Knowledge management system implementation success: A social capital perspective

Abstract

BACKGROUND:

Organizations develop knowledge management (KM) strategies with the intention to leverage knowledge across all functional areas. A knowledge management system (KMS) is used to facilitate KM processes such as the creation, storage, and application of knowledge. However, mere adoption and deployment of KMS do not warrant its effective use to facilitate knowledge-sharing efforts.

OBJECTIVE:

This study investigates the facilitative role of social capital in the use of KMS by considering three social capital dimensions –cognitive (i.e., shared norms), relational (i.e., trust), and structural (i.e., social connectedness).

METHODS:

A case study strategy was used for this study. A mixed approach of qualitative and quantitative techniques was used to obtain relevant data for analysis. Instruments used to collect the data were semi-structured interviews and questionnaires. Documents regarding social policy, shared values, and shared goals were also obtained for triangulation purposes. A total of 15 respondents were interviewed while 73 respondents participated in the survey. A simple random sampling technique was used to select the participants and the survey data were analyzed using Pearson’s correlation, factor analysis, and multiple regression analysis.

RESULTS:

The study found that each of the three dimensions of social capital has a positive and significant relationship with the use of KMS. Specifically, shared norms (i.e., cognitive social capital), trust (i.e., relational social capital), and strong connectedness (i.e., structural social capital) were good predictors of employees’ use of KMS while user attitude and perceived usefulness mediate social capital dimensions in terms of KMS usage for knowledge exchange or transfer in the organization.

CONCLUSIONS:

The application of social capital theory to KMS context should be a very important consideration by both researchers and practitioners due to the socio-technical nature of KMS and the need to recognize social capital as a mechanism for inducement and opportunity to promote KMS usage for successful knowledge sharing.

Keywords

Social capital knowledge management system (KMS)knowledge sharing shared norms trust social connectedness

Gabriel Nyame is a doctoral student at the University of Electronic Science and Technology of China (UESTC). He received MPhil in Computer Science, MBA in Business Information Technology, MPhil in Development Studies, and BSc in Computer Science in 2013, 2011, 2006, and 2000 respectively, all from the Kwame Nkrumah University of Science and Technology, Ghana. He has publications in reputed international journals and co-authored ICT books for basic schools for the government of Ghana in 2005. His current research interests include social theory applied in knowledge management systems, knowledge management and innovation, and information systems policy and planning.

Mavis Adu-Gyamfi is a Ph.D. student at the School of Management and Economics of the University of Electronic Science and Technology of China (UESTC). She is a Chartered Professional Administrator (ChPA) and a Chartered Management Consultant (CMC). She received her MBA in Human Resource Management in 2016 from Swiss Management Centre University (SMCU), Zug –Switzerland and Bachelor’s Degree in Management studies in 2011 from the University of Education, Winneba, Ghana. She has publications in reputed international journals and has also co-authored other publications too. Her current research interests include corporate social responsibility, green human resource and knowledge management and innovation.

Ernest Kwame Ampomah is a Ph.D. student at the School of Information and Software Engineering of University of Electronic Science and Technology of China. He acquired MPhil Computational Science and Engineering from University of Ghana in 2014. He worked as a Lecturer at Christian Service University College in Kumasi, Ghana from 2016 to 2018. His current research focus is on data mining and intelligent financial systems.

1 Introduction

Organizations continue to increase their efforts to enhance knowledge-sharing capabilities through the use of knowledge management systems (KMSs). Knowledge management (KM) has become a key strategic area in advancing knowledge-value creation. To facilitate KM processes, a KMS is recognized as a vehicle to accomplish KM goals. The implementation of KMSs in organizations help to create, store, share, utilize, and communicate knowledge to strengthen and sustain the competitiveness of organizations [1, 2]. With the proliferation of advanced digital mobile devices, the use of KMSs by knowledge-intensive enterprises has significantly reduced the effects of scattered knowledge, especially tacit knowledge. This effect is as a result of the progressive and extensive research carried out by both researchers and practitioners in terms of design, development, implementation, and management of knowledge management systems. In the IS/KMS literature, it is common to find studies that focus on KMS success models, their uses, and benefits obtained in both social and business sense. Examples include the works by [3 –8].

It is important to emphasize that a KMS that does not yield the anticipated benefits and to increase organizational performance is not worth its investment (e.g., knowledge infrastructure). According to Markus [9], an important concern to organizations is the ability to get employees to use knowledge management systems successfully to improve the performance of the organizations. Considering the socio-technical nature of KMS, the big question that still confronts both researchers and practitioners relates to how organizations can guarantee effective use of KMS to increase value creation.

By the theory of organizational proximity and in the context of digitalization, system users interact and communicate through information technology (IT) or the use of information systems such as a KMS. The relationship so established is dual in context where users interact indirectly through IT or direct communication through other means such as in-person communication. Any of these forms of interaction are avenues to exchange or transfer knowledge from a source to a recipient. The use of KMS involves social interaction in which people are able to share what they know that others do not know. Though instances of in-person knowledge sharing promote organizational social ties, relational capital, and reciprocal relationships [10, 11], it is imperative for these social factors in aggregation to impact knowledge creation and knowledge internalization. In essence, knowledge seekers should be able to find the right knowledge from the right knowledge owners in the right context.

There are many facets of social capital theory and they include norms, shared values, and attitudes pertaining to individuals, groups of people, or teams. One important aspect of social capital that this study recognizes is social relationship. Organizational social relationship is the core of social capital in KMS context. It provides the basis to understand the need for employees to use KMS to advance the value of knowledge and increase intellectual capital.

From the perspective of KMS, structural social capital relates to the network of KMS users and their access to available resources including knowledge resources. It draws on the organizational structure, which is naturally relational and hence tangible. Unlike structural social capital, cognitive social capital deals with the subjective interpretations and analyses of shared understandings by individual users and teams within the organization. This characterization also hinges on users’ understanding of the value of knowledge and need to willfully exchange or share knowledge for mutual benefits to impact organizational performance. Relational social capital emphasizes shared trust among users or groups involved in knowledge sharing. Users’ expectation to interact with others effectively depends on the nature and trustworthiness of network ties intended to enhance people’s attitudes and behaviors towards knowledge sharing. The structural social capital dimension is antecedent to both the cognitive social capital and the relational social capital dimensions but it is reinforced and motivated by the structural social capital dimension. Such inclination leads to the interaction and formation of new relationships, procedures, rules and roles [12]. Given this notion, both cognitive social capital and relational social capital provide a good premise for knowledge exchange, considering the shared values and quality of relationships within teams or the organization as a whole. Strong interconnectedness between users and their shared vision, as well as the quality of such interconnectedness describe the concept of organizational social capital [13].

The study objectives that we explore according to the three dimensions are as follows: relational capital, cognitive capital, and structural capital proposed in 1998 by Janine Nahapiet and Sumantra Ghoshal [14]. This study recognizes trust, shared norms, and social connectedness of the three dimensions respectively as pivotal to analyzing KMS usage for knowledge sharing in organizations. These social factors help to better understand the broader scope and complex nature of social capital concept as envisaged in the context of knowledge management systems.

2 Related works

2.1 KM and KMS

Knowledge management (KM) has evolved to integrate with information system (IS) functions to facilitate KM processes and strengthen functional interrelationships among people in an organization. Most KM models focus on issues relating to an organization’s ability to capture, store, and use/reuse all forms of knowledge whether tacit, explicit, or embedded. For instance, the SECI model by Nonaka & Takeuchi [15], the Zack & Meyer KM model by Zack & Meyer [16], the Wiig KM model by Wiig [17], and Bukowitz and Williams KM by Bukowitz and Williams [18] are commonly found in the KM literature. These recognized models bear common characteristics in terms of knowledge creation, storage/retrieval, sharing/transfer, use/reuse, and distribution/presentation. The sharing of tacit knowledge between participating individuals in either formal or informal means relates to relational social capital and cognitive social capital. Also, expressing tacit knowledge in explicit concepts and integrating them into a networked knowledge system relates to structural social capital.

Moreover, organizing knowledge is fundamental to understanding the usefulness and value of knowledge. Knowledge is expected to be complete and well connected to enable people to understand its purpose and what it intends to provide for clear internalization. As more people participate in knowledge sharing or transfer, they build trust through ties and proximity bounded on relational social capital. The challenge is the codification of tacit knowledge into explicit concepts accessible by others who do not have such knowledge or expertise. Thus, a KM strategy incorporates a KMS to help facilitate KM processes in the context of structural social capital, relational social capital, and cognitive social capital. Like other information systems, Leidner & Alavi [1] explained that a KMS helps people to create, share, transfer, and use knowledge as well as promoting collaboration and social interaction. Such KM efforts lead to the development of knowledge networks and well-integrated knowledge repository. He, Qiao, & Wei [19] thought of a KMS as one that supports and improves organizational processes in terms of knowledge creation, sharing, and utilization. However, the passion and intent to use a KMS for its intended deployment depend on the behavioral intention of the people in the organization and their inclination to the value and quality of the KMS.

Researchers and practitioners continue to study and investigate individual users’ intention to use IS in general, and KMSs in particular. Sustaining users’ intention to use a KMS depends on the level of satisfaction and benefits obtained from the persistent use of the system. As evidenced in the study by Bhattacherjee [20], both users’ beliefs and attitudes much influence their intention to either use or not use a KMS. As users become more satisfied in using a KMS with time, they develop stronger attitude towards system usage and hence have continuous intention to use the KMS to create new knowledge for improved job performance [21]. Despite the variance in sustained use of a KMS across different users, social factors in specific contexts are so important to explain and improve these existing dynamics. For instance, organizational, social, and people’s self-interest contribute to such variance in users’ intention towards knowledge-sharing efforts. Thus, a KMS is a socio-technical system in which the social component emphasizes collaboration through relationship –a critical element of social capital theory.

2.2 Social capital perspective

The concept of social capital (SC) stems from people’s capacity to generously act and embrace others in a cooperative manner. It emphasizes primarily social structures and the existence of social relationships in which there are usually anticipated positive relationships between people guided by trust, reciprocity, and respect. In the social capital perspective, there are greater concerns about the social context relating to productive human elements and the capital context relating to the expected benefits from sociability [22]. The question that remains critical for both researchers and practitioners to answer is “what social relationships in a social organization can yield productive benefits and how will that be achieved?” Attempts to minimize suppressive behaviors and to encourage supportive social structures and relationships are important consideration by many social capitalists. Like any economic resource, the benefits derived from social capital depend on the nature of social dimensions’ mappings suitable for a particular social organization. Thus, how social capital is used determines the predictive productive benefits of social capital.

According to Pretty & Ward [23], social capital consists of relation of common rules, connectedness, trust, norms and sanctions, and reciprocity within an institution. It is context-specific and as a result possesses scalable dimensions. Nahapiet & Ghoshal [14] considered the dimensions such as relational, cognitive, and structural as inherent in social capital and a function of social relationship. Importantly, social capital facilitates knowledge sharing and increases knowledge-value creation. In a similar context, Weil & Putnam [24] referred to social capital as the features of a social organization that facilitate coordinated actions to increase the efficiency of a social group or an organization. Unlike physical capital, social capital involves network connotations and norms of reciprocity that produces value or returns which may be positive or negative [25]. Interestingly, social capital is dynamic in nature and more heterogeneous based on the forms and types they take in a social group or organization.

As regards social relationship, an important characteristic of social capital is the potential to either bridge or bond social entities for value creation. Social relationship plays an important role by improving the efficiency of the social group or organization through connectedness, trust, and reciprocity. In KM, such connectedness for collaboration and knowledge sharing or transfer results in new knowledge creation while at the same time builds stronger ties or connectedness, trust, and shared norms across the organization. Thus, we acknowledge and agree with the three dimensions of social capital –relational, structural, and cognitive social capital identified by Nahapiet & Ghoshal [14] to better understand social capital in knowledge sharing and management context.

2.3 The role of social capital in KMS

In KMS, the role of the three dimensions of social capital is fundamental to deepening the understanding and principles of knowledge sharing and transfer within an organization. Practically, there are sophisticated interrelationships between these important dimensions –relational, cognitive, and structural social capital [12]. Yet, analyzing distinctively each of these dimensions conceptually, especially in KMS context helps to build stronger collaboration between people and resources.

2.3.1 Structural social capital

Structural social capital (SSC) defines network of resources and people available to strengthen social interaction and promote strong social ties for effective collaboration. The term technically refers to the degree of connectedness between people and the available resources to enhance collaborative efforts for successful knowledge sharing. It is quite tangible because of its relatedness to organizational structure, rules of engagement, procedures, and roles [12, 14]. Structural social capital is quite observable and pivotal to explaining the intricacies of the other dimensions. By default, organizations outline processes and structures based on roles, standard operating procedures, and chain-of-command that bring people or units together for interaction to accomplish work goals.

In KMS context, structural social capital involves people and resources and how people use technology-based tools to effectively share knowledge across the organization. The use of technological tools through KMS goes beyond individual, group or organizational boundary to increase social ties in observable manner. It brings to the “unknowns” the “knowns” and a level of familiarity with the social environment where actors recognize the mutual benefits through social interaction. The denser the social relationships among people, the higher the tendency to identify subject matter experts and the knowledge obtainable from them. Thus, KMS facilitates and increases network connectivity of relationships in which a person can have multiplicity of ties with others in a group, a project team, or an organization [26]. Further, actors such as knowledge producers can share or transfer knowledge when conditions such as closure, interdependence, time, and interaction favor social capital development, especially structural social capital. Interaction through a knowledge-sharing platform and interdependence relating to statuses, roles, and procedures can well support social capital and individualized identification (i.e., sense of belonging) to enhance the capacities of both the knowledge producer and the knowledge recipient.

From the perspective of the social capital theory, network ties enable people to have access to resources through interconnectedness and further enhance knowledge exchange as well as recognizing the value of such social interaction. The nature and strength of the ties is characterized by closeness and the frequency of interaction between knowledge contributors and knowledge seekers. This suggests that strong and trusted ties improve knowledge sharing and transfer in the organization. If people (such as subject matter experts) are accessible and have the willful desire to share what they know, then there exist strong ties. Thus, KMS supports the development of social capital formation across the organization.

2.3.2 Cognitive social capital

Cognitive social capital (CSC) relates to individualized interpretations of shared norms, language, narratives, and systems among members in a social group that promotes benefits accruable through social exchange [14]. It involves the interpretations and understandings of shared values and reality. The creation of a common language for social relations implies flexible means to exchange knowledge to further create new knowledge. People are thus able to access others including the knowledge they possess. It also becomes so easy to assess benefits through exchange and configuration or combination. However, instances where language differ, network ties are difficult to function because people are seen separated and restricted. Besides, cognitive social capital may relate to some shared vision, culture, or goals [27]. Whether common shared language and code, culture, or vision, the capability of parties to communicate and exchange what matters to them for increased benefits and innovation is reflective at both individual and organizational levels. It is important to stress that these shared elements (i.e., norms, culture, values, language, and goals) influence the behavior, perceptions, and judgments of members in the social group.

The use of KMS for knowledge sharing embodies the cognition of the knowledge contributor and the knowledge recipient. For effective knowledge exchange, there has to be overlap in possessed knowledge between the sharer and the seeker in terms of the sharer’s capacity to contribute knowledge to meet the knowledge seeker’s predisposition and absorptive capacity. Based on the shared elements, the knowledge consumer is expected to have the right capacity to develop, combine, and internalize concepts as an effort to advance new knowledge. Essentially, cognitive social capital is the basis for communication that brings benefits of knowledge exchange [28]. Hence, cognitive social capital reflects the rationale for developing most knowledge management systems because of the shared goal or vision to utilize a common platform to exchange knowledge for mutual benefits while at the same time leveraging knowledge across the organization.

2.3.3 Relational social capital

Relational social capital (RSC) refers to the trust-based resource exchange capability between people in a social group or organization. In a well trusted knowledge-sharing environment, people have the desire and willful power to build on the expertise and ideas of others to further enhance organizational knowledge. Trust is therefore central to understanding the quality of interactions and relationships within any form of knowledge exchange or transfer that takes place. For instance, Ridings, Gefen, & Arinze [29] established that trust is a good predictor of people’s intension to share or exchange knowledge, especially at the downstream in a virtual community. If people perceive others to have responsive relationship to their understanding and readiness to exchange knowledge, trust increases and can have an upstream effect. The more people interact and share what they know, the greater the trust build to promote effective collaboration. This eliminates the fear of a person losing value through knowledge sharing [30]. Obviously, knowledge transfer or sharing becomes less costly as there might be reduced doubts and conflicts between individuals participating in knowledge sharing. However, build-up trust better facilitates knowledge sharing and internalization when actors possess quality personal relationships. Qualities such as respect for others, obligation, cordial friendship, reciprocity, and identification are important characteristics of personal relationships embodied in relational social capital [28, 31]. Thus, relational social capital focuses on the nature and quality of the social network expressed in shared norms, identity, and interpersonal trust to improve knowledge sharing.

A trust-based relationship is a prerequisite to users’ access to new knowledge and motivates people to develop positive intension to use a knowledge sharing system. We argue that the effects of social capital on knowledge-sharing success through the use of KMS pivots on analyzing the nature and quality of responsive relationships motivated and reinforced between actors within the organization. According to Bourdieu [32], relationships are formed through social exchange and social capital is inherent in relationships. Social relationship is perceived to be expressible as a continuous process, and hence requires a persistent disposition to commit and respond positively towards knowledge exchange.

In KMS, users’ readiness, preparedness, commitment, and motivation to continuously interact to share knowledge are not only based on shared norms and identity, but also their ability to recognize the value of trust in their connectedness with others on a sharing platform. We argue that social relationship is perceived to contribute to people’s intension to use KMS in organizations. Thus, perceived social relationship is the presence of a social interaction in which a person has perceptions about other users of the KMS within the organization. By this notion, relational social capital (i.e., trust-based interaction), structural social capital (i.e., tie-based interaction), and cognitive social capital (i.e., shared norms-based interaction) characterize social relationship, and hence social capital.

2.4 Interplay of structural, cognitive, and relational social capital dimensions

Practically, there are complex interrelationships between these dimensions based on their social dynamism and varying organizational practices and culture. Given similar practices and culture among organizations, the interplay between these dimensions can still vary based on the motivation and reinforcement within the social systems of organizations.

Motivation relates to the feeling of trust that empowers individuals to partake in social interactions on the basis of perceived value from such social interactions. Structural social capital is inherently relational through a network of individuals’ positions, roles, and resources. Such a network involves responsive interrelationships motivated by trust and to further reinforce the interaction of the elements of the structural social capital dimension. From Fig. 1, the cycle (1 ⟶ 2 ⟶ 4 ⟶ 1) illustrates a trust-based interaction. Thus, between structural social capital and relational social capital, the increase in network ties is a recipe for increase in trust and trustworthiness [25, 33].

Fig. 1

Interplay of the social capital dimensions.

Reinforcement relates to the support and benefits obtained through knowledge exchange based on shared norms. Based on the cognition of individual actors, interpretations of the quality of social interaction are subjective, and have to be shaped by some shared norms and understandings. Norms of reciprocity, respect, and trust motivate individuals to willingly connect with others and to establish stronger organic solidarity that reinforces stronger network ties. Also, from Fig. 1, the cycle (1 ⟶ 3 ⟶ 5 ⟶ 1) expresses a shared norm-based interaction. Therefore, between the structural social capital and cognitive social capital, the increase in network tie strength is much determined by well-developed shared norms such as language and codes and reciprocity to facilitate knowledge transfer or sharing success.

As noted by Claridge [12], structural social capital is material-oriented, observable through network ties, and tangible while both cognitive and relational social capital are mentally-oriented or intangible, and subjected to individuals’ opinions and perceptions. Interestingly, relational social capital and cognitive social capital seem similar in cognitive framework and understanding but vary in terms of feelings of trust and subjective interpretations among actors in a social group. However, they are founded on the principles of structural social capital. Figure 1 shows the interplay of the three dimensions.

In KMS, social capital reflects the interconnectedness, trust, and shared values or goals between users for effective knowledge-sharing efforts. The nature and quality of the connectedness among users increase the feeling of trust to share and transfer knowledge willingly, and to realize the benefits of the common shared vision. A weaker user connectedness implies a lack of self-worth to trust connecting with others and to fulfill the rationale for implementing KMS to leverage knowledge across the organization. Thus, we argue that trust, strong connectedness, and shared norms are the key attributes of the three dimensions of social capital in both social and KMS contexts.

3 Methodology

3.1 Research design

We employed a single case study strategy to allow for contextual and in-depth examination of the subject for this study. A qualitative approach was used to obtain detailed information about whether or not social capital influences users’ use of KMS in the organization as well as the usefulness of KMS. Semi-structured interviews and questionnaires were used to collect data from the participants. To triangulate the data obtained through the interviews, we obtained documents on social policy, shared values, and goals. Further, an online survey was carried out to seek the opinions of KMS users as a way to clarify some questions asked during the interviews. A simple random sampling technique was used to select the users for the survey. Items in the interview guide, (see Appendix B), were based on the existing literature. Definitions of the constructs such as attitude, trust, strong connectedness, shared norms, system usage, and usefulness are shown in Table 1.

Table 1
Operational definitions of constructs

Constructs Definitions References

Attitude The ability of users to have affective reaction to the use of KMS. [21]

Perceived usefulness The believe that using KMS enhances job performance. [21]

KMS usage The frequency of using KMS [45]

Strong connectedness The degree to which users are strongly connected. [24, 25]

Shared norms The presence of norms intended to facilitate knowledge sharing [45]

Trust The degree to which users believe in the good intent, competence, and reliability of other people [45]

Constructs	Definitions	References
Attitude	The ability of users to have affective reaction to the use of KMS.	[21]
Perceived usefulness	The believe that using KMS enhances job performance.	[21]
KMS usage	The frequency of using KMS	[45]
Strong connectedness	The degree to which users are strongly connected.	[24, 25]
Shared norms	The presence of norms intended to facilitate knowledge sharing	[45]
Trust	The degree to which users believe in the good intent, competence, and reliability of other people	[45]

3.2 Company profile

Alpha company (which is a pseudonym) is a pharmaceutical company located in one of the industrial cities in Ghana. The company was established in 2005 with a primary mission of providing sustainable and quality healthcare to the people of Ghana and beyond. Its core mandate is the production of various medications such as anti-retroviral (ARV) drugs to combat the healthcare challenges facing the country in terms of the supply of quality drugs to improve the health of people. It has approximately 1,025 employees including casual and marketing representatives at the downstream. The company has regional offices in 9 of the 16 regions in Ghana.

With increase in innovation, technology, and growth, the company continues to increase its production base of drugs including newer products for both the domestic and international markets. In 2016, Alpha company rolled out a KMS project to enhance collaboration with strategic partners as well as coordinating business plans and internal activities in many different departments and units. This attempt was to facilitate communication, transfer, and sharing of individuals’ skills, experiences, and knowledge on specific subject areas. For KM practice, the company is revered as one of the adopters of KMS and its implementation in the pharmaceutical industry in Ghana.

As part of the KMS design and implementation strategy, phase one of the project focused on the development of knowledge portal where there was an integration of office automation systems and those of the functional business systems. The setup of the portal made resources, roles, standard operating procedures, and other people available for engagement. Phase two addressed the implementation of the key system modules consisting of knowledge-sharing platform, knowledge map, and knowledge repository to facilitate knowledge creation, storage, retrieval, transfer, and sharing. The last phase, which is phase three, primarily focused on promoting a network of subject matter experts to provide real-time project solutions.

3.3 Data collection

Due to the global COVID-19 pandemic, Skype, Zoom, and telephone (where appropriate) were used to collect data from the respondents. However, prior to the interviews, we collected documents on company values, practices, rules, system information, and sharing protocols to have a clearer understanding of what the company aspires to achieve using KMS in both social and technical contexts. This was done in March 2020. The interview questions and items in the questionnaire were pilot tested using three Office Assistants and four end users to clarify any anomalies, and this helped to reshape the questions. Respondents were assured of the anonymity of the responses they would provide before the interviews and administration of the questionnaires. Between April and June 2020, 15 respondents from different departments were interviewed and they consisted of the forum specialist, the Chief Knowledge Officer (CKO), the KMS project leader, three project team members, and nine end users.

It was necessary to obtain additional data regarding the perceptions of employees. To achieve this, we conducted an online survey using questionnaires between August and September 2020. The questions focused on employees’ attitude and behavior on the use of KMS, network ties, shared values and norms, trust, and the perception about system usefulness. In essence, the questions for the survey were based on the construct definitions in Table 1. Thus, connectedness, shared norms, and trust are important elements of social capital identifiable to facilitate knowledge sharing and transfer. We sent 150 questionnaires to KMS end users and 73 fully answered the questions representing a response rate of approximately 49%.

3.3.1 Validity and reliability measures

For construct validity and reliability concerning the interview items, we developed a draft case study report based on evidence from different sources which was made available to the key informants. Further, the evaluation reports and system log files including archives helped to triangulate the findings for optimum conformity. Also, we used a pattern-matching method to ensure internal validity.

Psychometrically, we measured the properties of the scale to ensure the validity of the questionnaire items [34, 35]. To test the validity of the items, we used principal component analysis and varimax rotation where factor loadings’ significance was set to the practical and acceptable threshold value of 0.6 or greater [36]. There was a distinctive loading of each item on another. The items loaded high on their respective factors. This indicated high discriminant and convergent validity. The results of the factor analysis produced six factors which accounted for 78.52%of the total variance. Hence, the six factors corresponded with the six constructs used for this study. Table 2 shows the validity of the measurement scale.

Table 2
Validity of questionnaire items

Items 1 2 3 4 5 6 Eigen Variance Cumulative

Items 1 2 3 4 5 6 value Explained (%) Variance (%)

ATT1 0.767 3.132 17.54 17.54

ATT2 0.758

PU1 0.810 3.408 15.32 32.86

PU2 0.793

PU3 0.714

PU4 0.682

KMSU1 0.934 2.662 12.89 45.75

KMSU2 0.883

TRST1 0.865 2.490 12.76 58.51

TRST2 0.848

TRST3 0.797

SNRM1 0.801 2.128 10.13 68.64

SNRM2 0.785

SNRM3 0.642

STRCON1 0.931 2.017 9.88 78.52

STRCON2 0.834

STRCON3 0.799

Note: Only factor loadings of at least 0.60 are shown.

For the reliability of the instrument, Cronbach’s alpha values were computed and the alpha values of the items ranged from 0.702 for attitude to 0.865 for strong connectedness. Each construct’s reliabilities did not exceed the acceptable threshold value of 0.7. Table 3 shows the reliability measure of the constructs.

Table 3

Reliability of the measurement instrument

Constructs	N	Mean^a	Standard deviation	Reliability (alpha)
ATT1	73	2.753	0.644	0.702
ATT2		3.145	1.613
PU1	73	4.181	0.571	0.843
PU2	73	3.901	0.834
PU3	73	3.539	0.751
PU4	73	3.212	0.334
KMSU1	73	4.112	1.594	0.789
KMSU2	73	3.091	1.110
TRST1	73	3.884	1.552	0.821
TRST2	73	4.460	1.679
TRST3	73	3.821	1.533
SNRM1	73	2.078	0.732	0.773
SNRM2	73	3.442	0.812
SNRM3	73	3.635	0.739
STRCON1	73	3.702	1.663	0.865
STRCON2	73	3.229	1.231
STRCON3	73	4.211	0.872

Notes: ^aLikert-type scale anchors between 1 (i.e., strongly disagree) and 5 (i.e., strongly agree). ATT = Attitude; PU = Perceived usefulness; KMSU = KMS usage; TRST = Trust; SNRM = Shared norms; STRCON = Strong connectedness.

3.4 Data analysis

The recorded interviews were transcribed. However, there were a few portions of some of the recordings that were difficult to interpret and understand due to poor network connectivity, low sound quality, and freezing videos during the interviews. These snags did not have impact on the overall emerging patterns and themes from the interview transcripts. Further, we performed inductive, thematic analysis on the data to identify the relevant patterns and obtain the main themes regarding the effects of the three dimensions of social capital on the use of KMS. As noted by Braun & Clarke [37], thematic analysis is one important and flexible means to obtain themes for qualitative study. For the coding process, we used an open coding scheme for iterative purposes and interpretative analysis. NVivo 12 (QSR International) was used to do the coding and collating of the transcriptions in a systematic manner. The coding process was not limited to the researchers’ theoretical assumptions since the open coding scheme allowed for iterative, dynamic, and evolving analysis. We transcribed the codes one line after the other and collated in each code using the NVivo software. Further, we categorized codes with repeatedly emerged patterns into sub-themes by running matrix coding queries, that is, codes having similar or closely similar in meanings were put into groups. These were subsequently aggregated, well-defined, and labeled as the main themes based on the content. To ensure the validity and accuracy of the emerged themes, each researcher coded the interview transcripts separately. After evaluating and discussing the outcomes from each researcher, a consensus was reached on the key themes that reflected the three dimensions of social capital and their effects on KMS usage. The coding process, together with the categorization of the codes into various themes, was done in conjunction with a senior faculty member who ensured that there were no coding overlaps among the codes as well as resultant themes.

In addition, we analyzed the survey data quantitatively using SPSS version 25. Means, standard deviations, and independent samples t-test were computed and Pearson correlation analysis (bivariate correlation) was also done. These statistics were relevant to explain the influence of the three dimensions of social capital on KMS usage. For instance, it was necessary to test whether or not the attitude of users towards the use of KMS is much influenced by social capital, primarily based on trust, shared norms, and strong connectedness.

4 Results

4.1 Descriptive statistics

Out of 73 respondents who participated in the survey, 67.1%were males and 32.9%were females. The dominant age groups among the respondents were 20–29 years and 30–39 years representing 32.9%and 42.5%respectively. These age groups are indicative of users understanding of the need to collaborate and share knowledge, perhaps motivated by their shared norms, organizational vision, and what they represent in the organization. However, only one person was below 20 years. Further, a total of 80.8%of the respondents had graduated from the university and this represents both Undergraduate and Masters degrees holders. This level of academic background favors users’ propensity to accept and use KMS in the organization.

Also, on average 73.9%of the respondents had at most 7 years work experience, indicating greater familiarity with the available technology infrastructure including KMS. With regard to KMS usage, 22 (30.1%) and 30 (41.1%) had had quite long years of using the existing KMS between 1–2 years and 3–4 years respectively. Remarkably, 12 (16.4%) had used the KMS for less than 1 year while 9 (12.4%) had used it for over 5 years. This suggests that extrinsic motivators and organic solidarity encourage more user participation in using the KMS. Table 4 depicts the demographic information of the respondents.

Table 4
Demographic characteristics

Variable Item Frequency (N = 73) Percent (%)

Gender Male 49 67.1

Female 24 32.9

Age Under 20 years 1 1.4

20 –29 years 24 32.9

30 –39 years 31 42.5

40 –49 years 13 17.8

Over 50 years 4 5.4

Academic qualification Doctoral 2 2.7

Masters 23 31.5

Undergraduate 36 49.3

Diploma 11 15.1

High school 1 1.4

Work experience Under 2 years 3 4.1

2 –4 years 25 34.2

5 –7 years 29 39.7

8 –10 years 10 13.8

Over 10 years 6 8.2

Use of KMS Under 1 year 12 16.4

1 –2 years 22 30.1

3 –4 years 30 41.1

Over 5 years 9 12.4

Variable	Item	Frequency (N = 73)	Percent (%)
Gender	Male	49	67.1
	Female	24	32.9
Age	Under 20 years	1	1.4
	20 –29 years	24	32.9
	30 –39 years	31	42.5
	40 –49 years	13	17.8
	Over 50 years	4	5.4
Academic qualification	Doctoral	2	2.7
	Masters	23	31.5
	Undergraduate	36	49.3
	Diploma	11	15.1
	High school	1	1.4
Work experience	Under 2 years	3	4.1
	2 –4 years	25	34.2
	5 –7 years	29	39.7
	8 –10 years	10	13.8
	Over 10 years	6	8.2
Use of KMS	Under 1 year	12	16.4
	1 –2 years	22	30.1
	3 –4 years	30	41.1
	Over 5 years	9	12.4

4.2 KMS usefulness

The perception of users towards KMS usefulness in terms of quality content and supportiveness influence their attitude and behavior in using the system. It was observed that there was a steady trend where social influence impacts the use of KMS. Users expressed feelings of trust, strong connectedness, and shared norms during their interactions with others when using KMS. Employees in Alpha Company frequently used the KMS to share knowledge, especially among project team members. As an evidence from the interview transcripts, a user in the R&D Department commented as

“Our sharing platform has been very helpful and I am able to share what I know confidently when a colleague faces some difficulties in solving a problem. . . . . . ...and so I think others do.” (User_R&D, 12/04/2020, interview)

As regards access to relevant documents and solution-based reports, employees retrieved them from the knowledge repository of the system to enable them carry out their job functions. This was common among project team members who mostly shared information to collaboratively achieve their project goals. Besides, users regularly accessed mainstream administrative documents, procedures, project proposals, and product development plans. A user from the Sales and Marketing Department gave the following comment.

“I obtain product distribution information from the system and this helps me to track contact points for all distributions made.” (User_S&M, 05/05/2020, interview)

Instances where a subject matter expert has to be contacted to clarify the use of a ‘best practice’ or need for additional information, a knowledge map was used to address such needs. It was, however, found that some knowledge packages lack appropriate formats and contexts, and hence affect knowledge nurture and internalization.

4.3 Trust-based relationship

Trust is so vital in increasing knowledge-sharing efforts of KMS users. The study found that users valued and respected each other’s contributed knowledge or idea and importantly trusted the sources of every knowledge through the system. Thus, the level of trustworthiness among users was so remarkable, indicating a sign of connectedness and strong relationship for positive knowledge-value creation. This was evident in a comment made by the CKO.

“I think we have grown in trust through our persistent interactions and feel well connected as a family guided by our common beliefs, values, experiences, and shared norms. . . . . . ...Of course, we do have a few challenges perhaps due to in-person contacts, but our primary aim is to reap the benefits of knowledge sharing.” (CKO, 13/05/2020, interview)

Given the periodic reports and targets expected from the sales and marketing team, users found the system more convenient to exchange knowledge rather than using the traditional telephone, especially when it is necessary for more salespersons to brainstorm on some sales strategies. One project and marketing team member remarked as follows.

“I feel very motivated by the coordinated actions through the system; it really makes me feel part of this successful and trusted team.” (User_PT&M, 25/05/2020, interview)

Thus, collaboration between system users was observed to be the pivot of knowledge-sharing success for most teams in the organization. Mutual trust was the motivational factor in getting more people to participate in knowledge sharing.

4.4 Shared norms and beliefs

A fertile-sharing environment is reflective of strong organizational culture. Alpha Company has well-established culture highly respected by individual employees, teams, and top management. As a result, there was higher collaboration that puts the interest of the company first rather than individual interests. High performing teams and individuals received citations as well as appear in the monthly bulletins of the company. Individuals and teams therefore strive to increase their job performance in order to merit such commendations. The KMS project team leader made the following remark.

“You know, as far as I am concern, I think that the work practices, engagements, procedures, and social interactions encourage me to focus on my targets for the period, and further minimize any act of culture of silence.” (KMS_PTL, 30/05/2020, interview)

Evidently, all the participants agreed to their commitment to using the KMS to enhance knowledge sharing. However, the study found that the extent of obligation and identification varies between individuals and teams. One user gave the following comment.

“Honestly, I don’t use the system very often. . . . . . ...I access it only when I have difficulty in solving some job-related tasks or need to clarify some doubts about a production process.” (User_KMS, 30/05/2020, interview)

4.5 Strong connectedness

The network of relationships between users in using the KMS was a remarkable one. The presence of strong ties supported by trust and well-defined learning culture helped to sustain users’ attitude and behavior in using the KMS. Majority of the people had significant experience in using the KMS (see Table 2), indicating their familiarity and feeling of closeness and connection to others in the KMS environment. There was strong interactive support between users. This positive effect of interrelationships enhanced acceptance of new ideas and effective knowledge sharing. From the interview transcripts, it was clear that users used other colleagues as a yardstick to interpret and comprehend their individualized approach and attitude towards knowledge-value creation. One user expressed her strong ties with others in a related department as

“In fact, I feel so connected to others and I think it is helpful for me to seek what knowledge I need from another department through the KMS.. . . . ...but mostly I am guided by the principles and approaches of other knowledgeable people in our company.” (User_KMS, 09/06/2020, interview)

4.6 Organizational support

People might refuse to use the KMS regularly if they realize no difference when the KMS had not been deployed. The success of KMS adoption and usage requires efforts by management. It is not limited to informing people about the knowledge-sharing intent of the new system but includes how well management encourages individuals and teams to develop positive mindset towards its persistent usage. For instance, the company evaluated individuals and teams based on their contributions in terms of quality knowledge content and reusability within the company. When employees feel that their contributions are valuable to the organization, they are further motivated to increase knowledge-sharing efforts to improve knowledge creation. It was found from the interview data that some rewards included 1) the most insightful and impactful new knowledge, 2) the most contributed subject matter expert, 3) the most coordinated and collaborative team for sharing project information, and 4) the most sourced knowledge for knowledge internalization. Additionally, there were tokens of remunerations for each of these rewards given to individuals and teams. One project team member gave the following comment.

“In 2017, I received a reward for introducing a new formula for the production of a variant of the “generic” antiretroviral product. You know, this was added to the knowledge repository.” (PMember_PT, 03/06/2020, interview)

To further increase the number of users to use the KMS, the study found that management invited expects to offer training aimed at developing the skills of individuals from one department or unit to another at different time periods once every year. This attempt was to clear doubts about employees who did not recognize the value of KMS and the support it provides for communications and collaboration. An evaluation report of the company’s staff development and training report document indicated an increase of 7%in KMS usage annually (Source: Alpha Company Training and Development Report, 2018).

In addition, respondents recognized management’s support and commitment to continuously invest in knowledge infrastructure and architecture for KM activities, and this partly contributed to their positive behavior towards KMS usage. One of the respondents remarked as follows.

“I think the implementation of the KMS has been so helpful, and I am much inspired by our CEO who also uses the KMS to communicate with us.” (User_KMS, 18/06/2020)

4.7 Interrelationships between the social capital dimensions

For easy interpretation and understanding of the interrelationships between the three dimensions of social capital illustrated in Fig. 1, we prefer to use the terms structural social capital (SSC) for strong connectedness, relational social capital (RSC) for trust, and cognitive social capital (CSC) for shared norms and values. A Pearson correlation coefficient was computed to determine the relationships between the three dimensions of social capital and KMS usage.

For the trust-based interaction cycle, the results show a positive and statistically significant relationship between SSC and RSC (r = 0.881, p < 0.01). This indicates that SSC is antecedent to RSC but the extent of interaction depends on the level of motivation and the degree of reinforcement. Also, with regard to the shared-norm interaction cycle, SSC has a positive and statistically significant relationship with CSC (r = 0.732, p < 0.01), indicating that network ties are resources to developing stronger shared norms, values, and visions. Moreover, SSC has a positive and statistically significant relationship with KMSUSG (r = 0.897, p < 0.01), which indicates that the stronger the network ties between people, the higher the tendency for people to use the KMS to share useful knowledge. In addition, the relationship between RSC and CSC was found to be moderately positive and statistically significant (r = 0.459, p < 0.01). This suggests that the interaction may be weakened by other factors relating to trust and respect between individuals or groups and unclear language, norms, and values. Also, RSC correlated positively with KMSUSG and the correlation was statistically significant (r = 0.712, p < 0.01). An increase in trust between individuals and teams is likely to drive on KMS usage to increase intellectual capital for the organization. Further, there was a positive and statistically significant relationship between CSC and KMSUSG (r = 0.664, p < 0.01). This suggests that shared norms and values play a role in people’s willingness to use KMS to share or transfer knowledge. Table 5 shows the descriptive statistics and correlations of the three dimensions.

Table 5
Descriptive statistics and correlations

Descriptive statistics Correlations (n = 73)

Variable Mean SD SSC RSC CSC KMSUSG

SSC 3.714 1.255 1 0.881^ 0.732^ 0.897^

RSC 4.055 1.588 1 0.459^ 0.712^

CSC 3.052 0.761 1 0.664^

KMSUSG 3.602 1.352 1

Descriptive statistics	Correlations (n = 73)
SSC	3.714	1.255	1	0.881^**	0.732^**	0.897^**
RSC	4.055	1.588		1	0.459^**	0.712^**
CSC	3.052	0.761			1	0.664^**
KMSUSG	3.602	1.352				1

^**Correlation is significant at the 0.01 level (2-tailed). Note: SSC = Structural social capital; RSC = Relational social capital; CSC = Cognitive social capital; KMSUSG = KMS usage

Additionally, we performed regression analysis to test the effects of the three dimensions of social capital including system usefulness and user attitude on KMS usage. The R statistic value of 0.756 showed a high degree of correlation between KMS usage and the predictor variables (trust, strong connectedness, shared norms, attitude, and usefulness). Also, the R² value of 0.682 showed that the predictor variables explained about 68%of the variabilities in KMS usage. Further, the results indicated a statistically significant F statistic (F (1, 73) = 24.831, p < 0.01)). This suggests that the predictor variables significantly explain the dependent variable (i.e., KMS usage). Hence, the predictor variables impacted 68%explanation of the variabilities in the KMS usage. From Table 6, the regression results show that all the predictors: trust (t = 6.258), strong connectedness (t = 6.419), shared norms (t = 5.574), attitude (t = 6.422), and system usefulness (t = 7.276) significantly influence KMS usage. Also, strong connectedness (β= 0.295, p < 0.01), trust (β= 0.248, p < 0.01), system usefulness (β= 0.188, p < 0.01), user attitude towards KMS (β= 0.203, p < 0.01), and shared norms (β= 0.221, p < 0.01) were significant predictors of KMS usage. The results suggest that the three dimensions of social capital have a better performance index of KMS usage compared with attitude and system usefulness. Thus, the three dimensions of social capital act as good predictors of employees’ use of KMS while their attitude and perceived usefulness mediate those elements of social capital. Table 6 shows the results of the regression analysis.

Table 6

Model summary of regression analysis

Source	Sum of Squares	df	Mean Square		Number of Obs
Model	30.279	5	10.093		F (1, 73)	24.831
Residual	42.572	67	0.608		Prob > F	0.000
Total	72.851	72	10.701		R	0.756^a
					R-squared	0.682
					Adj. R-squared	0.673
KMS usage	Coef.	Std. Err.	t	P > \|t\|	Standardized Coefficients Beta
_constant	0.255	0.110	2.318	0.020
Trust	0.194	0.031	6.258	0.000	0.248
Strong connectedness	0.276	0.043	6.419	0.000	0.295
Shared norms	0.262	0.047	5.574	0.000	0.188
Attitude	0.289	0.045	6.422	0.000	0.203
Usefulness	0.211	0.029	7.276	0.000	0.221

^aPredictors: (Constant), Trust (i.e., RSC), Strong connectedness (i.e., SSC), Shared norms (i.e., CSC), Attitude, Usefulness. ^bDependent Variable: KMS usage.

5 Discussion

The concept of social capital has been widely studied and analyzed in several contexts in which various dimensions of social capital become the focus of analyses. Analyzing social capital in KMS context has received little attention, especially the dimensions that are relevant for interpretation and profound understanding of KMS usage and as a vehicle to facilitate knowledge sharing. This study examined trust (i.e., relational social capital), shared norms (i.e., cognitive social capital), and strong connectedness (i.e., structural social capital) as essential dimensions to deepen the understanding and rationale for KMS adoption and implementation for successful KM initiatives. A positive approach to these dimensions ensures sustainable knowledge-sharing efforts in an organization. Further, social capital which inherently implies social relationships between people are fundamental resources in knowledge-sharing environment to potentially increase organizational knowledge. Thus, quality collaborative efforts through the use of KMS can lead to quality improvement in KM initiatives.

Given the sample characteristics of the respondents, we found majority of the respondents between 20 to 39 years on average accounting for a total of 75.4%. It is reasonable to believe that the respondents understand the value of collaboration and sharing of knowledge motivated by shared vision, norms and culture. This finding was partially consistent with the study by Elmorshidy [38], which found the dominant age group between 19 and 25 or more, indicating the respondents’ better understanding of KMS for knowledge sharing. Also, respondents’ higher academic qualification (i.e., Undergraduate and Masters degrees) was far rooted in their likelihood to adopt and use KMS for their job functions. The study found long years of respondents’ familiarity with the KMS (i.e., at most 7 years work experience), proving the value of KMS to facilitate knowledge sharing. These findings support the study by King and Marks [39], which found about 84%of the respondents (i.e., up to 5 years or more) with rich experience in using KMS and a minimum of bachelor’s degree as far as academic qualification is an important concern.

Considering the interplay between the three dimensions of social capital, we found that the relationship between SSC and RSC (i.e., r = 0.881, p < 0.01) is stronger and statistically significant compared to the relationship between SSC and CSC (r = 0.732, p < 0.01). This may be as a result of the propensity of network ties that naturally create social ties in the context of organizational proximity. This finding supports the claim by Claridge [12] that social capital dimensions are highly interconnected, especially between RSC and SSC. The reverse also suggests that the higher a trust-based social relationship between people, the stronger the network ties that become a resource to grow intellectual capital within the organization [31]. However, individuals and teams have to be motivated and supported in their networking efforts to successfully use the KMS to achieve their knowledge-sharing goals. Also, the positive association between RSC and CSC implies that quality social interactions between people or teams are avenues to deepen respect, reciprocity, and trust for effective collaboration and knowledge-sharing success through KMS.

Regarding KMS usefulness, we found that shared norms, trust, and user connectedness influence users’ frequency of use and value of KMS for sharing or transferring knowledge. This finding supports the study by Bhattacherjee & Premkumar [21], which found that social influences relate to users’ intention to perceiving the usefulness of KMS. Also, this finding partly supports the work by Jahmani, Fadiya, Abubakar, & Elrehail [40], which established that users’ perceived usefulness of KMS has effect on the use of KMS for knowledge sharing and retrieval. In addition, mutual trust among users increases collaboration. As a result, users have value for knowledge and trusted knowledge sources contributed by others through the KMS. This finding is consistent with Humayun, Gang, & Masood [41] whose study found that trust relates positively to KMS usage which further enhances cooperative behavior. As evidenced in this study, each of the three dimensions of social capital has a positive significant relationship with KMS usage (r = 0.897, p < 0.01) for structural social capital, (r = 0.712, p < 0.01) for relational social capital, and (r = 0.664, p < 0.01) for cognitive social capital. Impliedly, trust, shared norms, and strong user connectedness are fundamental and antecedent to increasing users’ positive behavior towards the successful use of KMS. Failure to examine these dimensions thoroughly can jeopardize KMS initiatives. This finding is consistent with the result obtained by Chen [42]. Moreover, users found the KMS very convenient to use to exchange knowledge, and this supports the study by Ting, Woon, & Kankanhalli [43] which found ease of use of KMS to influence the usefulness of KMS used primarily for knowledge sharing. Hence, there was a positive attitude towards the use of KMS.

Based on the shared norms, values, and practices, users developed positive attitudes towards KMS usage for effective knowledge exchange. Given this level of attitude, the collective interest of the company supersedes individual interest. Hence, this study found that such collective interest was a recipe for increased collaboration among participating KMS users. This is in tandem with the study by Inkpen & Tsang [27] in which shared norms, vision, and culture are characteristics of strategic alliance to promote knowledge sharing or transfer. However, Davenport & Daellenbach [26] suggested that despite the interactive support through KMS, the feeling of users being connected cannot necessarily be equal. Thus, expectations and obligations of users and teams in terms of network of relationships related to individualized attitudes. As a result, the study found a variability in users’ obligation and identification, given their shared interest.

The strong ties among KMS users were more identifiable and pronounced with teams and close colleagues or co-workers regardless of unit or departmental affiliation. Perhaps, users’ familiarity with the use of KMS facilitated their easy connections with others and the formation of closure to reinforce trust, interdependence, obligation, and unique identification. Thus, the study found that strong connectedness served as reference points for other co-workers in terms of their values and standards as well as acquiring the needed knowledge. This supports the study by Yli-Renko, Autio, & Sapienza [44], which established the association between network ties and acquisition of knowledge. Consequently, users developed positive mindset towards knowledge-value creation through the use of KMS. The Beta statistics indicate that strong connectedness, trust, system usefulness, user attitude, and shared norms influenced up to 29.5%, 24.8%, 18.8%, 20.3%, and 22.1%respectively of KMS usage. The relatively lower influence of KMS usage by shared norms may be affected by individualized understanding of language and norms across functional areas.

Overall, the three dimensions of social capital influence KMS usage and the study found strong connectedness to influence KMS usage more than trust and shared norms. This means that structural social capital is critical to developing quality trust and reliable shared norms, values, and visions that reflect the potential benefits of KMS usage. It is, however, important to emphasize that users’ connectedness and support for each other requires organizational support to reinforce build-up trust to enhance quality knowledge sharing.

Organizational support is so crucial in the overall knowledge-sharing efforts through KMS. Given the knowledge infrastructure and architecture as evidenced in the study by Nyame & Qin [3], it is imperative for senior management to play an active role in motivating users to continuously use the KMS for knowledge sharing/transfer and internalization purposes. The value attached to social relationships upstream of Alpha company also inspired more users to participate in KMS usage. This study found that senior management rewards users of KMS for different reasons and as a way to encourage more participation of KMS usage and to sustain its continuance use. This finding supports the work by Kankanhalli, Tan & Wei [45], which found that one inducement mechanism to increase employees’ participation to use KMS for contributing knowledge is rewards. In their multi-dimensional model, they explained that rewards are antecedent to inducing effective knowledge contribution. Moreover, the provision of periodic training in the use of KMS and creating the awareness about the value or benefits of KMS further encouraged KMS usage by the employees. This supports Kankanhalli et al. [45]’s finding, which emphasized top management support as an antecedent to opportunity to facilitate knowledge creation and contribution through KMS. Thus, commitment by top management to focus on knowledge needs and the provision of appropriate knowledge infrastructure at any given time drives positively users’ attitude and actions towards KMS usage.

6 Research implications

KMS is a socio-technical system and requires a thorough analysis from both social and technical perspectives. The social component drives its technical design and implementation. From social capital theory perspective, knowledge exchange through social relationships using KMS is a critical factor to increase intellectual capital. Investigating the three dimensions of social capital in terms of trust, shared norms, and strong connectedness in this study gives profound insights into the social influences of social capital on KMS usage. Thus, it is essential for researchers to consider analyzing issues relating to social capital and their effects on employees’ continuance intention to use KMS for more productive benefits.

From organizational management standpoint, senior management of Alpha Company is required to prioritize social capital for successful continuance usage of KMS, particularly the dimensions examined in this study. The moderate positive and significant correlation between social capital and KMS usage implies more efforts required of management to ensure stronger mutual trust, shared norms, and connectedness in the organization. Increased practical experience by employees concerning these dimensions of social capital has greater tendency to improve users’ attitudes, continuance intension, and behaviors towards KMS usage. Hence, it is vital for any organization that has deployed KMS as a means to seriously leverage knowledge to make more efforts to establish mutual trust, shared norms, and strong connectedness across the organization.

7 Limitations and future work

Of course, the study has a few limitations, and these can be considered in future studies. First, the use of one case study makes it impossible for the results to be generalized. Second, the study was limited to only three dimensions of social capital. However, social capital is multi-dimensional, which suggests that social capital is scalable based on specific contexts and level of analysis. Hence, other dimensions such as satisfaction through relationships, institutional trust, formal and informal social ties, group characteristics, and social support are equally applicable to KMS context. Last, the analysis of the study focused primarily on micro and meso levels of social capital due to the nature of KMS and its deployable characteristics in most KM initiatives. It must be, however, emphasized that attitudes, social interactions, and behaviors towards KMS usage characterize many knowledge management initiatives.

Future works can use longitudinal data to test the effects of the three dimensions of social capital on KMS usage. Also, the use of multiple case studies can help broaden the scope of analysis and establish deeper insights about the facilitative roles of the three dimensions in the use of KMS. Further, future studies can examine other dimensions of social capital suitable for KM strategy and investigate how those dimensions impact the use of KMS for knowledge-sharing success.

8 Conclusion

This study examines the three dimensions of social capital –relational social capital (i.e., trust), cognitive social capital (i.e., shared norms), and structural social capital (i.e., social connectedness) in KMS context and their effects on KMS usage. Further, we developed a model that explains the cyclical nature of these dimensions and the different perspectives they provide to better understand knowledge creation and internalization. We found that each of the three dimensions of social capital has a positive significant relationship with KMS usage, indicating that shared norms, trust, and strong connectedness reinforce the use of KMS. Thus, given an environment of trusted social relationships, people become more committed to using KMS to contribute knowledge at the benefit of both the knowledge seeker and the organization as a whole. Additionally, this study validates the three dimensions –relational, cognitive, and structural social capital as critical factors in explaining KMS usage in an organization. As a contribution to IS/KMS literature, this study provides a perspective of social capital theory in which social capital plays a facilitative role based on trust, shared norms, and strong connectedness in the use of KMS. Thus, the study gives more insights about the connection between social capital theory and KMS as a socio-technical system.

Footnotes

Acknowledgments

Our gratitude goes to Dr. Adasa Nkrumah Kofi Frimpong of the School of Management Science and Engineering of UESTC for quality proofreading of the manuscript. Also, we express our profound appreciation to Prof. Li Xiaoyu of the School of Information and Software Engineering of UESTC for meticulously helping us dive into IS/KMS literature to identify the key constructs relevant for this study.

Author contributions

CONCEPTION: Gabriel Nyame

METHODOLOGY: Gabriel Nyame and Mavis Adu-Gyamfi

DATA COLLECTION: Ernest Kwame Ampomah

INTERPRETATION OR ANALYSIS OF DATA: Gabriel Nyame, Mavis Adu-Gyamfi and Ernest Kwame Ampomah

PREPARATION OF THE MANUSCRIPT: Gabriel Nyame and Mavis Adu-Gyamfi

REVISION FOR IMPORTANT INTELLECTUAL CONTENT: Gabriel Nyame, Mavis Adu-Gyamfi and Ernest Kwame Ampomah

SUPERVISION: Ernest Kwame Ampomah

Appendix

A. Measurement items for the survey

Attitude

Using KMS is a good idea. . . . . .bad idea.

Using KMS is ineffective... effective idea.

Perceived usefulness

Using KMS improves my performance.

Using KMS increases my productivity.

Using KMS enhances my effectiveness.

I find KMS to be useful for my work.

KMS usage

I regularly use KMS to contribute my knowledge in my work.

I regularly use KMS to create new knowledge in my work.

Strong connectedness (i.e., structural social capital)

I have close working relationship with others.

I use the communications tools of the KMS to interact with others.

Overall, I find myself well connected with other co-workers.

Shared norms (i.e., cognitive social capital)

There is a norm of cooperation.

There is a norm of openness to conflicting views.

There is a norm of teamwork.

Trust (i.e., relational social capital)

I believe that people give credit for others’ knowledge where it is due.

I believe that people share the best knowledge that they have.

I believe that people use others’ knowledge appropriately.

Note: A 5-point Likert type scale was used to measure all items and they were anchored 1 = strongly disagree to 5 = strongly agree

Appendix

B.Interview guide

Trust-based social relationship

How do you interact with others for knowledge sharing purposes using KMS?

To what extent do you have trust in those you interact with on specific-job matters?

In your opinion, how valuable are the knowledge sources to you regarding your job?

KMS usefulness

What does the use of KMS help you to accomplish in terms of your job performance?

How often do you access the knowledge base for documents or knowledge-based “best practices”?

Can you describe briefly the nature of the knowledge packages you access from the knowledge base?

Overall, what specific benefits do you obtain from using the KMS?

Shared norms and beliefs

How do you feel when your share experiences with others regarding your job and vice versa?

How do you find the existing shared norms and values to enhance your knowledge-sharing efforts?

To what extent do you cooperate with others for collaborative knowledge sharing?

Strong connectedness

To what extent do you feel much connected during your interactions with others?

To what extent do you find social ties as useful in shaping attitude towards knowledge sharing?

Organizational support

What support does management provide to encourage persistent use of the KMS?

Does management consider KMS as important tool for knowledge sharing?

What areas of investment do you find management to improve IT capability for knowledge sharing?

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Items	1	2	3	4	5	6	Eigen	Variance	Cumulative
Items	1	2	3	4	5	6	value	Explained (%)	Variance (%)
ATT1	0.767						3.132	17.54	17.54
ATT2	0.758
PU1		0.810					3.408	15.32	32.86
PU2		0.793
PU3		0.714
PU4		0.682
KMSU1			0.934				2.662	12.89	45.75
KMSU2			0.883
TRST1				0.865			2.490	12.76	58.51
TRST2				0.848
TRST3				0.797
SNRM1					0.801		2.128	10.13	68.64
SNRM2					0.785
SNRM3					0.642
STRCON1						0.931	2.017	9.88	78.52
STRCON2						0.834
STRCON3						0.799

Knowledge management system implementation success: A social capital perspective

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Related works

2.1 KM and KMS

2.2 Social capital perspective

2.3 The role of social capital in KMS

2.3.1 Structural social capital

2.3.2 Cognitive social capital

2.3.3 Relational social capital

2.4 Interplay of structural, cognitive, and relational social capital dimensions

3.1 Research design

3.3 Data collection

3.3.1 Validity and reliability measures

4 Results

4.1 Descriptive statistics

4.3 Trust-based relationship

4.4 Shared norms and beliefs

4.5 Strong connectedness

4.6 Organizational support

4.7 Interrelationships between the social capital dimensions

Table 5 Descriptive statistics and correlations Descriptive statistics Correlations (n = 73) Variable Mean SD SSC RSC CSC KMSUSG SSC 3.714 1.255 1 0.881** 0.732** 0.897** RSC 4.055 1.588 1 0.459** 0.712** CSC 3.052 0.761 1 0.664** KMSUSG 3.602 1.352 1

6 Research implications

7 Limitations and future work

8 Conclusion

Footnotes

Acknowledgments

Author contributions

Appendix

Appendix

References

Table 5
Descriptive statistics and correlations

Descriptive statistics Correlations (n = 73)

Variable Mean SD SSC RSC CSC KMSUSG

SSC 3.714 1.255 1 0.881^ 0.732^ 0.897^

RSC 4.055 1.588 1 0.459^ 0.712^

CSC 3.052 0.761 1 0.664^

KMSUSG 3.602 1.352 1