73 unique keywords commonly used in the titles of articles emerged in the first sample, closely connected to the KM stream. A narrowed search of this comprehensive list of terms (knowledge management OR new product development OR organizational outcomes OR individual outcomes OR project outcomes) yielded 25,622 articles.

Two programs, the VOS Viewer Version 1.6.18 and the R Software Version 4.2.2, support this study. The VOS Viewer conducts network analysis and displays the findings in a graphical format, identifying the networks of author collaborations and the links between KM themes in this case. The investigation is developed in R, a computer language for statistical analyses ( Derviş, 2019 ), and visualized using the VOS Viewer ( Van Eck & Waltman, 2017 ). The search terms are closely associated with the purpose, scope, gap, and research questions to be addressed, representing the inclusion criteria. Also, only articles published in the English language are selected. The search involves research articles, book chapters, and conference papers published between 2000 and 2022. Fig. 1 provides the article selection summary.

Article selection summary.

The VOS Viewer maps the bibliographic materials into a graphical representation by using specified input data ( Al-Ashmori, Othman and Rahmawati (2020) ; Williams (2020) . The data are analyzed using various bibliometric methods, including BC, co-citation, and co-occurrence of the author's keywords. When two (2) authors, "A" and "B", quote a third author's document, "C", the citation is referred to as BC. When a document references two (2) publications, this is referred to as co-citation, as when publications A and B are mentioned by research C. Additionally, the co-occurrence of keywords is calculated by calculating the number of times a term appears in the same article. Table 1 presents the keywords, queries, and number of documents.

Keywords, queries, and number of documents

Table 1 presents the keywords, i.e., knowledge management OR new product development, knowledge management OR organizational outcomes, knowledge management OR individual outcomes, knowledge management OR project outcomes, knowledge management OR new product development OR organizational outcomes OR individual outcomes OR project outcomes. Despite an increasing publication trend among policymakers and scholars in developing countries on KM, more studies are needed.

Fig. 2 shows the research publication trend in “knowledge management”. The study begins with the year 2000, observing an increasing trend in publications yearly, with 28,548 publications cited 49,6339 times.

Knowledge management publications based on country.

Fig. 2 displays the annual output for the top twenty (20) countries producing the most KM publications, the selection minimum being 5. One hundred and thirty-four (134) countries produced KM-related publications, with 107 countries meeting the threshold. Table 2 demonstrates that the United States has the highest number of publications by a developed country (5084), while the United Kingdom ranks second with 2442 publications and China third with 1501 publications. More importantly, Table 2 displays the surging interest of policymakers and researchers in KM.

Publication trend based on country

As shown in Table 3 , the maximum number of KM-related publications in 2022 is 1939. From 2000 to 2005, there was no publication related to KM. However, the yearly growth rate of KM publications exceeded four (4) times, from 384 articles in 2006 to 1939 articles in 2022. This progression shows considerable growth in KM research from an NPD perspective. KM data was taken until 10 December 2022.

Publication trend based on number of citations, publications, and year

Table 4 shows the fourteen (14) journals that had the maximum number of KM-related publications between 2005 and 2022. The highest number of publications came from the Journal of Knowledge Management (916 publications, 40,733 citations) and Knowledge Management Research & Practice (360 publications, 5826 citations). Most journals were from Scopus (SSCI, SCIE, and ESCI-indexed). This study's results reveal that most publications were on KM practices, organizational culture, leadership behavior, and performance.

Number of publications based on journals

Thirty-seven thousand nine hundred and eighteen (37,918) authors produced 28,548 publications on Knowledge Management. Based on the number of publications, citations received, number of publications, H-index, and institutional affiliation, the fourteen (14) most prolific authors are shown in Table 5 . The maximum number of publications was by Ye Li and Ying Wang.

Number of publications and citations based on authors

Table 6 shows the institutions that contributed the most to the growth of KM research. A country's citation count is based on the institutional affiliations given on the publications. Therefore, institutions in the United States of America, the United Kingdom, and China are the most significant contributors. Asian institutions made a small but considerable contribution, primarily through China, India, and Malaysia. Based on the number of publications, the institution's ranking is shown in Table 5 . The Islamic Azad University and The Hong Kong Polytechnic University achieved 160 publications. These two (2) institutions are among the most influential in KM research. The top 25 universities in the world based on publications comprise universities in the United States of America, the United Kingdom, and China.

Number of publications and citations based on institutions

As seen in Fig. 3 , items are identified by label and node. The sizes of each item's label and node are determined by the item's weight (importance). Furthermore, the distances between different keywords, and their placement and relatedness to other topics, show their connections in the bibliographic network map. Based on the bibliometric analysis, several variables relate to knowledge management and are marked by large letters. It indicates research that examines the variables’ effect or relationship with KM. There are five (5) primary subjects related to KM: new product development (464 frequency), innovation (280 frequency), project management (218 frequency), knowledge transfer/sharing (145 frequency), and organizational learning (116 frequency).

Trending Keywords used from (2000–2022).

This study uses bibliometric analysis to thoroughly assess the existing literature on KM, NPD, organizational outcomes, individual outcomes, and project outcomes in SMEs to identify antecedents, consequences, and future research paths, aiding in the development of a conceptual map. It is likely the first work that uses a systematic methodology and a bibliometric approach to investigate KM and NDP in SMEs. The literature is analyzed using (1) “textual analysis”, identifying emerging research hotspots and keywords such as organizational outcomes, individual outcomes, and project outcomes as critical success factors for effective KM and NDP in SMEs; (2) “co-citation analysis” of references, identifying the theoretical foundations of knowledge as a competitive advantage through KM and NDP; and (3) “bibliographic coupling analysis” of documents, revealing the antecedents.

The generated concept map may aid practitioners in comprehending the distinct roles of KM and NDP in the specific context of SMEs, particularly in terms of organizational, individual, and project performance development ( Haider and Kayani, 2020 ). The results indicate the following. First, KM and NDP benefit SMEs with organizational learning, improved customer interaction, innovations, increased profit, enhanced operational processes, and faster decision-making. Second, innovation, trust, and performance are highlighted as crucial human elements in SMEs associated with KM and NDP. Third, human resource management research can contribute to KM and NDP in the SME domain by examining KM and NDP-based practices, establishing a link between the emergence of innovation and innovative behaviors, and gaining a better understanding of strategies for the long-term storage and retrieval of tacit and explicit knowledge, or organizational memory.

This study's systematic review of the literature has identified clear directions for future research in the following areas: governance structure, human resource management support, knowledge-sharing practices, managerial decisions, types of tools and sharing mechanisms, type and complexity of knowledge, organizational outcomes, individual outcomes, project outcomes, and SMEs’ size and sector. Numerous inquiry domains are concerned with the fundamental human challenges and functions, moving away from the technical emphasis of KM and NDP.

This study's bibliometric analysis indicates that HRM research has the potential to advance understanding of SMEs’ behaviors related to KM and NDP in three specific areas: (1) understanding KM and NDP-based practices; (2) connecting the emergence of innovation and innovative behaviors to these practices to organizational, individual and project outcomes; and (3) contributing to a better understanding of strategies for long-term storage and retrieval of tacit and explicit knowledge. To begin, HRM researchers can examine KM practices, tools, and mechanisms to design SME studies that clarify the interplay and impact on an employee's KM and NDP behavior, to support the employee and facilitate knowledge management and sharing in succession planning. Conceptually, a strategy-as-practice perspective may facilitate the adoption of everyday practices and a better understanding of how SME employees execute knowledge management and sharing in their local settings.

Second, the results establish a stronger relationship between KM and NDP to innovation in the context of SMEs. However, further study is necessary to understand how KM contributes to practical innovation in NPD. Although it is well established that KM implementation is necessary for an SME's innovation capabilities, more understanding is needed to manage this implementation. Future studies can examine how information technology and digitalization enhance SMEs’ KM and NPD, leading to innovation as organizational, individual, and project outcomes. SME preferences for knowledge management through online or face-to-face channels should be investigated, along with the digital capabilities required to learn new information and apply innovation.

Third, there needs to be a better understanding of how SMEs acquire and preserve knowledge and more studies on organizational memory in SMEs. HRM scholars can investigate its strategic importance and how tacit and explicit knowledge can be proactively acquired, stored, and retrieved to help SMEs in the long run.

This study comes with certain limitations. For instance, bibliometric analysis is one of many literature review methods. Systematic literature review (SLR), interpretative techniques, and narrative approaches may also be used. However, bibliometrics provides a more scientific synthesis of a topic using several sources through the dimension database. According to Walsh & Renaud (2017) , bibliometric approaches need markers to quantify the amount, quality, and relationships between publications, obscuring emerging ideas in a study area. Thus, this study's strategy to circumvent this constraint may serve as a model for future KM-related advancement using bibliometric analysis. Nonetheless, this study of keyword co-occurrence, abstracts, and titles may include some bias. The sample consists only of journal articles and conference papers, book chapters with no special issues were included. Because the VOS Viewer uses a fractional counting approach to restrict journal citations, alternative applications such as Histcite, Pajek, or SCiMat may analyze data differently to provide various viewpoints.

Future research may use a constructive categorization strategy to highlight emerging research trends in knowledge systems. A mix of direct citation and BCA-D analysis may be beneficial. By choosing and synthesizing abstracts, the selected approach may omit certain insights that can be gained from full-text analysis. Future scholars can continue by coding and analyzing whole manuscripts. Despite these limitations, this work significantly contributes to the growing knowledge of KM and NPD related to organizational, individual, and project outcomes in SMEs.

This study was supported by grants from National Natural Science Foundation of China (72171197), the Natural Science Foundation of Sichuan Province of China (23NSFSC0795).

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• DOI: 10.1016/j.indic.2024.100431
• Corpus ID: 270744925

Mitigating the flood disaster effects through the implementation of knowledge management: A systematic literature review

• Maryam Yousefi Mohammadi , E. Abbasi , +1 author Ali Asgari
• Published in Environmental and… 1 June 2024
• Environmental Science

52 References

Nature-based solutions (nbs) in spatial planning for urban flood mitigation: the perspective of flood management experts in accra, geophysical assessment of flood vulnerability of accra metropolitan area, ghana, adjustment or transformation disaster risk intervention examples from austria, indonesia, kiribati and south africa, strengthening flood resilient development in malaysia through integration of flood risk reduction measures in local plans, innovation in flood risk management: an ‘avenues of innovation’ analysis, knowledge management practices in disaster management: systematic review, flood risk management in austria: analysing the shift in responsibility-sharing between public and private actors from a public stakeholder's perspective, past, present, and prospective themes of sustainable agricultural supply chains: a content analysis, factors affecting successful transition between post-disaster recovery phases: a case study of 2010 floods in sindh, pakistan, spatial and temporal distribution and trend in flood and drought disasters in east china., related papers.

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The Role of Top Management Involvement and Supply Chain Integration on Smes’ Innovation Performance: Moderation Impact of Firm Experience Capability

• Published: 28 June 2024

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• Timothy Amoako   ORCID: orcid.org/0000-0001-6669-6344 1 ,
• Hao Chen 1 ,
• Stephen Abiam Danso 1 &
• Edem Segbefia 1  

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Top management involvement (MVV) is acknowledged to be a critical factor in enhancing firm performance. As a result, we evaluated the mediation role of supply chain integration (SCI) in the relationship between MVV and innovation performance. Again, the study evaluated the moderation role of firm experience capability (FEXCAP) in the association between MVV and SCI (internal, customer, and supplier). To this end, we conducted a quantitative empirical study in Ghana enterprise agency. Data was gathered from 685 manufacturing and service small and medium enterprises’ top managers and analyzed using the Analysis of Moment Structures (Amos) and Statistical Package for Social Sciences version 26 software. The results revealed that SCI (internal, customer, and supplier) partially mediated the relationship between MVV and innovation performance.

Additionally, FEXCAP positively moderated the association between MVV and SCI (internal, customer, and supplier). The study is critical as it endorses the significant role of MVV in improving the effectiveness of SCI and FEXCAP in firm innovation performance. We evaluated our model in a small and medium context.

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Afshan, N., Mandal, P., & Gunasekaran, A. (2022). Mediating role of immediate performance outcomes between supply chain integration and firm performance. Asia Pacific Journal of Marketing and Logistics, 34 (4), 669–687. https://doi.org/10.1108/APJML-11-2020-0841

Article   Google Scholar  

Alfalla-Luque, R., Marin-Garcia, J. A., & Medina-Lopez, C. (2015). An analysis of the direct and mediated effects of employee commitment and supply chain integration on organisational performance. International Journal of Production Economics, 162 , 242–257. https://doi.org/10.1016/j.ijpe.2014.07.004

Amoako, T., Huai Sheng, Z., & Dogbe, C. S. K. (2022a). Effect of internal integration on SMEs’ performance: The role of external integration and ICT. International Journal of Productivity and PerformaNce Management, 71 (2), 643–665. https://doi.org/10.1108/IJPPM-03-2020-0120

Amoako, T., Sheng, Z. H., & Dogbe, C. S. K. (2022b). Assessing the moderation role of ICT in the relationship between supply chain integration and SME performance. Journal of Industrial Integration and Management, 7 (02), 203–233. https://doi.org/10.1142/S2424862221500160

Amoako-Gyampah, K., Meredith, J., & Loyd, K. W. (2018). Using a social capital lens to identify the mechanisms of top management commitment: A case study of a technology project. Project Management Journal, 49 (1), 79–95. https://doi.org/10.1177/875697281804900106

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103 (3), 411. https://doi.org/10.1037/0033-2909.103.3.411

Attrams, A. S., & Tshehla, M. (2022). Challenges of services sector SMEs in a developing country: A case of Ghana. Journal of the International Council for Small Business, 3 (3), 228–236. https://doi.org/10.1080/26437015.2021.1982369

Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative science quarterly , 421–458.  https://doi.org/10.2307/2393203

Balsmeier, B., & Czarnitzki, D. (2013). How important is industry-specific managerial experience for innovative firm performance? In Academy of Management Proceedings (Vol. 2013, No. 1, p. 12060). Briarcliff Manor, NY 10510: Academy of Management

Barney, J. B. (2012). Purchasing, supply chain management and sustained competitive advantage: The relevance of resource-based theory. Journal of Supply Chain Management, 48 (2), 3–6. https://doi.org/10.1111/j.1745-493X.2012.03265.x

Barrales-Molina, V., Montes, F. J. L., & Gutierrez-Gutierrez, L. J. (2015). Dynamic capabilities, human resources, and operating routines: A new product development approach. Industrial Management & Data Systems, 115 (8), 1388–1411. https://doi.org/10.1108/IMDS-02-2015-0058

Ben Slimane, S., Coeurderoy, R., & Mhenni, H. (2022). Digital transformation of small and medium enterprises: A systematic literature review and an integrative framework. International Studies of Management & Organization, 52 (2), 96–120. https://doi.org/10.1080/00208825.2022.2072067

Ben Slimane, S. (2021). Absorption–technological absorptive capacity and innovation: The primacy of knowledge.  Innovation Economics, Engineering and Management Handbook 2: Special Themes , 43–49. https://doi.org/10.1002/9781119832522.ch3

Bengtsson, M., Raza-Ullah, T., & Vanyushyn, V. (2016). The coopetition paradox and tension: The moderating role of coopetition capability. Industrial Marketing Management, 53 , 19–30. https://doi.org/10.1016/j.indmarman.2015.11.008

Boonstra, A. (2013). How do top managers support strategic information system projects and why do they sometimes withhold this support? International Journal of Project Management, 31 (4), 498–512. https://doi.org/10.1016/j.ijproman.2012.09.013

Brusset, X., & Teller, C. (2016). Supply Chain Capabilities, Risks, and Resilience. International Journal of Production Economic, 184 , 59–68. https://doi.org/10.1016/j.ijpe.2016.09.008

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56 (2), 81. https://doi.org/10.1037/h0046016

Caroline, N., Harriet, K., & Anne, N. (2016). Top management commitment for successful small and medium enterprises (SMEs): a hoax or a reality? European Scientific Journal , 12(4). https://doi.org/10.19044/esj.2016.v12n4p259

Chang, W., Ellinger, A. E., & Kim, K. K. (2016). Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors. European Management Journal, 34 (3), 282–295. https://doi.org/10.1016/j.emj.2015.11.008

Chen, H., Amoako, T., Quansah, C. E. Danso, S. A., & Jidda, D. J. (2023). Assessment of the impact of management commitment and supply chain integration on SMEs’ innovation performance: Moderation role of government support. Heliyon, 9(5). https://doi.org/10.1016/j.heliyon.2023.e15914

Chen, I. J., & Paulraj, A. (2004). Towards a theory of supply chain management: The constructs and measurements. Journal of Operations Management, 22 (2), 119–150. https://doi.org/10.1016/j.jom.2003.12.007

Colbert, B. A. (2004). The complex resource-based view: Implications for theory and practice in strategic human resource management. Academy of Management Review, 29 (3), 341–358. https://doi.org/10.5465/amr.2004.13670987

Danese, P., Romano, P., & Formentini, M. (2013). The impact of supply chain integration on responsiveness: The moderating effect of using an international supplier network. Transportation Research Part e: Logistics and Transportation Review, 49 (1), 125–140. https://doi.org/10.1016/j.tre.2012.08.002

Dong, L. (2008). Exploring the impact of top management support of enterprise systems implementations outcomes: Two cases. Business Process Management Journal, 14 (2), 204–218. https://doi.org/10.1108/14637150810864934

Doty, D. H., & Glick, W. H. (1998). Common methods bias: Does common methods variance really bias results? Organizational Research Methods, 1 (4), 374–406. https://doi.org/10.1177/1094428198140

Easterby-Smith, M., Lyles, M. A., & Peteraf, M. A. (2009). Dynamic capabilities: Current debates and future directions. British Journal of Management, 20 , S1–S8. https://doi.org/10.1111/j.1467-8551.2008.00609.x

Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21 (10–11), 1105–1121. https://doi.org/10.1002/1097-0266(200010/11)21:10/11%3c1105::AID-SMJ133%3e3.0.CO;2-E

Farzaneh, M., Ghasemzadeh, P., & Nazari, J. A. (2021). Contributory role of dynamic capabilities in the relationship between organizational learning and innovation performance. European Journal of Innovation Management, 24 (3), 655–676. https://doi.org/10.1108/EJIM-12-2019-0355

Fawcett, S. E., & Magnan, G. M. (2002). The rhetoric and reality of supply chain integration. International Journal of Physical Distribution & Logistics Management, 32 (5), 339–361. https://doi.org/10.1108/09600030210436222

Flynn, B. B., & Huo, & B., Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28 (1), 58–71. https://doi.org/10.1016/j.jom.2009.06.001

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics . Sage Publications Sage CA. https://doi.org/10.1177/002224378101800313

Book   Google Scholar  

Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: An international study of supply chain strategies. Journal of Operations Management, 19 (2), 185–200. https://doi.org/10.1016/S0272-6963(00)00055-3

Gaskin, J., & Lim, J. (2016). Master Validity Tool, AMOS Plugin: Gaskination's StatWiki.

Gaskin, J., James, M., & Lim, J. (2020). Indirect Effects, AMOS Plugin. Gaskination's StatWiki.

GEA. (2020). Ghana Enterprise Agency. Available: https://gea.gov.gh/ , 2020.

Gounaris, S., & Tzempelikos, N. (2012). Conceptualization and measurement of key account management orientation. JBM-Journal of Business Market Management, 5(3): 173–194. https://hdl.handle.net/10419/66011

Hair, J. F., William, C. B., Babin, B. J., et al. (2010). Multivariate Data Analysis . Prentice Hall.

Google Scholar  

Harmancioglu, N., Grinstein, A., & Goldman, A. (2010). Innovation and performance outcomes of market information collection efforts: The role of top management team involvement. International Journal of Research in Marketing, 27 (1), 33–43. https://doi.org/10.1016/j.ijresmar.2009.09.005

Helfat, C. E. (2007). Stylized facts, empirical research, and theory development in management. Strategic organization, 5 (2), 185–192. https://doi.org/10.1177/1476127007077559

Hermano, V., & Martín-Cruz, N. (2016). The role of top management involvement in firms performing projects: A dynamic capabilities approach. Journal of Business Research, 69 (9), 3447–3458. https://doi.org/10.1016/j.jbusres.2016.01.041

Hooshangi, M., Sadaghiani, J. S., & Astaneh, M. R. (2017). The mediation role of supply chain integration in the relationship between employee commitment with organisational performance. International Journal of Business Information Systems, 24 (2), 210–226. https://doi.org/10.1504/IJBIS.2017.081447

Horn, P., Scheffler, P., & Schiele, H. (2014). Internal integration as a pre-condition for external integration in global sourcing: A social capital perspective. International Journal of Production Economics, 153 , 54–65. https://doi.org/10.1016/j.ijpe.2014.03.020

Hsu, L. C., & Wang, C. H. (2012). Clarifying the effect of intellectual capital on performance: The mediating role of dynamic capability. British Journal of Management, 23 (2), 179–205. https://doi.org/10.1111/j.1467-8551.2010.00718.x

Huang, M. C., Yen, G. F., & Liu, T. C. (2014). Reexamining supply chain integration and the supplier's performance relationships under uncertainty. Supply Chain Management: An International Journal, 19 (1), 64–78. https://doi.org/10.1108/SCM-04-2013-0114 .

Huo, B. (2012). The impact of supply chain integration on company performance: An organizational capability perspective. Supply Chain Management: An International Journal, 17 (6), 596–610. https://doi.org/10.1108/13598541211269210

Huo, B., Ye, Y., & Zhao, X. (2016). The impact of human capital on supply chain integration and competitive performance. International Journal of Production Economics, 178 , 132–143. https://doi.org/10.1016/j.ijpe.2016.05.009

Jajja, M. S. S., Chatha, K. A., & Farooq, S. (2018). Impact of supply chain risk on agility performance: Mediating role of supply chain integration. International Journal of Production Economics, 205 , 118–138. https://doi.org/10.1016/j.ijpe.2018.08.032

Kline, P. (2015). A handbook of test construction (psychology revivals): introduction to psychometric design. Routledge. https://doi.org/10.4324/9781315695990

Kozlenkova, I. V., Samaha, S. A., & Palmatier, R. W. (2014). Resource-based theory in marketing. Journal of the Academy of Marketing Science, 42 , 1–21. https://doi.org/10.1007/s11747-013-0336-7

Kwon, I. W. G., & Suh, T. (2004). Factors affecting the level of trust and commitment in supply chain relationships. Journal of Supply Chain Management, 40 (1), 4–14. https://doi.org/10.1111/j.1745-493X.2004.tb00165.x

Lee, H. L., & Whang, S. (2000). Information sharing in a supply chain. International Journal of Manufacturing Technology and Management, 1 (1), 79–93. https://doi.org/10.1504/IJMTM.2000.001329

Leuschner, R., Rogers, D. S., & Charvet, F. F. (2013). A meta-analysis of supply chain integration and firm performance. Journal of Supply Chain Management, 49 (2), 34–57. https://doi.org/10.1111/jscm.12013

MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88 (4), 542–555. https://doi.org/10.1016/j.jretai.2012.08.001

Marchet, G., Melacini, M., & Perotti, S. (2018). Business logistics models in omni-channel: A classification framework and empirical analysis. International Journal of Physical Distribution & Logistics Management, 48 (4), 439–464. https://doi.org/10.1108/IJPDLM-09-2016-0273

Menguc, B., Auh, S., & Yannopoulos, P. (2014). Customer and supplier involvement in design: The moderating role of incremental and radical innovation capability. Journal of Product Innovation Management, 31 (2), 313–328. https://doi.org/10.1111/jpim.12097

Merrilees, B., Rundle-Thiele, S., & Lye, A. (2011). Marketing Capabilities: Antecedents and implications for B2B SME performance. Industrial Marketing Management, 40 (3), 368–375. https://doi.org/10.1016/j.indmarman.2010.08.005

Napolitano, L. (1997). Customer-supplier partnering: A strategy whose time has come. Journal of Personal Selling & Sales Management, 17 (4), 1–8. https://doi.org/10.1080/08853134.1997.10754105

Naranjo-Gil, D. (2009). Management information systems and strategic performances: The role of top team composition. International Journal of Information Management, 29 (2), 104–110. https://doi.org/10.1016/j.ijinfomgt.2008.05.009

Narasimhan, R., & Kim, S. W. (2002). Effect of supply chain integration on the relationship between diversification and performance: Evidence from Japanese and Korean firms. Journal of Operations Management, 20 (3), 303–323. https://doi.org/10.1016/S0272-6963(02)00008-6

Nunnally, J. C. (1978). An overview of psychological measurement. Clinical diagnosis of mental disorders: A handbook , 97–146. https://doi.org/10.1007/978-1-4684-2490-4_4

Ouakouak, M. L., Ouedraogo, N., & Mbengue, A. (2014). The mediating role of organizational capabilities in the relationship between middle managers’ involvement and firm performance: A European study. European Management Journal, 32 (2), 305–318. https://doi.org/10.1016/j.emj.2013.03.002

Pakurár, M., Haddad, H., & Nagy, J. (2019). The impact of supply chain integration and internal control on financial performance in the Jordanian banking sector. Sustainability, 11 (5), 1248. https://doi.org/10.3390/su11051248

Petit, Y. (2012). Project portfolios in dynamic environments: Organizing for uncertainty. International Journal of Project Management, 30 (5), 539–553. https://doi.org/10.1016/j.ijproman.2011.11.007

Podsakoff, P. M., MacKenzie, S. B., & Lee, J.-Y. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88 (5), 879. https://doi.org/10.1037/0021-9010.88.5.879

Ponsignon, F., Smith, J. S., & Smart, A. (2020). Development and validation of a measurement scale for the experience capability construct. Journal of Service Managemen, t, 32 (3), 315–345. https://doi.org/10.1108/JOSM-11-2019-0353

Robertson, J., Caruana, A., & Ferreira, C. (2021). Innovation performance: The effect of knowledge-based dynamic capabilities in cross-country innovation ecosystems. International Business Review . https://doi.org/10.1016/j.ibusrev.2021.101866.101866

Rodríguez, N. G., Pérez, M. J. S., & Gutiérrez, J. A. T. (2008). Can a good organizational climate compensate for a lack of top management commitment to new product development? Journal of Business Research, 61 (2), 118–131. https://doi.org/10.1016/j.jbusres.2007.06.011

Ruel, S., El Baz, J., Ivanov, D. & Das, A. (2021). Supply chain viability: Conceptualization, measurement, and nomological validation. Annals of Operations Research ,  335 , 1107–1136. https://doi.org/10.1007/s10479-021-03974-9

Schoenherr, T., & Swink, M. (2012). Revisiting the arcs of integration: Cross-validations and extensions. Journal of Operations Management, 30 (1–2), 99–115. https://doi.org/10.1016/j.jom.2011.09.001

Shou, Y., Li, Y., & Park, Y. (2018). Supply chain integration and operational performance: The contingency effects of production systems. Journal of Purchasing and Supply Management, 24 (4), 352–360. https://doi.org/10.1016/j.pursup.2017.11.004

Siagian, H., & Tarigan, Z. J. H. (2021). The impact of top management commitment, green purchasing, and supply chain management practices on operational performance  (Doctoral dissertation: Petra Christian University).  https://repository.petra.ac.id/id/eprint/19181

Song, G., & Song, S. (2021). Fostering supply chain integration in omni-channel retailing through human resource factors: Empirical study in China’s market. International Journal of Logistics Research and Applications, 24 (1), 1–22. https://doi.org/10.1080/13675567.2020.1713305

Sood, A., James, G. M., & Tellis, G. J. (2012). Predicting the path of technological innovation: SAW vs. Moore, Bass, Gompertz, and Kryder. Marketing Science, 31 (6): 964–979. https://doi.org/10.1287/mksc.1120.0739

Staeh, L. (2010). Understanding the role of managerial agency in achieving business benefits from ERP systems. Information Systems Journal, 20 (3), 213–238. https://doi.org/10.1111/j.1365-2575.2008.00316.x

Swink, M. (2000). Technological innovativeness as a moderator of new product design integration and top management support. Journal of Product Innovation Management: An International Publication of the Product Development & Management Association, 17 (3), 208–220. https://doi.org/10.1111/1540-5885.1730208

Takase, M., Maude, P., & Manias, E. (2005). Nurses’ job dissatisfaction and turnover intention: Methodological myths and an alternative approach. Nursing & Health Sciences, 7 (3), 209–217. https://doi.org/10.1111/j.1442-2018.2005.00232.x

Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28 (13), 1319–1350. https://doi.org/10.1002/smj.640

Teece, D. J. (2019). A capability theory of the firm: An economics and (strategic) management perspective. New Zealand Economic Papers, 53 (1), 1–43. https://doi.org/10.1080/00779954.2017.1371208

Teece, D. J., Pisano, G., & Shuen, A. (1990). Firm capabilities, resources and the concept of strategy,. Economic analysis and policy working paper EAP 38, University of California .

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management , 18(7): 509-533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z

Teece, D. J. (2017). Dynamic capabilities and (digital) platform lifecycles. Entrepreneurship, innovation, and platforms . Emerald Publishing Limited, pp.211–225. https://doi.org/10.1108/S0742-332220170000037008

Thanh Nhon, H., Van Phuong, N., & Quang Trung, N. (2020). Exploring the mediating role of dynamic capabilities in the relationship between intellectual capital and performance of information and communications technology firms. Cogent Business & Management, 7 (1), 1831724. https://doi.org/10.1080/23311975.2020.1831724

Thun, J. H., & Müller, A. (2010). An empirical analysis of green supply chain management in the German automotive industry. Business Strategy and the Environment, 19 (2), 119–132. https://doi.org/10.1002/bse.642

Tian, H., Dogbe, C. S. K., & Pomegbe, W. W. K. (2021a). Organizational learning ambidexterity and openness, as determinants of SMEs’ innovation performance. European Journal of Innovation Management, 24 (2), 414–438. https://doi.org/10.1108/EJIM-05-2019-0140

Tian, H., Otchere, S. K., & Coffie, C. P. (2021b). Supply chain integration, interfirm value co-creation and firm performance nexus in Ghanaian SMEs: Mediating roles of stakeholder pressure and innovation capability. Sustainability, 13 (4), 2351. https://doi.org/10.3390/su13042351

Tzempelikos, N. (2015). Top management commitment and involvement and their link to key account management effectiveness. Journal of Business & Industrial Marketing, 30 (1), 32–44. https://doi.org/10.1108/JBIM-12-2012-0238

Visamitanan, K., & Assarut, N. (2021). Impact of green supply chain management practices on employee engagement and organizational commitment: Mediating role of firm performance. Global Business Review., 10 (1177/09721509211018569), 09721509211018569. https://doi.org/10.1177/09721509211018569

Wong, C. Y., Boon-Itt, S., & Wong, C. W. (2011). The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance. Journal of Operations Management, 29 (6), 604–615. https://doi.org/10.1016/j.jom.2011.01.003

Wu, L., Chuang, C.-H., & Hsu, C.-H. (2014). Information sharing and collaborative behaviors in enabling supply chain performance: A social exchange perspective. International Journal of Production Economics, 148 , 122–132. https://doi.org/10.1016/j.ijpe.2013.09.016

Ye, X., Ren, S., & Li, X. (2020). The mediating role of psychological capital between perceived management commitment and safety behavior. Journal of Safety Research, 72 , 29–40. https://doi.org/10.1016/j.jsr.2019.12.004

Yen, H. R., & Niehoff, B. P. (2004). Organizational citizenship behaviors and organizational effectiveness: Examining relationships in Taiwanese banks. Journal of Applied Social Psychology, 34 (8), 1617–1637. https://doi.org/10.1111/j.1559-1816.2004.tb02790.x

Young, R., & Poon, S. (2013). Top management support—almost always necessary and sometimes sufficient for success: Findings from a fuzzy set analysis. International Journal of Project Management, 31 (7), 943–957. https://doi.org/10.1016/j.ijproman.2012.11.013

Yu, Y., & Huo, B. (2018). Supply chain quality integration: Relational antecedents and operational consequences. Supply Chain Management: An International Journal, 23 (3), 188–206. https://doi.org/10.1108/SCM-08-2017-0280

Zhang, M., Guo, H., & Huo, B. (2019). Linking supply chain quality integration with mass customization and product modularity. International Journal of Production Economics, 207 , 227–235. https://doi.org/10.1016/j.ijpe.2017.01.011

Zhao, X., Huo, B., & Flynn, B. B. (2008). The impact of power and relationship commitment on the integration between manufacturers and customers in a supply chain. Journal of Operations Management, 26 (3), 368–388. https://doi.org/10.1016/j.jom.2007.08.002

Zhao, X., Huo, B., & Selen, W. (2011). The impact of internal integration and relationship commitment on external integration. Journal of Operations Management, 29 (1–2), 17–32. https://doi.org/10.1016/j.jom.2010.04.004

Zhu, Y., Quansah, P. E., & Obeng, A. F. (2022). High-performance work systems and safety performance in the mining sector: exploring the mediating influence of workforce agility and the moderating effect of safety locus of control. Current Psychology, 42, 25100–25126 (2023). https://doi.org/10.1007/s12144-022-03606-w

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The research was supported by the National Social Science Foundation of China (NSSFC;18BGL255).

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Timothy Amoako, Hao Chen, Stephen Abiam Danso & Edem Segbefia

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Amoako, T., Chen, H., Danso, S.A. et al. The Role of Top Management Involvement and Supply Chain Integration on Smes’ Innovation Performance: Moderation Impact of Firm Experience Capability. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02166-7

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Received : 15 April 2024

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DOI : https://doi.org/10.1007/s13132-024-02166-7

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Journal of Knowledge Management

ISSN : 1367-3270

Article publication date: 12 June 2020

Issue publication date: 10 August 2020

This paper aims to review for the first time existing research literature about the role of gender in creating, sharing and using knowledge in organizations and proposes a conceptual framework to guide future research directions.

Design/methodology/approach

Based on the systematic literature review method this study collects, synthesizes and analyses articles related to knowledge management (KM) and gender published in online databases by following a pre-defined review protocol. The paper analyses 41 papers published in peer-reviewed journals.

The role of gender in KM has been rarely addressed in KM journals and journals with specific emphasis on gender. The existing literature is fragmented, but existing research suggests that knowledge sharing might be influenced by gender. Based on the analysis and synthesis, a conceptual framework is proposed to guide further research on determining if gender matters in KM.

Research limitations/implications

Academic researchers should aim to include gender-related variables into their KM research to further explore if gender matters in KM.

Practical implications

The practical implication suggests that managers and knowledge managers should raise awareness about how stereotypes and gendered expectations about role behaviour affect how knowledge and experiences are created and shared within the organization.

Social implications

The authors believe that a better understanding of knowledge handling and gendered role expectations at the workplace could also have an impact beyond organizational boundaries.

Originality/value

The paper presents the first comprehensive systematic literature review of the article published on knowledge creation, sharing and usage and gender and provides a conceptual framework for future research.

• Knowledge sharing
• Knowledge management
• Systematic literature review

Acknowledgements

This research is supported by the Master’s Research Project Students 2017: Ms Ivanova, Ms Nzekou, Ms Tausch; and Mr Sovic.The authors like to thank our research students for their effort and discussions within this research project.Furthermore, this research was partly funded by the German Federal “Women Professors Programme in Higher Education” at the University of Applied Sciences – FH Potsdam.The authors like to thank the office of equal opportunities at the FH Potsdam for this support.

Heisig, P. and Kannan, S. (2020), "Knowledge management: does gender matter? A systematic review of literature", Journal of Knowledge Management , Vol. 24 No. 6, pp. 1315-1342. https://doi.org/10.1108/JKM-08-2018-0472

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