Revisiting Knowledge Management System Use: Unravelling Interventions that Nurture Knowledge Seeking

Knowledge management systems (KMS) are adopted with the aim of facilitating knowledge flow within the organization. However, it is seen that member participation on these platforms is limited. The objective of this work is to identify aspects that influence intention to seek knowledge on KMS. Antecedents to knowledge seeking behaviour were identified through a morphological review of literature. A conceptual model was proposed based on the decomposed theory of planned behaviour. Structural equation modelling was used to assess the adequacy of the model. Results show that seeking happens when the individual has an intrinsic motivation to learn and when the quality of knowledge available on KMS is perceived as having high content value. Interestingly, the authors find that top management has no bearing on one’s intention to seek. Findings reveal that HR activities need to identify people management practices, such as hiring people with a curious disposition and promoting seeking as a positive behaviour. KM practices need to focus on stimulating curiosity and learning amongst members.


INTRodUCTIoN
Traditionally, knowledge management systems (KMS) have been adopted by organizations to facilitate enterprise-wide collaboration, encourage knowledge flow, and make organizational knowledge accessible to a larger section of employees.Adoption of KMS is expected to proliferate usable knowledge and meet the core objective of knowledge management which is 'providing the right information to the right people at the right time'.KMS use is said to be optimized only when critical knowledge resources are shared, and these contributions are leveraged for reuse (Watson & Hewett, 2006).Interestingly, while member involvement on KMS is a precursor to knowledge exchange, encouraging members to participate in KMS remains a challenge.Many companies still struggle to get members to actively engage on knowledge management platforms, such as enterprise social networks and online discussion communities (Kumi & Sabherwal, 2018).Hence, the study of individual knowledge behaviors continues to interest academicians and knowledge management (KM) practitioners alike.Besides, many organizations are embracing work from home options to mitigate disruptions at work due to COVID 19.Consequently, knowledge exchange is likely to happen predominantly over the electronic medium during the next few years.Hence, urging participation is critical to leveraging KMS.
Two individual knowledge behaviors that have been studied largely in literature are knowledge sharing and seeking behaviors.While extant studies in the field have examined the importance of knowledge sharing behaviors, relatively fewer studies focus on understanding knowledge seeking behavior of individuals (Fadel et al., 2015).
This work examines knowledge seeking behavior (KSB) in the context of KMS and is particularly important as, post adoption of KMS, organizations are interested in increasing returns on KMS investment.In the current context, returns can be interpreted as the fraction of those who leverage information from KMS to the those who proffer to KMS (Veeravalli et al., 2019a).We propose that enhancing seeking behaviors on KMS will lead to an increase in sharing and consequently a higher return on contribution.Further, organizational endorsement of seeking behavior could have a domino effect and lead to an increase in discussions, feedback, and contributor recognition, which could, in turn, lead to meaningful contributions.
Interestingly, according to Jennex (2008), sheer quantity of KMS use is not an indicator of KMS adoption success.The author, further to a longitudinal study, indicates that quality of knowledge captured will drive use and provide satisfaction to both new and experienced users.Studies also reveal that leader-disclosure, leader-reliance, among members-disclosure, and among members-reliance also determine knowledge collecting and donating behavior of members (Jahmani et al., 2018;Le & Lei, 2018).Recent studies also indicate that organizations can manage their social capital by leveraging knowledge management processes including learning orientation, knowledge sharing, organizational memory, and knowledge reuse (Farooq, 2018).Hence, prior to developing interventions to increase engagement on KMS, it is important to discern aspects that influence knowledge seeking behavior of individuals.
This work aims at understanding elements that persuade an individual to seek knowledge at work, identify organizational factors that influence KSB, and to validate whether top management support impacts KSB of individuals positively.The purpose of this study is to develop a comprehensive model to identify fundamental aspects that impact knowledge seeking intention of individuals in a technological context.
Sections that follow present a brief review of literature, followed by the proposed model, and findings from the empirical study.

MoRPHoLoGICAL ReVIew oF LITeRATURe
Review of literature was undertaken using a morphological approach with the aim of assessing seminal work undertaken in the area and identifying research gaps.The term morphology refers to the study of 'structure' or 'form'.The General Morphological Analysis (GMA) is a qualitative technique that is often adopted by social scientists to categorise and comprehend principal links in complex systems that are not amenable to simplistic quantification (Ritchey, 2013).Morphological analysis can be used to qualitatively understand the effect of a combination of variables in systems that are hard to model quantitatively.
Morphological framework starts with the identification of important dimensions (or subdimensions) under study.While in standard quantitative analysis, such features usually take real numbers as values, in morphological analysis, the possible values for each of these dimensions are qualitative and called as "options".The process of developing a framework entails comprehensive mapping of all possible groupings of elements that constitute an entity (Veeravalli & Vijayalakshmi, 2019b).In this paper, a review of literature is organized in a morphological table as it allows one dIMeNSIoNS ANd oPTIoNS -AN oVeRVIew

Forms of Knowledge
This dimension examines commonly identified forms of knowledge that are predominantly considered for study in knowledge management.Options include: 1. Tacit and 2. Explicit knowledge.Tacit knowledge is knowledge that is not articulated easily that which is intuitive by nature, and 'embodied' in human beings either as a skill or knowledge.In contrast, explicit knowledge is knowledge that can be easily articulated, codified, and stored in electronic repositories.
While literature focuses on both explicit and tacit knowledge, knowledge seeking from IT systems must largely deal with only explicit knowledge and its reuse.Building on that, in this work, we study how individuals seek explicit knowledge from KMS.

organizational Level Facilitators
This dimension features studies that focus on promoting adoption of KMS because there are fewer studies in knowledge seeking per se that focus solely on the importance of organizational level factors.Five primary options have been listed, which are: 1. Top management support, 2. Resource facilitating conditions, 3. Human resources (HR) practices, 4. Knowledge management (KM) practices, and 5. Learning culture.
The first option considered is the effect of top management support.Further to a qualitative study on factors that impact the adoption and diffusion of knowledge management systems, Quaddus & Xu (2005) posit that among the major variables that affect diffusion of KMS, organizational culture and top management support significantly impact KMS diffusion.Findings from the study on enablers and impediments to the use of electronic information management systems indicate that mere support on the part of top management is insufficient, top management would have to participate on these systems and share their knowledge to promote knowledge sharing (Humayun & Gang, 2013).In the context of Enterprise Social Networks (ESN), top management support was found to influence employee's usage of enterprise networks (Chin et al., 2015).However, He & Wei (2009) describe that management support influences only knowledge sharing behavior and that intention to seek knowledge is primarily drive by performance expectancy and effort expectancy.To ascertain the impact of this construct on one's intention to seek knowledge, this is construct is incorporated in our conceptual model.
The second option discusses resource facilitating conditions and their impact on knowledge seeking behavior.According to Bock et al. (2006), resource facilitating conditions such as training aid the use of electronic knowledge repositories for knowledge seeking.Examining social relationships and their role in knowledge management systems usage, He et al. (2009) found that extrinsic motivators such as training also encouraged participation on KMS.Tsai & Kang (2019) advocate making relevant resources such as online tutorials and additional teaching materials available to help seekers internalize knowledge sought.Further, in the context of ESN, Chin et al. (2019) find that facilitating conditions are significant predictors of consumptive use of ESN.We see that the study of facilitating conditions such as training, awareness programs continue to be important.They are akin to help manuals that are distributed with electronic goods.
The next two options listed are HR and KM practices.A case study conducted by Currie & Kerrin (2003) specifies that HR practices seem to discourage knowledge sharing across functions rather than mediate and promote the same.Donate & Gaudimillas (2015) provide empirical evidence that HR practices specifically oriented towards KM promote KM practices.Limited academic research has been undertaken to study the impact of HR practices and KM practices on knowledge seeking behaviors in the context of KMS.Hence, the proposed model includes these two constructs.The final option listed as an organizational level facilitator is the learning culture an organization affords.In the era of digital disruptions, an organizations' learning culture can be the biggest driver of business impact.In the context of knowledge reuse on Electronic Knowledge Repositories (EKR), while Chhim et al. (2017) posit that learning culture positively influences knowledge reuse through EKRs, limited empirical research exists on the impact of learning culture on KM adoption and knowledge seeking behavior, revealing a gap in the literature.This is probably because studying the learning culture of an organization and its impact may require longitudinal studies that are difficult to set up at enterprise levels.

Individual Level Factors that Influence Knowledge Seeking
This dimension considers factors that could influence an individual to seek knowledge.The options include: 1. perceived usefulness, 2. perceived ease of use, 3. perceived knowledge growth, 4. perceived output quality, 5. incentives, 6. peer influence, 7. perceived trust and 8. collaborative norms.
The first option, perceived usefulness of KMS, has been studied both in the light of knowledge seeking and continuance intention.According to Singh et al. 2018, individuals seek knowledge from EKR only when the knowledge available is recognized as useful.Results of their study of health care professionals indicates that attitude to seek knowledge is significantly influenced by perceived usefulness.Knowledge seeking is likely to happen when the knowledge available is perceived as extremely important, particularly by physicians.In their work on factors impacting continuous intention to seek knowledge, Humayun & Gang (2013) also report similar results.
According to Phang et al. (2009), the second option, perceived ease of use, influences a member's intention to seek knowledge as against their intention to contribute knowledge.Hence, the following steps need to be taken: 1. efforts should be taken to simplify KMS and their interface and 2. communicate to knowledge seekers that KMS are relatively easy to use.Contrary findings are reported by Kankanhalli et al. (2005); they find that when users had considerable experience using electronic systems, perceived ease of use did not influence one's intention to seek knowledge.Differences in findings may be attributed to the sample selected for the study.Moreover, today's knowledge seekers are adept at using IT systems.Hence, this construct was not considered for this study.
Perceived knowledge growth is the third option discussed under this dimension.Several studies have found seeker knowledge growth to be significantly positively related to continuous knowledge seeking intention (Bock et al., 2006;Humayun & Gang, 2013;Lai et al., 2014).Interestingly, contrary findings are reported by He et al. (2009).Their results indicate that seeker knowledge growth did not have a significant impact on intention to continue seeking knowledge from KMS.
The fourth option listed is perceived output quality.Kankanhalli et al. (2005) report that when employees perceive the quality of knowledge available on electronic knowledge repositories as relevant, reliable, and timely, they tend to engage on KMS for knowledge seeking.Chhim et al. (2017) also found a positive relationship between knowledge quality and knowledge reuse through EKRs.These results are corroborated by Chin et al. (2019), who report that high content value leads to greater use of ESN for information and knowledge seeking.Considering that perceived knowledge quality has a significant impact on one's intention to engage in electronic repositories, this has been included in our model.
The fifth option discusses the impact of incentives on knowledge seeking behavior.From the literature it is evident that researchers have contradictory views on the impact of incentives on knowledge seeking.Availability of incentives was found to have a significant positive relationship with usage of electronic knowledge repositories for knowledge seeking, in cases where the task interdependence was high (Kankanhalli et al., 2005).According to a study conducted by Humayun & Gang (2013), organizational rewards have a positive influence on one's intention to seek knowledge.Qiao et al. (2019) stress that organizations should offer extrinsic rewards as primary motivators to entice KSB.However, the results of empirical studies conducted by He et al. (2009) indicate that organizational rewards are not significantly related to seeking continuance intention.The authors believe that this could be because of variances in the reward scheme or that the reward itself is too insignificant for members to make an association.
The next option discussed is peer influence.Studying the impact of interpersonal relationships on knowledge seeking behavior, Yuan et al. (2011) infer that strong ties with members influenced the people from whom knowledge was sought.However, while choosing between personal relationships and electronic resources for knowledge seeking, three factors, namely time, cost, and location were often considered.Results of this study are critical to understanding how knowledge seeking networks are formed.He & Wei (2009) also echo the same finding and highlight that social relationships at work affect one's attitude to seek knowledge.
Perceived trust is the next option examined under this dimension.Generalized trust, pro-sharing norms, and identification were posited as factors that moderated the relationship between individual factors and use of electronic knowledge repositories (EKR) for knowledge seeking (Kankanhalli, 2002).Trust in the knowledge shared has a bearing on one's attitude to seek knowledge (Singh et al., 2018).Further to an empirical study, Zhu et al. (2018) surmise that effective knowledge transfer occurs when the recipient trusts the sharer and believes in their expertise.
The last option considered is the impact of collaborative norms.While the moderating effect of this construct has been studied by Bock et al. (2006), data was collected from a relatively small group of part time students pursuing a graduate degree in a large university.The finding that collaborative norms can reduce the impact of and inhibitors such future obligation need to be studied further.Empirical studies are needed to make a conclusive statement.

Factors that Inhibit Knowledge Seeking
This dimension looks at factors that inhibit knowledge seeking behavior on KMS.The options include: 1.Individual level factors, 2. Organizational level factors and 3. Technological factors.
Individual level factors that constrain knowledge seeking include effort required to seek knowledge, information overload, and cost of future obligation.Humayun & Gang (2013) studied the effect of effort on seeking.Findings indicate that users who perceived that knowledge seeking was likely to take a lot of time and effort were not inclined towards using KMS frequently.The amount of effort required to seek knowledge also impacts intention to continue using KMS (He & Wei, 2009).Effort required has also been identified as the one of the most important costs associated with knowledge seeking (Sedighi et al., 2017).However, further to an exploratory study on physicians use of Web 2.0, Singh et al. (2018) report contrary results.Their findings reveal that attitude to seek knowledge is not affected by the effort involved.This could be attributed to the critical nature of knowledge sought in the healthcare profession.
Difficulty in finding the right information and information overload have been discussed as main reasons that affect reuse of information (So & Bolloju, 2005, Bock et al., 2010).Dittes et al. (2019) indicate that technical overload and technostress are two important factors that affect employees and limit the benefits of using any IT infrastructure for digital work.
A few studies, such as the study by Bock et al. (2006), have studied cost of future obligation and its impact on the usage of EKR for knowledge seeking.Results indicate that cost of future obligation can demotivate an individual from participating on the system.Their findings corroborate the results from similar studies done in the context of knowledge sharing.However, more studies are needed to conclude decisively.
Extant studies focus on the impact of organizational level barriers on use of KMS for knowledge sharing, very little work is done in the context of knowledge seeking.This gap in literature can be addressed by conducting studies on the impact of organizational culture, hierarchical structure of the organization, level of competition, and the like.
Technological inhibitors include perceived system reliability, perceived system compatibility and the number of platforms available.Lai et al. (2014) find that system compatibility and system quality help create a positive attitude towards knowledge seeking.Kankanhalli et al. (2005) contend that suitable indexing and retrieval technologies are required for effective knowledge seeking.

Theoretical Frameworks Used
This dimension enlists various theoretical concepts that have been used to study knowledge seeking behavior.The options include: 1. Technology acceptance model (TAM), 2. Theory of planned behavior (TPB), 3. Social exchange theory, 4. Expectation-confirmation theory, 5. Theory of task technology fit, and 6.Decomposed theory of planned behavior (DTPB).
Researchers have primarily used TAM and TPB as the conceptual basis for their studies.TAM and TPB have been used to elicit attitudinal beliefs such as perceived usefulness and perceived ease of use (Kankanhalli et al., 2005;Bock et al., 2006;He & Wei, 2009;Lai et al., 2014).Tsai & Kang (2019) formulated their study on the lines of the social exchange theory where findings indicate that a seeker's intention to reciprocate is influenced by perceived social benefits and perceived community support.
Drawing from the expectation -confirmation theory, He & Wei (2009) investigate factors that drive continuance intention of both knowledge sharing and seeking.Kankanhalli et al. (2005) have drawn two important characteristics of task requirements, namely: task tacitness and task interdependence from the theory of task technology fit.Technology adoption models are often used to design the conceptual models of various studies; probably because most studies in the area deal with tracing factors that affect an individual's intention to use electronic systems.
In an organizational context, situational factors are diverse, difficult to specify and capture.Taylor & Todd (1995) proposed the DTPB to address the situational limitations of TPB.DTPB, as the name suggests, is derived from TPB, however, the emphasis of this theory is on decomposition of belief structures that influence intention to perform a behavior.Decomposing the constructs provides relevant explanation and aids development of suitable interventions (Pavlou & Fygenson, 2006).Decomposition enables identification of relevant factors that can be operationalised and incorporated in a research framework.It is particularly relevant to managers who wish to develop specific interventions.
Further to review of extant literature, DTPB was narrowed down as the theoretical basis for this study.DTPB allows for choosing concrete antecedents of attitudinal beliefs, subjective norms and controls that can be influenced through system design and implementation strategies.Decomposition enables identification of relevant factors that can be operationalised and incorporated in a research framework.
Our model is designed along the lines of the DTPB model.Attitudinal belief dimensions for our model have been derived from literature.Constructs such as knowledge seeker growth and perceived knowledge quality have been included to capture relevant contextual factors.Further, some constructs like "Compatibility" and "Self-efficacy" feature in DTPB but are not considered in our study.We believe that in today's world, information technology solutions have permeated work lives and people are very comfortable accessing E-Systems for information seeking.

Context of Study
Researchers have studied factors that impact knowledge seeking under various scenarios.These include studies on public sector organizations, professional virtual communities, IT Sector, health care, construction engineering, and consulting firms.This dimension helps us identify sectors where further research can be carried on such as the manufacturing sector.

ModeL
The framework developed in Table 1 provides a bird's eye view of work done in knowledge seeking and highlights combinations of studies that can be undertaken as well.Answers to the following research questions were also sought using the morphological framework: • Why do people seek knowledge at work?From earlier studies, perceived knowledge growth and perceived information quality were narrowed down as the two most important factors that needed to be examined.• Under what condition does knowledge seeking thrive/happen the most?To answer this question, we included the impact of seeker effort, role of social interaction ties, and perceived trust in the community to our factors that affect attitudinal beliefs.• What influence do supervisors have on one's knowledge seeking behaviors?After analysis of literature, top management support was found to be a critical factor influencing knowledge seeking behavior, hence retained.• Does ease of finding solutions influence one's intention to seek knowledge?To study this, construct such as resource facilitating conditions have been included in the study.• Do KM practices and HR practices that organizations adopt actively promote knowledge seeking?
To answer this, the model encompasses constructs for KM and HR practices.They are represented under 'perceived behavioral control'.
In order to arrive at factors that influence knowledge seeking intention of individuals, we examine suitable attitudinal, normative and control beliefs from the morphological table discussed above and integrate them in the DTPB model for our study as proposed in Figure 1.In the next section, we discuss the model.

Antecedents to Attitude to Seek Knowledge on KMS
Attitudinal beliefs, in simple terms, include behavioral aspects that affect the perception of the individual actor and create either a favourable or unfavourable impression.Attitude to seek knowledge on KMS is defined as the degree to which an individual has favourable or unfavourable evaluation of the outcome of seeking behaviors (Ajzen, 1991).Antecedents of attitudinal beliefs are presented below; these have been derived from literature and the morphological framework.

Perceived Seeker Knowledge Growth
Imbibing from the experiences of others serves as positive motivator to participate in electronic communities and KMS offer the engaged seeker continuous access to organizational knowledge.According to Lai et al. (2014), motivation to learn enhances member participation on KMS for knowledge seeking and includes perceived benefits of keeping abreast of new ideas.Seeker knowledge growth can be described as the benefit one perceives they will receive when they keep updated with new ideas by accessing the pool of knowledge resources that KMS offers.Studies indicate that members who actively collect knowledge from KMS are likely to improve their capabilities and expertise (Chen & Hung, 2010).Based on the premise that users may perceive learning from others' experiences as a positive outcome of seeking knowledge on KMS, we surmise that perceiving knowledge growth as an outcome of seeking knowledge on KMS impacts one's attitude to seek positively and thus: Hypothesis 1: Attitude to seek knowledge on KMS will be positively affected by perceived seeker knowledge growth.

Perceived Knowledge Quality
The aphorism 'Knowledge is power' suggests that access to appropriate and qualitative knowledge brings power.While quality of knowledge available on KMS is often judged by whether it can be easily assimilated, it is important to note that perceived knowledge quality comprises of two aspects: a) the level of trust in the source of knowledge and b) the perception of the quality of information that is available on KMS.In today's diverse workplace, almost all social interactions are based on trust, and it lays the foundation for seamless knowledge exchange (He et al., 2009a).Trust is vital as it influences one's willingness to accept shared knowledge (Zhang & Chen, 2018).Empirical studies also suggest that seekers often determine the value of the information by judging its source (Desouza et al., 2006).Further, members are interested in the contextual significance shared knowledge provides.Quality of content available encourages members to participate in professional virtual communities (Lai et al., 2014).Besides, content provided by KMS must be accurate, logical, timely, and practical for others to participate and use KMS (Ong & Lai, 2007).Hence when members seek on KMS, if the knowledge they access is beneficial and trustworthy their attitude to seek knowledge is positively impacted by the quality of knowledge.Building on this we propose: Hypothesis 2: Attitude to seek knowledge on KMS will be positively affected by perceived knowledge quality.

Perceived Seeker Effort
The process of knowledge seeking involves articulating the right question and refining the search terms until suitable output is obtained.Studying effort expectancy in the context of ESN, Chin et al.  (2019) indicate that consumptive use of ESN requires skill.The user should be adept at searching, filtering, and assimilating the information sought and found.Besides, time and effort constraints, and insufficiency of personalization also impact the momentum associated with KSB (He & Wei, 2009).Seeker effort is defined as the perception of time and effort required to engage in the act of knowledge seeking on KMS (Kankanhalli, 2002).Significant time and effort could be expended during this activity, and this is a significant determinant of attitude to seek knowledge.When time and effort required to seek on KMS are disproportionate to the benefits realised from seeking, it will have a negative effect on one's attitude to seek knowledge.Hence, we hypothesize: Hypothesis 3: Attitude to seek knowledge on KMS will be negatively affected by perceived seeker effort.

Social Interaction Ties
Social interaction ties are defined as the strength of relationships, the amount of time spent, and communication frequency with other members of the organization (Chiu et al., 2006).Furthermore, social network ties are characterized by shared history, high inter-dependence, and frequent interactions, which in turn promote relationships, bonding, and cost-effective knowledge exchange (Nahapiet & Ghoshal, 1998).
Empirical studies on the influence of social interaction ties on knowledge sharing indicate that social ties positively affect quantity of knowledge shared (Chiu et al., 2006).We posit that trust and interaction ties between members will make it easier to seek directly rather than over the platform.Hence: Hypothesis 4: Attitude to seek knowledge on KMS will be negatively affected by social interaction ties.

Top Management Support
The current knowledge economy calls for knowledge-oriented leaders to advocate the need for development of knowledge initiatives and exploitation for enhancing organizational performance (Donate & Sanchez de Pablo, 2015;Singh et al., 2018).When top management values knowledge, it creates an environment that nurtures knowledge corpus of co-workers (Del Giudice & Maggioni, 2014).Further, top management support has been found to help create a conducive environment for adoption of IS in the organization (Ragu-Nathan et al., 2004).
Top management support refers to the degree to which top management (including supervisors) understand the importance of seeking knowledge on KMS and the extent to which they are involved in KM activities (Ragu-Nathan et al., 2004).Top management support was identified as a catalyst in promoting the use of KMS for knowledge seeking (Humayun & Gang, 2013).
Seeking behavior being viewed as an admission of inability to accomplish tasks independently by management should be addressed for knowledge seeking to improve When one perceives support from management to seek knowledge, it allays their fears of being labelled as a knowledge recipient and the approval of seeking behavior will have a positive impact on one's intention to seek on KMS.On this basis, we have formulated the hypothesis below: Hypothesis 5: Intention to seek on KMS will be positively influenced by top management support.

Perceived Behavioral Control
Perceived behavioral control is the person's perception of the ease or difficulty of performing the behavior of interest (Ajzen, 1991;Taylor & Todd, 1995).In simple terms, it is one's perception of their ability to perform a behavior armed with the knowledge of factors that facilitate or impede the performance of such behavior.Antecedents of perceived behavioral control included in our work are facilitators that aid adoption of KMS for knowledge seeking such as training, availability of stateof-the-art KM platforms (Bock et al., 2006), and organizational practices intended at promoting knowledge exchange on KMS.Appropriate facilitating conditions, and the influence of pertinent HR and KM practices are anticipated to positively impact one's perception about the feasibility to engage on KMS for knowledge seeking.

Organizational Practices
Organizational practices, as the name suggests, are practices adopted by the firm to actively promote knowledge behaviors.Practices examined in this study include: a. Facilitating conditions, b.HR practices and c.KM practices adopted with the purpose of encouraging members to engage and learn from KMS.

Facilitating Conditions
As the name implies, facilitating conditions include resources that can aid knowledge seeking from KMS. Resource facilitating conditions reflect availability of the required resources such as training, support, and availability of state-of-the-art KM platforms (Bock et al., 2006).In the current study, these include the availability of training, hands-on-sessions, and manuals.According to Kankanhalli et al. (2005), resource availability has a significant positive relationship with seeking explicit knowledge from repositories.Further, training and facilitating conditions have been noted to influence knowledge seeking behavior (He & Wei, 2009).A favourable perception of the availability of technological and other training resources can aid system adoption, hence the hypothesis: Hypothesis 6.1: Perceived behavioral control will be positively influenced by the availability of facilitating conditions.

HR Practices
Today's knowledge driven economy boasts of a diverse workforce and differences in the learning requirements of members at the workplace need to be addressed.HR teams can effectively promote learning by adopting appropriate practices.HR practices, in the current context, are defined as practices adopted by human resource management to promote participation in knowledge management systems to seek knowledge.Strategic HR practices like assessment of fit, performance appraisals that emphasize on knowledge exchange, and creating an environment that encourages employees to openly seek help can influence and shape skills, attitude, and behavior of employees, and promote knowledge flow.Atapattu & Jayakody (2014) indicate that while HR practices have a profound impact on employees, it is important to note that employee's openness to change also influence the success of KM initiatives.This situation can be remedied by HR teams as they can help with aligning expectations and institutionalizing learning.Further, HR can help with competency development of employees thereby ensuring a permanently updated workforce.This in turn can help in internal recruitment and minimizing loss of knowledge (Gope et al., 2018).As aligning HR practices with the knowledge strategy of the firm can lead to efficient knowledge exchange, and empowering employees with autonomy can aid knowledge seeking behavior, we hypothesize: Hypothesis 6.2: Perceived behavioral control will be positively influenced by HR practices of the firm.

KM Practices
In this study, KM practices refer to the organizational and managerial practices that have been adopted specifically with the intention to promote knowledge transfer and exchange.KM practices include all efforts to augment efficient and effective management of the firm's knowledge (Andreeva & Kianto, 2012).Further to a review of KM literature, Inkinen (2016) report that KM practices can influence individuals and impact performance.However, success of these practices depends on how knowledge is developed and applied.Furthermore, describing interactions between KM practices and intellectual capital, Kianto et al. (2017) suggest that KM practices have a significant impact on utilization and development of organizational knowledge.According to Donate & Gaudimillas (2015), KM exploration and exploitation practices moderated by knowledge-oriented HR practices have a positive influence on innovation.Several studies focus on the impact of KM practices on overall performance of the organization; however, few studies focus on the impact of KM practices on individual knowledge behaviors on KMS.Drawing from studies that have established the positive influence of KM practices on knowledge creation, transfer, and performance of the organization, we hypothesize: Hypothesis 6.3: Perceived behavioral control will be positively influenced by KM practices of the firm.

Intention to Seek Knowledge
Behavioral intention is said to be an approximal predictor of behavior.The construct reflects how motivated a person is to perform the said behavior.In the current context, intention to seek knowledge is the degree of one's belief that one will engage in KMS to seek knowledge (Ajzen, 1991;Taylor & Todd, 1995).Attitudinal beliefs, subjective norm, and perceived behavioral control provide substantive information about the determinants of a behavior.In this work, we have replaced 'Subjective Norm' with 'Top Management support and hence are not proposing a hypothesis for subjective norms separately.Nonetheless, we propose the following hypotheses to test the influence of 'Attitude to seek knowledge' and 'Perceived behavioral control': Hypothesis 7: Intention to seek knowledge on KMS will be positively affected by one's attitude to seek knowledge on KMS.
Hypothesis 8: Intention to seek knowledge on KMS will be positively affected by one's perceived behavioral control.

Research Methodology
The preliminary questionnaire was developed after considering items that have been validated by researchers in the domains of knowledge management, knowledge sharing, knowledge seeking and IS adoption.A six-point Likert scale ranging from Strongly Disagree to Strongly Agree was chosen to measure participant responses.The preliminary questionnaire was tested for face and content validity before it was administered to respondents.After content validation and revision of measures, items to be used for measurement were finalized.Next step involved conducting a pilot study to examine necessary psychometric properties.
Data for the pilot study was collected from members of a multinational company providing end to end engineering and IT business solutions.This company was chosen as the company was also interested in the study and gave us permission to conduct the study for them.Prior to the study, a note explaining the purpose and scope of the study was shared with the participants.
While 161 members from two different business units in the organization took part in the survey, only 81 responses were complete.Further to eliminating responses of those participants who answered every question in the same manner and took less than 5 minutes to complete the survey, the final usable data count was 64.
SPSS software (version 22) was used to run reliability tests on data collected from the pilot study.Almost all items were found reliable with Cronbach's alpha for all items being .70 or over.Only the alpha value for items measuring perceived seeker effort was .30.Two out of the three items measuring this variable were negatively worded questions.In the final questionnaire, these items have been converted to positively worded questions.Further, one item that did not load on to 'Top Management support' and was dropped.The item on introduction of KMS at the time of on-boarding was dropped as it did not load on to HR practices and its deletion did not reduce the reliability measure.Items measuring subjective norms were reworded to represent the context.
Further, to the pilot study, the final instrument was arrived at after incorporating necessary changes to ensure that the items were clear and representative.This tool, which was established to be reliable, was used to collect data from a larger population.The main study was conducted with the aim of collecting data from a larger population to test our hypotheses.
Operational definitions of the constructs and details of where the items were adapted from can be found in Table 2.

Main Study
Data for our study was collected by eliciting responses to our survey from professionals with a minimum of two years of experience across organizations, both Indian and multinational corporations with offices in India, that have implemented KMS.We chose organizations where knowledge

Perceived Seeker Effort
Seeker effort is defined as the perception of time and effort required to engage in the act of knowledge seeking on KMS.Kankanhalli (2002) He & Wei ( 2009)

Social Interaction Ties
Social interaction ties are defined as the strength of the relationships, the amount of time spent, and communication frequency with other members of the organization.

Top Management Support
Management support refers to the degree to which top management (including supervisors) understand the importance of seeking knowledge on KMS and the extent to which they are involved in KM activities.

Ragu-Nathan et al. (2004) Facilitating Conditions
Facilitating conditions reflect the availability of resources and support needed to engage in knowledge seeking behaviors.
Taylor & Todd (1995) Bock et al. (2006) HR Practices HR practices are defined as practices adopted by human resources management to promote participation in knowledge management systems to seek knowledge.

KM Practices
Refer to the knowledge management practices that are adopted by the organization to promote knowledge exchange.

Chuang et al. (2013) Self-developed
Attitude to Seek Knowledge on KMS Attitude to seek knowledge on KMS is defined as the degree to which an individual has favourable or unfavourable evaluation of the outcome of seeking behaviours.

Perceived behavioral control
Perceived behavioral control is the person's perception of the ease or difficulty of seeking knowledge on KMS.
Ajzen (1991) Taylor & Todd (1995) Intention to use KMS to seek knowledge The degree of one's belief that one will engage in KMS to seek knowledge.
Ajzen (1991) Taylor & Todd (1995) sharing and exchange over electronic media are promoted.Purposive sampling was used to identify professionals from organizations, across multiple sectors, that have implemented KMS.Respondents were identified through professional networks like LinkedIn, institute alumni databases and social networking.Participants were contacted via email and the validated instrument was administered to working professionals through an online survey.Since members were also contacted over email and through the HR team of various organizations, the exact number of participants who were contacted for participation is not known.However, to reduce non-response bias, we ensured that we contacted a representative random sample and contacted only working professionals with over 2 years of work experience and familiarity with KMS.In order to improve response rates, a note explaining the purpose of the study along with an assurance that responses would be treated confidential was shared with respondents.A total of 600 responses were received.Data was checked for missing values and outliers using SPSS.Incomplete responses were neither imputed nor considered for the study.The final usable data count was 486.

Profile of Respondents
Analysis of demographic variables collected during the study is discussed below.Gender distribution, age, educational qualification, number of years survey participants have spent at their respective organizations is given in Table 3.

Common Method Variance
In order to minimize common method variance, procedural remedies were adopted at the questionnaire design stage.Order of items were randomized; care was taken to develop unambiguous and concise items to help the respondent comprehend the items.Harman's single factor test was performed, and it was found that the single factor that accounted for the largest proportion of variance had a value of ~ 41.8%, less than the benchmark value of 50%.Next, Kaiser-Meyer-Olkin measure of sampling adequacy test (KMO) and Bartlett's test of sphericity were done, and data collected was found to be appropriate for factor analysis.
Another widely accepted method of testing for CMB, is the latent factor method (Chang, et al., 2010).In this method, differences between loadings of items on theoretical constructs vis a vis loading of items on theoretical constructs along with the common latent factor are examined.Further to arriving at the model using confirmatory factor analysis, we added a common latent factor (CLF) to capture the common variance among all observed variables in the model.Differences less than |.2| were observed indicating the absence of common method bias.

establishing Reliability and Construct Validity
Scale reliability was assessed using SPSS version 22. Results of reliability analysis were favorable, and all items were found to be reliable.Construct validity was established through convergent and discriminant validity.Convergent validity signifies that the items can explain enough variance in the underlying construct.In our study, measures exhibit convergent validity.
To establish discriminant validity, we assess the AVE of each construct and check if it exceeds the highest squared correlation with any other latent construct.Table 4 shows the AVE figures in the diagonal and the squared inter-construct correlation figures.All AVE figures listed are greater than all squared inter-construct correlations, establishing discriminant validity.

Assessing the Adequacy of the Model
Further to reviewing the individual theoretical constructs a series of CFA explorations was performed using AMOS version 22 to detect whether the constructs in our framework were distinctive.Fit indices generally used to ascertain model fit include the goodness-of-fit index (GFI), which is a measure of the amount of covariance in the sample covariance matrix reproduced by the estimated covariance matrix.A value above 0.90 is acceptable.Comparative fit index (CFI) values of above 0.90 are considered good.Another measure used to check for model fit is the root mean square error of approximation (RMSEA).An RMSEA value below 0.06 is considered good.Along with RMSEA, the other measure that is considered is the p of close fit (Pclose).If Pclose is greater than 0.05 then it is concluded that the fit of the model is "close."The conservative cut off value for Standardised Root Mean Square Residual (SRMR) is less than .05,however values less than .08 are also acceptable.Fit indices indicate that the theoretical model fits the data (refer Table 5).
Next, structural equation modelling (SEM) was adopted for testing the hypothesized relationships as it allows for examination of multiple dependent relationships simultaneously.The first step involved testing the measurement model and the second step involved testing the structural model.We conducted confirmatory factor analysis to examine the measurement model to ascertain if the items measured the latent variables they were expected to measure.The proposed nine-factor model fit the data well.
Path relationships between the constructs and the results of the structural model were examined.Fit indices are shared in Table 5, and they indicate good fit.The structural model along with the path coefficients are presented in Figure 2. In the sections below, we discuss the results of the path analysis.

Quantitative Validation of the Conceptual Model Through data Analysis
Antecedents of Attitude to Seek Knowledge on KMS  This model was found to be significant (at p <0.001) and the R square value of attitude to seek knowledge was 0.84, suggesting that perceived seeker knowledge growth, perceived knowledge quality, and perceived seeker effort can explain 84% of the variation in attitude to seek knowledge on KMS.
Examining the results of the path analysis, it is found that perceived seeker knowledge growth is significantly related to attitude to seek knowledge on KMS (β =0.67, p < 0.001); thereby supporting the hypothesis that perceived seeker knowledge growth positively affects one's attitude to seek knowledge on KMS.This implies that seeking happens out of an intrinsic desire to improve one's knowledge, corroborating findings of similar studies on EKR usage (Bock et al., 2006).Contrary findings have been reported by He & Wei (2009), who report that in a corporate setting an employee seeks knowledge on KMS just to perform their job more efficiently.
Further, it is noted that perceived knowledge quality exhibits a significant relation to attitude to seek knowledge on KMS (β = 0.36, p < 0.001).Based on this, we find that H2 is supported, and perceived knowledge quality positively affects attitude to seek knowledge on KMS.This indicates that it is imperative that high quality information be shared on KMS for seeking to happen, which is consistent with the recommendation that managers pay attention to the quality of information shared as it impacts one's intention to adopt KMS (Kuo and Lee, 2009).Wang & Lai (2014) report that among factors that directly or indirectly influence KMS usage, system quality and knowledge are very significant factors.
Perceived seeker effort was found to have a negative, yet statistically significant, relation with to attitude to seek knowledge on KMS (β = -0.143,p < 0.001).This result indicates that users who perceive KMS as difficult to use would have a weaker intention to continue seeking on KMS, validating H3 which hypothesizes that perceived seeker effort will negatively affect one's attitude to seek knowledge on KMS.These findings, however, are inconsistent with results reported by Singh et al. (2018).According to the authors, usefulness of knowledge is a determinant of attitude to seek knowledge and not effort involved.Variations in the result can be attributed to disparity in maturity levels of KMS amongst organizations and differences in member perception of the ease or difficulty of use of KMS.
H4, which states that social interaction ties negatively affect one's attitude to seek knowledge on KMS is not supported (β=-0.08,p > 0.05), implying that social relationships at the workplace have no bearing one's intention to seek knowledge on KMS.This allays doubts that people prefer to seek knowledge from social / professional circles over seeking from KMS.However, empirical studies on the influence of social interaction ties on knowledge sharing indicate that social ties positively affect quantity of knowledge shared (Chiu et al., 2006).Further, He et al. (2009b) point out that in the case of distributed teams, social relationships could establish positive attitudes toward knowledge sharing and therefore stimulate KMS usage.Differences in findings may be attributed to differences in the context.
Impact of Top Management Support on Intention to Seek Knowledge on KMS The impact of top management support on adoption of information systems has been well researched, however, fewer studies have been conducted specifically in the domain of knowledge seeking.
In the context of adoption of KMS, initiatives taken by top management at various stages have been found to positively stimulate KMS adoption (Quaddus & Xu, 2005;Wang & Lai 2014, Singh et al., 2019).Humayun & Gang (2013) find that top management support is critical to promoting the use of KMS for knowledge seeking.However, in our study, H5, which states that top management support positively affects a member's intention to seek knowledge on KMS is not supported (β = 0.09, p > 0.05).This result is interesting and in line with the findings of He & Wei (2009), whose results indicate that management influences only knowledge sharing behavior and that intention to seek knowledge is primarily driven by performance expectancy and effort expectancy.Considering that fewer studies examined this relationship, this result needs to be further validated.

Impact of Organizational Practices on Perceived Behavioral Control
We posit organizational practices as antecedents of perceived behavioral control.Organizational practices comprise of a) facilitators that aid adoption of KMS for knowledge seeking such as training, b) HR practices specifically geared to promoting knowledge exchange, and c) KM practices aimed at popularizing KMS usage for knowledge exchange.In this model, the R square value of perceived behavioral control was 0.60, suggesting that organizational practices explain 60% of the variation in perceived behavioral control.
Testing for H6, we find that it is supported (β = 0.77, p < 0.001) indicating that relevant facilitating conditions, HR practices and KM practices, significantly impact behavioral control of knowledge seeking on KMS.Further, H6.1 is supported (β= 0.91, p < 0.001), indicating that presence of facilitating conditions such as training and availability of knowledge champions to encourage adoption has a positive effect on a member's perceived behavioral control of knowledge seeking on KMS.This result is consistent with the findings of Chin et al. (2019), who predict that facilitating conditions are positively associated with consumptive enterprise social network use.Examining social relationships and their role in knowledge management systems usage, He et al. (2009b) found that rewards, training, and management facilitation also encouraged participation on KMS.
Testing for H6.2, we find that it was supported (β =0.76, p < 0.001), suggesting appropriate HR practices positively affect one's perceived behavioral control of knowledge seeking on KMS.Our findings corroborate the findings of Donate & Gaudimillas (2015).
H6.3 was supported (β = 0.72, p < 0.001).Empirical evidence suggests that KM practices positively affect a member's perceived behavioral control of knowledge seeking on KMS.According to Inkinen (2016), HR practices, IT practices, and KM leadership are the KM practices that most often facilitate positive outcomes related to firm performance.

Attitude to Seek Knowledge, Perceived Behavioral Control, and Intention to Seek Knowledge
This model was found to be significant (at p <0.001), and the R square value of attitude to seek knowledge and perceived behavioral control was 0.75, suggesting that attitude to seek knowledge and perceived behavioral control explain 75% of the variation in intention to seek knowledge on KMS.
Attitude to seek knowledge was found to have a significant positive relationship with intention to seek knowledge.H7 was supported (β=0.64,p < 0.001).Similarly, H8 was also supported (β = 0.28, p < 0.001), implying that perceived behavioral control has a significant positive relationship with intention to seek knowledge.These findings are in line with the theoretical model (Ajzen, 1991;Taylor & Todd, 1995).A summary of the findings from this study are given in Table 6.

THeoReTICAL CoNTRIBUTIoNS
The main contribution of this study is the integrated model that is proposed to assess factors that impact knowledge seeking intention.The study on the influence of organizational practices on individual's intention to seek knowledge enhances theoretical knowledge.Further, the morphological review of literature presented earlier sheds light on the various dimensions that need to be considered and it also highlights areas that need further attention.It is a consolidated reference on work done in the area.The current framework can be revised by re-examining and deliberating upon the dimensions and options.
This study also highlights that perceived knowledge growth and perceived knowledge quality have a greater impact than top management support on knowledge seeking intention, implying knowledge seeking is intrinsically motivated.This sheds lights on the types of HR and KM practices that will enhance knowledge seeking behavior.

MANAGeRIAL IMPLICATIoNS
Our study analyses of two factors that impact one's intention to seek knowledge, namely, attitude and behavioral control.Further to empirical analysis, we identify seeker knowledge growth and perceived knowledge quality as antecedents that have a bearing on one's attitude to seek knowledge.
We find that a member seeks knowledge only when they develop a positive disposition towards engaging on KMS and they believe that growth will happen.Several reasons can be attributed to why people seek from KMS, these include finding answers to questions that invigorate their interest, seeking solutions to specific problems they are trying to solve, and uncovering solutions to problems of common interest to the organization.To stimulate participation and keep seekers' interests active, organizations should encourage sharing of relevant knowledge and request subject matter experts to share their experiences and thoughts.
Another factor that helps motivate users to seek knowledge is the perception of the quality of knowledge available in KMS.Initial experiences define continuance intention and will have a role to play in users getting entrenched in these systems (Yan & Davison, 2013).Hence, content on KMS must contain information that is relevant, contextual, and useful to members.
KMS should be presented as social systems that nurture relationships between stakeholders.Top management and senior personnel should engage on KMS to incentivize members to participate.Seekers' should find exclusive information on KMS with ease.For this to happen, platforms would have to be screened to see if pertinent knowledge is shared, if questions posed are answered in reasonable time, and to assess time and effort taken to retrieve information.
It is seen that effort one must expend to seek on KMS has a negative impact on attitude to seek knowledge.Hence, all steps should be taken to reduce one's effort when seeking on KMS.State of art In the context of knowledge seeking, top management has no bearing on one's intention to seek knowledge.

4.
Does ease of finding solutions influence one's intention to seek knowledge?
Facilitating conditions such as appropriate training has a positive influence on a member's perceived behavioral control.This suggests that if timely and suitable training is provided, members' intention to seek on KMS will increase.

5.
Do HR practices and KM practices that organizations adopt help to actively promote knowledge seeking?
HR and KM practices are found to have a statistically significant and a positive influence on a member's perceived behavioral control.This implies that HR and KM practices currently adopted should be fine-tuned to promote knowledge seeking.Active acceptance of seeking behavior will promote knowledge seeking recommender systems should be available to reduce seek time and direct queries to in-house experts.Availability of multiple platforms could also make seeking knowledge cumbersome, as the end user is presented with the daunting task of sifting through lots of information.Hence, it is important to minimize redundant platforms.While top management support did not have a statistically significant relation to seeking in our study, studies in IS adoption indicate that management participation promotes participation on web portals immensely.Studies reveal that leaders often acknowledge the benefits of encouraging curiosity; however, they fail to promote it (Gino, 2018).We recommend that managers login, read contributions or pose questions to users; this would motivate users to participate of their own free volition.This in turn would have a domino effect and sustain knowledge seeking.
HR teams must design performance appraisals to include KM duties and at the same time make the seeker feel valued by the organization.HR teams should capitalize on opportunities to promote learning within the organization.The first step would be to accept seeking as a positive behavior.During recruitment and selection, HR teams should pay special attention to candidates with a curious disposition.Besides, curiosity encourages members to take an active interest in other's ideas and work effectively with the group (Chang & Shih, 2019).
KM practices on the other hand need to be fine-tuned to stimulate a continuous learning environment.Hiring curious minds and emphasizing learning goals is vital to improving organizational performance (Gino, 2018).Studies recommend that organizations establish 'Knowledge Cafes' to help individuals engage in knowledge sharing and learning (Chhim et al., 2017).Steps that could be taken can include support for attending conferences, arranging seminars etc., help build groups to promote collective learning and focus on relationship building among employees.

LIMITATIoNS
As with any study, an attempt to generalize findings should be moderated considering the limitations.While most of the samples were obtained from multi-national corporations in India, it was still limited to a sample predominantly from the IT sector; conducting this study on a lager sample covering diverse sectors could provide additional insights.Moreover, since participants use different KMS platforms their view on the ease or difficulty in using the same will differ.Hence, longitudinal studies replicating this study in multiple organizations will help strengthen the validity of our findings and improve generalizability of the results.Enterprise level data on actual usage behavior needs to be collected and the correlation between intention to use and actual usage of the system needs to be validated.This can probably be achieved if a case study methodology is adopted, and permission is sought from the company to access their KMS usage log files.
Further studies are needed to demonstrate if demographic factors such as age and gender have a bearing on one's intention to seek knowledge on KMS.

dIReCTIoNS FoR FUTURe ReSeARCH
Several avenues for future work in the area can be identified.Future studies should focus on the impact of factors that affect organizational dynamics like organizational culture and hierarchical structure of the organization etc. on knowledge seeking behaviors.Moreover, as discussed in the limitations, longitudinal studies are required to establish the relationship between intention to seek knowledge and actual usage behavior.Also, keeping line with research in KMS continuance intention, a comprehensive model that evaluates factors that affect both sharing and seeking should be evaluated.The impact of working from home on individual knowledge behaviors needs to be assessed.

CoNCLUSIoN
This study is among the first to consider the impact of organizational HR and KM practices on knowledge seeking behaviors and their implications.Findings indicate that HR practices need to identify people management practices that support knowledge exchange dynamics in the organization and KM practices need to focus on stimulating curiosity and learning amongst members.
Important inferences include: 1. the degree of knowledge growth attainable and value attributed to information available on KMS are key influencers of one's attitude to seek knowledge.2. top management support does not directly influence members to participate on KMS to seek knowledge and 3. Organizational practices such as relevant training, HR practices that facilitate knowledge exchange, and KM practices that foster curiosity will improve participation.
This work strengthens the current understanding of precursors of knowledge seeking and emphasizes the importance of organizational practices to enhance usage of KMS.Additionally, the study presents practitioners insights for developing operational interventions.Organizations need to focus on improving the quality of information available and request high -profile members to participate at the beginning.After the initial efforts gather momentum, sustained seeking will trigger an increase in aggregate sharing, thereby boosting the learning culture of the company.

Figure 2 .
Figure 2. Structural model with path coefficients

Table 4 . Establishing Discriminant Validity
Values in bold in the diagonal are average variance extracted.The values in the column reflect squared inter-construct correlation figures.