Practically speaking, the fact that self-efficacy may have a positive effect on problem-solving efficacy has crucial implications for our understanding of the optimal ability and confidence belief in a task (Tierney and Farmer, 2004). This belief can be termed as problem solving self-efficacy (PSSE) in a special task. One’s ability and willingness to adopt strategies to monitor, adjust, and reflect upon the problem-solving process can partially influence one’s problem-solving efficacy (Caldas et al., 2016). In line with this, one’s PSSE related to information integrating jobs can be defined as ones’ confidence in and ability to solve IT problems by monitoring, adjusting, and reflecting on problem solving in that service (Whitelock-Wainwright et al., 2020).

According to the theory of social cognitive learning, PSSE can be enhanced, but it can also be inhibited (Newman et al., 2018). Cervone and Peake’s (1986) study showed that participants were led to overestimate or underestimate their self-efficacy for a special task by being provided with an irrelevant anchor. Efficacy beliefs can be easily biased as, when one faces a problem-solving task, it is difficult to correctly assess one’s abilities and skills, and the effort that is necessary to successfully carry out the task (Oh, 2020). That is, self-efficacy in problem solving is subject to that problem or phenomenon (Lau et al., 2019). Individuals have a tendency to inaccurately assess their competence, and tend to be overconfident (Carter and Dunning, 2008; Molden and Higgins, 2012). It is thus likely that when a new problem-solving task is considered as being important for one’s future work self, one’s future self-efficacy judgments will be affected by that belief (Oh, 2020). Accordingly, given the problem-solving tasks related to software functions that IT workers focus on, the role that PSSE plays was explored in this study.

The knowledge sharing process is perceived to be related to sharing individual, team, and organizational knowledge. Knowledge sharing can bring significant benefits to employees and organizations due to its potential and actual advantages (Lin, 2007). In an organization, employees share knowledge with each other through storing their experiences in knowledge databases, which can lead to the improvement of their organization’s efficiency (Lin, 2006; Al Qeisi and Al Zagheer, 2015). Knowledge sharing promotes employees’ cooperation and teamwork through exchanging problem-solving experience and new ideas to improve the organization’s learning capacity (Dyer and Nobeoka, 2000; Cummings, 2014; Obrenovic et al., 2021). Regarding the influence of organizational factors on knowledge sharing, Ellahi and Mushtaq (2011) identified several factors that have an influence on knowledge-sharing behavior, namely mutual trust among staff, perceived usefulness and perceived value, job tasks, and appropriate technologies. The personality trait of conscientiousness, attitude of eagerness, and subjective norms were also found to have a positive impact on tacit knowledge sharing behavior (Obrenovic et al., 2021). According to Bock et al. (2005), four major factors that affect individuals’ attitudes toward knowledge sharing are anticipated extrinsic reward, anticipated reciprocal response, sense of self-interest, and fairness. For example, Rungsithong and Meyer (2020) found that trust is related to the effectiveness of knowledge sharing. Obrenovic et al. (2020) found that willingness to share had an impact on tacit knowledge sharing and played a mediator role between the trait of altruism and tacit knowledge sharing. Individual, organizational, and technology infrastructure are considered as factors affecting knowledge sharing behavior (Kakhki et al., 2020). Additionally, the fear of losing power due to knowledge sharing restrains young scientists (Oliveira et al., 2019), indicating that stolen reflection in knowledge sharing needs to be considered when studying types of knowledge sharing. However, few studies have considered the four types of mental processes in knowledge sharing in the information system integration service. Thus, the present study explored the levels of the four types of knowledge sharing.

According to Borman et al. (1994) and Sobaih et al. (2019), job performance is the extent to which one is able to successfully carry out one’s job tasks considering the normal constraints of the job and using the available resources. It includes two types of performance, namely task or in-role performance, and contextual or extra-role performance. Also, it refers to the employees’ ability to achieve the expectations of the organization (Darvishmotevali and Ali, 2020). Borman and Motowidlo (1993) further proposed dividing job performance into task performance and contextual performance, where task performance refers to the outcome of job performance related to completing a mission within the organization, whereas contextual performance is an extra-role behavior that is not restricted by any official rules. Indeed, the behavioral and outcome aspects of performance are linked together (Sobaih et al., 2019). In line with Borman and Motowidlo’s (1993) explanation, in this study, job performance is defined as an evaluation of the individual’s job results.

According to the person-environment fit theory (Muchinsky and Monahan, 1987), if a team leader can hire employees to fit the organizational culture, the team performance can be promoted (McGuire et al., 2019). Additionally, He and Brown (2013), adopting the person-job fit theory (Edwards, 1991), studied the effect of employees’ competence on their job performance, and found that there was a positive correlation. As failure costs increase exponentially during product implementation, and trouble-shooting cycles shorten, job performance within the information technology industry becomes a competitive factor (Peiro et al., 2020; Whitelock-Wainwright et al., 2020). Thus, the level of individuals’ job performance in the information system integration service jobs was explored in this study.

This proposition was developed further with the micro-ecology theory (Bronfenbrenner, 1979) which addresses the relationship between person-process-content. The person-process-content model (Bronfenbrenner and Crouter, 1983) was derived from the idea of the maximization of change processes. Based on the person-process-context model, we described the variability in the developmental process as studied in this paper as a function of context (job performance), person (self-efficacy), and process (knowledge sharing). Thereby, we predicted that those employees who have powerful PSSE beliefs would be likely to dedicate resources to and share their knowledge in the problem-solving process, which would in turn result in problem-solving efficiency. When we apply this theory to work in the IT industry, the correlates between employees’ self-efficacy, knowledge sharing attitude and job performance are conceptualized as the research model for this study (Figure 1).

Participants usually evaluate their collaboration with partners in light of two aspects, namely the distributive fairness of the investment-reward ratio, and the procedures designed to reward contributions (Katok and Pavlov, 2013). ET has been drawn on in a number of studies to examine the multilateral relationships that are embedded in either network rational reflection or cross-organization knowledge sharing (Zhou et al., 2020). For example, it has been found that employees’ attitudes toward and intention to engage in knowledge sharing are enhanced by self-efficacy, which provides benefits for the whole organization (Chen et al., 2012). While self-efficacy has been shown to persist over time (Pajares and Graham, 1999), individuals’ judgments may change in line with changes in their perceptions of the job condition. Bandura (1986) described the connection between one’s ability to control one’s interactions and beliefs about one’s capabilities and potential to control one’s behavior (Pajares, 2002), arguing that those people who have high levels of self-efficacy tend to set themselves more challenging goals and usually work harder than those who have low levels of self-efficacy (Bandura, 1997). Some arguments arise in knowledge sharing. A previous study revealed that if employees found inequality in their knowledge sharing, they would change their attitude toward it (Hsu and Lin, 2008), but another study indicated that individual self-efficacy was more important in sharing knowledge than equity concerns (Tiedens and Linton, 2001; Bandura and Locke, 2003). However, when there is uncertainty appraisal and management is challenged in the IT industry (Brashers and Hogan, 2013), employees might not have the well-formed self-efficacy beliefs needed to be able to regulate their actions (Tamjidyamcholo et al., 2013). How employees’ PSSE relates to different types of knowledge sharing was hypothesized as follows:

Knowledge sharing is a vital social asset for organizations because it leads to better job performance and improves organizational success (Swanson et al., 2020). The Self-monitoring theory explains the tendency of individuals to manage their behaviors and expressions such that they reflect the surrounding environment (Snyder, 1974). When people have a high self-monitoring orientation, they will analyze the appropriate behavior and the best way to express their ideas in certain situations (Bell et al., 2000). For example, being able to sensitively react to situational clues and demands is an important issue for employees which results in different knowledge sharing behavior that affects the job performances in an organization (Oh and Kilduff, 2008). That is, promoting clues and demands in knowledge sharing can promote employees’ job performance in an organization (Stollberger et al., 2019; Swanson et al., 2020). Moreover, Nassif (2019) highlighted dark personalities, stating that if people are Narcissists, they will exhibit hostility and aggressiveness when their egos come under threat, while Psychopaths have been observed to worry about making negative impressions in group communications (Rauthmann, 2012). Core behavioral adaptation related to the extent to which dark traits affect one’s feeling of being worthy to share and of being competent to work (Pratim, 2007), in particular among IT professionals (Cui, 2017), has not been explored in related studies. Thus, how different types of IT employees’ knowledge sharing is related to job performance was hypothesized as follows:

A questionnaire-based survey technique was used, and snowball sampling was adopted. A questionnaire form was created in Google Docs, then a message was sent to friends who work in the sector of the information system integration service in the IT industry via Facebook, Line, and email. They were asked to mail the form to more friends working in the same field.

In consideration of the ethical issues, we assured respondents about the anonymity of their responses (Islam et al., 2020). As both independent and dependent variables were measured at the same time, the present study took some measures to avoid the issue of common method variance (Islam and Tariq, 2018). After 2 weeks of data collection, 320 responses were returned, and after deleting incomplete questionnaires, there were 307 data available to be subjected to statistical analysis.

Regarding the composition of the participants, 73 (23.8%) were females and 234 (76.2%) were males, indicating that the majority of employees in the information integrating service are males. In terms of age, 49 (16%) were between 21 and 30 years old, 112 (36.5%) were between 31 and 40 years old, and 132 (43%) were between 41 and 50 years old, indicating that employees aged between 41 and 50 constituted the major population of the current study.

The questionnaire items were adapted from the previously mentioned theories and research and were obtained by using the forward-backward translation method to ensure face validity. This study used the questionnaires to collect the required data and construct the content basis for the analysis. The questionnaire applied a 5-point Likert scale as the scoring model for the subsequent analysis and presentation of the results. The questionnaire reliability and validity were examined using SPSS and AMOS.

Problem solving measurement: Previous research has indicated that there is a positive relationship between creative self-efficacy and creative problem solving in the workplace (Amabile, 1996; Treffinger, 2000). We adapted the creative self-efficacy scale developed by Tierney and Farmer (2004) and embedded problem-solving self-efficacy in IT-oriented jobs into the items, for example, “I have confidence in solving IT problems” or “I can solve any problem in the information integrating system service.”

Knowledge sharing types of measurement: Automatic response and rational reflection were adapted from the dual process theory (Kahneman, 2011). The present study drew on the theory of I’m OK, You’re OK (Harris, 2004), and the opportunistic and self-interested orientations of knowledge sharing (Estrada et al., 2016) to design items related to ridiculed reflection and stolen reflection. Exemplary items in automatic response included: “If I find out someone made a mistake, I will point it out right away” and “I always express my suggestions immediately when they come to mind.” Rational reflection included: “If I have a new idea, I would think about whether it is logical before speaking out” and “If I have a new idea, I would compare that idea to others before I speak out.” Ridiculed reflection included: “If I have a new idea, I would think about whether it will be ridiculed before I speak out” and “If I have a new idea, I would consider whether it is too naïve before I speak out.” Stolen reflection included: “If I have a new idea, I would think about whether it will be stolen before I speak out” and “If I have a new idea, I would consider whether it will be taken as others’ new idea before I speak out.”

Job performance measurement: Given that our scale is job-specific, we had specific objectives for measuring IT job performance related to service work (Pitafi et al., 2018). That is, to assess employees’ job performance in a customer-oriented environment, we adapted Grote and Grote’s (1996) guide to performance appraisal to design our scale, including the items: “I think that by knowledge sharing with others, I can troubleshoot in the information integrating system sooner than before” and “I think that by knowledge sharing with others, I can solve more difficult problems.”

Scholars have suggested that the recommended value of each fitting index is that the chi-square degree of freedom ratio (χ²/df) should be less than 5 (Hancock et al., 2018), while the root mean square error of approximation (RMSEA) has to be less than 0.1. It is also recommended that the goodness-of-fit index (GFI), adjusted goodness of fit index (AGFI), normed fit index (NFI), non-normed fit index (NNFI), comparative fit index (CFI), incremental fit index (IFI) and relative fit index (RFI) all exceed the threshold of 0.8 (Hancock et al., 2018), whereas the parsimonious normed fit index (PNFI) and parsimonious goodness of fit index (PGFI) are best to exceed 0.5 (Hancock et al., 2018). The statistical verification values of this study are χ²/df = 2.012, RMSEA = 0.058, GFI = 0.948, AGFI = 0.924, NFI = 0.902, NNFI = 0.903, CFI = 0.911, IFI = 0.914, RFI = 0.908, PNFI = 0.768, and PGFI = 0.730. All values reached the suggested index values and fitted the model well (Byrne, 2010). Besides, the result of a multivariate normality test showed that the Mardia’s coefficient of multivariate kurtosis was 133.911, the critical ratio of which was 25.149.

This study adopted AMOS20 to verify the paths of the research model. Figure 2 shows that PSSE is positively related to automatic response (β = 0.335, t = 4.921^∗∗∗), rational reflection (β = 0.551, t = 7.672^∗∗∗) and ridiculed reflection (β = 0.354, t = 5.414^∗∗∗), but is negatively related to stolen reflection (β = −0.333, t = −5.153^∗∗∗). Figure 2 also shows that rational reflection in knowledge sharing is positively related to job performance (β = 0.455, t = 6.485^∗∗∗); ridiculed reflection and automatic response in knowledge sharing are positively related to job performance (β = 0.184, t = 3.326^∗∗;β = 0.180, t = 2.963^∗∗); however, stolen reflection in knowledge sharing is negatively related to job performance (β = −0.195, t = 3.157^∗∗). The values of effect size (f²) between PSSE and knowledge sharing correlate, and knowledge sharing and job performance are above the threshold. The explanative power of PSSE on automatic response is 30.4 %, on rational reflection it is 21.2%, on ridiculed reflection it is 22.5%, and on stolen reflection it is 31.1%. The explanative power of the four types of knowledge sharing on job performance is 33.3%. Those effect sizes and explanative powers are all above the threshold suggested by Byrne (2010).

This research adapted a micro ecological system and the social exchange theory to develop four types of knowledge sharing. These types advance the understanding of knowledge sharing behavior in previous studies. In line with this, other types of knowledge sharing may be developed according to different organizational needs. Among the four types of knowledge sharing, rational reflection can most positively predict one’s job performance. With rational reflection, the proposed ideas may be more suitable for solving job problems; thus, IT managers have to encourage their employees to practice rational reflection and thinking before take an action and to increase the effectiveness of members’ knowledge sharing. Besides, IT companies need collaborative problem solving. If employees with ridiculed reflection hesitate to share their knowledge, that will hamper their job performance and their team effectiveness; thus, IT managers need to develop a friendly chatting environment to decrease the ridiculed reflection of employees.

The results of this study have at least three practical implications for the field. First, this study contributes a novel, identity-centered perspective to explain four types of knowledge sharing. Specifically, we argue that the strength with which a person identifies with that aspect of PSSE will predict that person’s willingness to engage in knowledge sharing. The impact of an individual’s PSSE should be considered in terms of how its effects alter job performance. Second, this study provides a strong example of the importance of management approaches to avoid stolen reflection tendency in knowledge sharing. Finally, the results of this study revealed that PSSE is the antecedent of job performance mediated by knowledge sharing; thus, to enhance knowledge sharing tendency, employees with high levels of PSSE are the best choice to work in the information system integration service industry.

Regarding gender, most participants in this study were male because the IT industry attracts more males in Taiwan. Thus, the present study did not analyze the difference in knowledge sharing by gender. Future studies may focus on some industries which recruit more female workers, such as bank workers, to identify any gender differences in knowledge sharing and job performance. Besides, this study was conducted on a limited sample of 307 System Integration IT workers. Thus, it is essential to expand the research samples to different IT sectors in future research.

There are many types of job categories in the information system integration service industry, for example, information security, programming, and so on. Different job attributes have different needs for knowledge sharing; thus, future studies may identify each of these subcomponents’ individual impacts on knowledge sharing behavior, and may undertake a comparative study to understand the possible effects of knowledge sharing differences in different types of organizations.

Diversity researchers have long appreciated the importance of articulating effects of individual differences as an integral part of group knowledge sharing. The effect of four types of knowledge sharing: automatic response, rational reflection, ridiculed reflection, and stolen reflection should be further examined in different organizational cultures to have a greater variety of different perspectives to advance the meaning of knowledge sharing research.

To test the external validity of the questionnaire items, the present study applied t-tests to compare the top 27% and bottom 27% to ascertain whether the value was over 3 to ensure the external validity of each item. There are some other statistical methods, for example, the heterotrait-monotrait ratio of correlations, for examining the external validity of questionnaire items; thus, other approaches may be applied in future studies to test the item external validity.