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Article

Improving Knowledge-Sharing Intentions: A Study in Indonesian Service Industries

by
David Afandy
1,2,
Agus Gunawan
1,2,3,*,
Jol Stoffers
3,4,5,
Yoke Pribadi Kornarius
1 and
Angela Caroline
1
1
Department of Business Administration, Parahyangan Catholic University, Bandung 40141, Indonesia
2
MBA Department, Parahyangan Catholic University, Bandung 40141, Indonesia
3
NEIMED, Socio-Economic Knowledge Institute, 6419 AT Heerlen, The Netherlands
4
Faculty of Management, Open Universiteit of the Netherlands, 6419 AT Heerlen, The Netherlands
5
Research Centre for Employability, Zuyd University of Applied Sciences, 6131 MT Sittard, The Netherlands
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(14), 8305; https://doi.org/10.3390/su14148305
Submission received: 5 June 2022 / Revised: 29 June 2022 / Accepted: 5 July 2022 / Published: 7 July 2022
Versions Notes

Abstract

:
Managers of service firms should improve the knowledge-sharing intentions among employees to obtain knowledge stored in them and use it to provide better services to customers. Across types of organizations, especially professional bureaucracies and operating adhocracies, one question is whether service firms can use the same information technology infrastructure strategy to improve workers’ knowledge-sharing intentions. To address this question, 347 respondents working in service industries participated in this study, and focus group discussions were conducted among representatives of those firms to produce better interpretations of statistical results. Findings suggest a weak but significant relationship between information technology infrastructure and knowledge-sharing intentions. While entering a new normal period after the COVID-19 pandemic, effective information technology infrastructures appear to represent a natural and ordinary facility. Despite operating in disparate organization types, managers in both professional bureaucracies and operating adhocracies should build trust and relationships with workers to increase knowledge-sharing intentions.

1. Introduction

Managing a service firm requires consideration of several special factors. First is maintaining service quality across workers. Customers develop disparate impressions when served by different workers because services are intangible, and in most cases, some degree of customization is required to meet a customer’s specific needs; service ownership cannot be transferred to other people since customers experience services directly [1,2]. Second is that managers should understand the workforce’s requirements well to address future challenges, technology intensity [1], and knowledge productivity [3]. In addition to these two characteristics, the most crucial factor is organization type. Mintzberg [4] categorizes five types of organizations—simple structure, machine bureaucracy, professional bureaucracy, division form, and adhocracy—which are formed based on four aspects of organizational structure. First is operating core, strategic apex, middle line, technostructure, and support staff; second is method coordination, such as mutual adjustment, direct supervision, standardization of work processes, outputs, and skills; third is job specialization, behavior formalization, training and indoctrination, unit grouping, unit size, action planning, performance control system, liaison devices, and vertical or horizontal decentralization; and fourth is configuration, such as the age and size of the organization, and its environment.
To compete in this contemporary knowledge and information era, managers must adapt and transform the firm to match development of external technology and environmental changes [5]. Knowledge management thus represents a significant source of such adaptation to external changes, since knowledge fuels change and technology represents the machine [6]. Success with implementing knowledge management is essential to acquiring intellectual assets that later become competitive assets for adaptation and innovation [7]. Setiarso [8] argues that a firm’s knowledge is stored in workers’ minds (42%), paper reports and documents (26%), electronic documents (20%), and computer-based knowledge management (12%). A strong need thus exists for service firms to achieve better knowledge management, reflecting a continuous process of knowledge acquisition, sharing, and application using adequate media and tools such as information technology [9,10,11,12].
Service firm managers who require effective knowledge management must consider two types of organizations—professional bureaucracies and operating adhocracies [13]. Firms that use professional bureaucracies, such as law firms, hospitals, and public-sector organizations, can enhance their capabilities using staff members’ formal professional knowledge. Firms that use operating adhocracies, categorized as project-based businesses, commonly provide non-standard, innovative, problem-solving services, examples of which include consultancies and software developers. Among both professional bureaucracies and operating adhocracies, a service firm manager’s ability to improve workers’ knowledge-sharing intentions plays a role in ensuring successful knowledge management.
Some research suggests that information technology infrastructures have no influence on knowledge-sharing intentions, such as Ibrahim [14] in manufacturing and Kucharska and Erickson [15] in various industries. However, in the service sector, such infrastructures do [10,16,17,18,19]. When a service firm has an effective information technology infrastructure, it can use its knowledge and support its workers in delivering regular and innovative services [1,10]. Caroline et al. [20] show that when a firm has such an infrastructure, workers are eager to use it to support knowledge-sharing intentions. Managers of Indonesian service firms have an easier time providing adequate information technology infrastructures to workers, in comparison to changing leadership and communication styles, or monetary reward systems. When dealing with information technology infrastructures, managers must ensure successful integrations of hardware, networks, software, databases, procedures, and support staff, which workers use to do their jobs productively [10,21,22]. The study aimed to offer insights into whether managers should prioritize enhancing information technology infrastructures. It is also aimed to assesses whether disparities are evident regarding perceptions of information technology infrastructure among workers who are employed in different types of service organizations, namely professional bureaucracies and operating adhocracies.

2. Literature Review and Hypotheses Development

There are four important activities in knowledge management practices, namely knowledge generation and sharing, knowledge organizing and storing, knowledge dissemination and sharing, and knowledge application [23,24]. How to design and implement a knowledge management system that can support knowledge generation is a challenging issue, as well as how to encourage workers to participate by sharing their knowledge, as the system will be useless without their participation. Thus, knowledge sharing has become a significant requirement for successful knowledge management in a firm [25,26]. Knowledge sharing is a process in which members of the firm exchange their personal experiences and information so that the other members can use it [24,27,28,29].
The theory of reasoned action or theory of planned behavior have become the fundamental backbone to trigger knowledge-sharing behavior. The theory of reasoned action is based on the assumption that every worker is influenced by his/her personal beliefs, which are based on his/her subjective norms and attitudes [25]. When someone believes that something is good, he/she will have the intention to pursue it. The theory of planned behavior is based on the assumption that someone’s beliefs are influenced by the firm’s climate and perceptions towards the firm behavior, considering that each worker is a member of the firm [30,31]. Previous experience as a member of the group may shape the person’s intentional behavior. The theory of planned behavior places strong emphasis on socially desirable behavior created within the firm [32]. Thus, this theory can better explain how knowledge sharing occurs in the business firm setting.
In order to obtain in-depth insights into knowledge-sharing behavior, longitudinal data is necessary. Researchers argue that knowledge-sharing intentions are sufficient for obtaining predictions on tendencies towards knowledge-sharing behavior. Indeed, studies highlight a strong correlation between the intention to share knowledge and knowledge-sharing behavior [25]. Knowledge-sharing intentions can be measured with three dimensions [33,34,35,36,37]: first, the worker’s norms concerning which kind of knowledge should be shared with the others; second, the worker’s attitude regarding whom he/she is willing to share the knowledge with; and finally, the worker’s belief as a member of the firm.
Davidavičienė et al. [18] argue that it is very challenging for a firm to control its workers’ knowledge-sharing intentions. Thus, the firm needs to offer a strategic policy by providing an effective information technology infrastructure that will improve the workers’ knowledge-sharing intentions [38]. Since the COVID-19 pandemic occurred, information technology infrastructure has become an increasingly dominant factor for increasing knowledge-sharing practices [39]. Based on some workers needing to work from home, the pandemic pushed firms to offer instant access to workers to access the firm’s system from a distance. Those firms that had already developed a knowledge-sharing culture prior to the pandemic gained benefits as they were more prepared than firms without such a culture [40]. Information technology infrastructure has become important as a tool for enabling knowledge sharing and co-creating knowledge for geographically dispersed employees [41,42]. By providing an effective information technology infrastructure, the firm can minimalize communication barriers between workers [39,40]. Therefore, they can use the infrastructure instead of needing to talk directly to each other in sharing their knowledge.
Information technology infrastructure is a combination of hardware, software, and specific workers who manage the information technology [15]. The stronger need to access the firm’s information technology infrastructure from various places at the same time also increases the security necessary for preventing unauthorized persons from accessing the firm’s system, thus prompting the need for more detailed management of hardware and software [43]. Hardware is separated between devices and the network, where the network comprises not only hardware but also security management and related procedures [21,44]. The focus on software is also separated between software as the mediator for accessing the system and databases where data and processed data are stored [45,46,47]. Thus, information technology infrastructure can be measured with six dimensions, namely hardware, software, network, databases, procedures, and support staff [10,21,22,45].
The current study builds on the notion that if service firm managers provide better information technology infrastructures, workers have stronger intentions to share knowledge. Thus, we propose the following hypothesis:
Hypothesis 1.
Information technology infrastructure positively influences knowledge-sharing intentions.
Caroline et al. [48] highlight disparities when comparing the relationship between information technology infrastructure and knowledge-sharing intentions in small- and medium-sized firms versus large firms. Taking into account the significant difference between service firms that follow different types of service organizations (namely professional bureaucracies and operating adhocracies), it is important to understand whether there is a difference in workers’ perception of the information technology infrastructure provided. A firm’s characteristics prompt managers to take different approaches to enhance workers’ knowledge-sharing intentions. When a manager understands the firm’s characteristics clearly, he/she can make better decisions to increase those intentions. Thus, we propose our second hypothesis:
Hypothesis 2.
Information technology infrastructures differ between professional bureaucracies and operating adhocracies.

3. Methods

Purposive sampling was used to identify 347 respondents who worked in Indonesia’s service sector, wherein eighteen service firms participated. These firms operated in various industries, including financial, healthcare, hotel, telecommunications, contractor and developer, logistics, rental, education, and automotive, and each was categorized as either a professional bureaucracy or operating adhocracy. Further, 255 respondents come from professional bureaucracy and 92 respondents are from operating adhocracy. A questionnaire that used a Likert-type scale comprised 29 indicators that measured respondents’ perspectives on information technology infrastructure, and 11 indicators measured perspectives of knowledge-sharing intentions. Information technology infrastructure was measured across six dimensions—hardware, network, software, database, procedure, and supporting staff—from Jabbouri et al. [21], Mao et al. [45], Pérez-López and Alegre [10], and Tseng [22]. Respondents reported perceptions regarding the importance of information technology infrastructure indicators. Knowledge-sharing intentions were measured across three dimensions—ways of sharing knowledge, with whom they wanted to share knowledge, and personal beliefs about sharing knowledge—from Casimir et al. [33], Dong et al. [34], Ding et al. [35], Zhang et al. [36], and Wang et al. [37]. Respondents reported their degree of agreement with knowledge-sharing intention indicators, shown in Table 1.
After obtaining results from quantitative methods, we conducted focus group discussions among representatives from the 18 service firms to obtain greater understanding of statistical results and achieve better interpretations regarding firms characterized as professional bureaucracies and operating adhocracies. The focus groups were divided into two groups, one characterized by professional bureaucracies and the other by operating adhocracies. After obtaining consensus from each group, we assessed similarities and differences between the two. Reasons behind each representative’s opinion were recorded and used during further analysis.

4. Results and Discussion

With a 0.05 significant level and 347 respondents, the r-table was 0.1053. Thus, all information technology infrastructure and knowledge-sharing intention indicators appeared valid (Table 2).
The Cronbach’s alpha coefficient for the eighteen indicators of information technology infrastructure was 0.912, and that for the eleven indicators of knowledge-sharing intentions was 0.853. Thus, the reliabilities of both variables were acceptable (Table 3).
The result of a Kolmogorov-Smirnov normality test was 0.072, suggesting that the data had a normal distribution and were appropriate for regression analysis. The result of a heteroscedasticity test was 0.053, suggesting the data were heteroscedastic. With a significance level of 0.05, the result of a linearity test was 2.748, more than the F-table, which is 1.401, suggesting that the relationship between information technology infrastructure and knowledge-sharing intentions was non-linear. Using Pearson’s r, the correlation between information technology infrastructure and knowledge-sharing intentions was 0.331, which, according to Guilford [49], falls in the category of a weak correlation, but nonetheless a significant relationship (Table 4).
The result of non-linear regression was y = 35.911x2 − 283.598x + 596.710. The result of hypothesis testing was 4.039, higher than the t-table, which is 1.9669, supporting H1. Information technology infrastructure thus appears to affect knowledge-sharing intentions, with a coefficient of determination of 15.2% (Table 5).
Quadratic regression suggests that an optimal level exists at which information technology infrastructure no longer moves in the same direction as knowledge-sharing intentions, and after reaching that level, greater information technology infrastructure leads to lower knowledge-sharing intentions. During the focus group discussions, all representatives of large service firms agreed that a firm should provide an information technology infrastructure, and when a firm provides a good one, workers can do their work well. Managers should thus consider other variables that increase knowledge-sharing intentions, which corroborates findings from Caroline et al. [48]. Leadership with a suitable communication style from managers and appropriate incentive systems that contribute to the firm are paramount to creating an atmosphere that triggers knowledge-sharing intentions among the workers. Those large service firms largely fall into the category of professional bureaucracies.
The correlation found between information technology infrastructure and knowledge-sharing intentions in the service sector is different in comparison to that discussed by Caroline et al. [20], suggesting a relationship (0.635) in family businesses. Most family firms do not provide information technology infrastructures to workers, who instead use existing tools that are most commonly not IT-based. When the COVID-19 pandemic occurred, family businesses needed to adapt processes, with information technology infrastructures provided to workers, for example, by providing a laptop with a link to Google Drive so that they could work from home [20]. At that time, workers believed that information technology infrastructure had a meaningful relationship with knowledge-sharing intentions. When supported by an effective information technology infrastructure, workers are eager to share knowledge. All representatives involved during focus group discussions reported that the COVID-19 pandemic has already become the new normal. An information technology infrastructure provided by a firm is a necessity, and it does not increase willingness to share knowledge. A disparity was found between professional bureaucracies and operating adhocracies (Table 6), supporting H2—and thus organization type appears to affect firms’ information technology infrastructures.
Findings from the focus groups suggest that in professional bureaucracies, each worker operates autonomously, coordinating with each other. To ensure standardization of workers’ skills, performance is monitored and based on standards from external professional bodies, with advantages, such as financial benefits, professional recognition, and career development, achieved if workers meet those standards. Workers are encouraged to write reports regularly, from which knowledge is codified to strengthen the firm’s knowledge. Thus, tacit knowledge from each worker is collected and transformed into knowledge that can be shared among workers, with the firm’s overall capabilities enhanced, allowing better provision of services and ensuring a positive reputation from the customer. Challenges occur when workers are unwilling to share knowledge, preferring instead to keep it to themselves to their own benefit.
Workers in operating adhocracies commonly come from various backgrounds in terms of education and capabilities, with problem-solving a crucial skill. Due to the project-based nature of the organization, job formalization and capability standardization are much less common in operating adhocracies, in comparison to professional bureaucracy. With strong collaboration among workers with different backgrounds, a firm can provide innovative solutions to customers, and in most cases, knowledge-sharing intentions are higher due to personal relationships and bonds among workers, instead of formal knowledge codification found elsewhere. Challenges to knowledge-sharing arise when workers do not have strong bonds with new team members, and thus they require more time to build trust and relationships.
Disparities between professional bureaucracies and operating adhocracies regarding information technology infrastructures derive from the network. Workers in professional bureaucracies emphasize ensuring network security more strongly, and configurations regarding who should be granted permission to create, read, edit, or delete data on the network are crucial and depend on job specialization. In operating adhocracies, workers perceive the network as a tool for collaborative work, and thus, comparatively, they do not require high network security.
Results from an independent t-test suggest that professional bureaucracies and operating adhocracies operate differently. Among three dimensions of knowledge-sharing intentions—ways of sharing knowledge, with whom knowledge is shared, and personal beliefs about sharing knowledge—only the first two contribute to differences between these two types of organizations. In terms of the ways knowledge is shared, disparate perceptions are evident between respondents from the two types of organizations, with people in professional bureaucracies holding lower perceptions about the ways knowledge is shared. Workers in professional bureaucracies share their knowledge using daily reports and other firm documents, since they are accustomed to following standard operating procedures regarding reporting job results. They are also less willing to create their own methodologies when sharing knowledge because they believe that each worker already possesses similar skills, and thus there is no need to create new ways of sharing knowledge. They are reluctant to share know-how, know-where, and know-whom, or share knowledge obtained from formal education or training certifications. This finding accords with characteristics of professional bureaucracies, in which work is autonomous and each worker’s skills are standard, following standards from external professional bodies. During focus group discussions, managers of professional bureaucracies reported needing to innovate when building trust and relationships among workers to trigger knowledge-sharing intentions. Greater structured reports that the firm needs also leads to lower intentions to share knowledge. Workers in banks, healthcare, and communications, which require complex, formalized reports, have lower intentions to share knowledge with others than do those who work in service firms with simple reports, such as logistics and automotive workshops.
In contrast, workers in operating adhocracies have higher knowledge-sharing intentions; they are not against sharing unstructured knowledge, such as personal experiences, and they share various results on delivering services to customers (i.e., know-how). They also have higher intentions of sharing contacts, and they are likely to cooperate if colleagues experience demands from customers (i.e., know-whom and know-where). The need for knowledge-sharing is much more common in operating adhocracies because they experience dynamic and unique demands from customers. To deal with more customers, consultancy firms require more workers, divided into senior and junior members. Junior workers deal with technical issues and support senior workers, who deliver strategic solutions. Regarding with whom knowledge is shared, workers in professional bureaucracies have lower intentions of sharing knowledge with workers across departments, even if they are friends outside of work; they perceive that it is irrelevant to share knowledge with other departments since each job requires different specifications. Maintaining confidentiality might also play a role in why they are reluctant to share knowledge. In operating adhocracies, workers are accustomed to working with various employees when delivering services to customers, and they have greater knowledge-sharing intentions regarding workers in other departments.

5. Conclusions and Practical Implications

When people were forced to change ordinary business processes of working from an office to working from home due to COVID-19, they were initially shocked. Firms’ efforts to provide effective information technology infrastructures so workers could work remotely became crucial, triggering a willingness to share knowledge. After becoming accustomed to the situation, information technology infrastructures did not play a role in improving knowledge-sharing. Service firm managers thus did not need to prioritize budgets to ensure more effective and up-to-date information technology infrastructures. After reaching an optimum level, information technology infrastructures play only a minor role in triggering greater knowledge-sharing intentions. Future research should therefore assess other factors, such as leadership, communication styles, and incentive systems. Managers should be aware of their organization’s characteristics, especially concerning professional bureaucracies versus operating adhocracies. Barriers to sharing knowledge are greater in professional bureaucracies, but despite this difference, managers from both organizational types should build trust and relationships with workers. Workers in professional bureaucracies have more limited authority to share their knowledge due to the firm’s rules and policies; for instance, client data that related to non-disclosure agreements. Focus group discussions suggest that this approach is much more effective to increasing knowledge-sharing intentions among workers.

6. Limitations and Future Research

Respondents’ perspectives when they needed to change work habits due to COVID-19, and their perspectives on the new normal, cannot be justified well. Future research should be conducted after the new normal is stable, not during an early stage, such as the current study, during which comparisons using the same respondents would generate more useful insights. In the service sector, the influence of information technology infrastructures is lower regarding knowledge-sharing intentions. Given workers’ low intentions regarding sharing knowledge, service industries cannot achieve an optimal advantage, and since trust and relationships between managers and workers are important, leadership might play a strong role in increasing such intentions.

Author Contributions

Conceptualization, D.A., A.G., J.S., Y.P.K. and A.C.; Methodology, D.A., A.G., J.S., Y.P.K. and A.C.; Software, D.A. and A.G.; Validation, D.A., A.G., Y.P.K. and A.C.; Formal analysis, D.A., A.G., Y.P.K. and A.C.; Investigation, D.A., A.G., Y.P.K. and A.C.; Resources, D.A., A.G., J.S., Y.P.K. and A.C.; Data curation, D.A. and A.G.; Writing—original draft preparation, D.A., A.G., J.S., Y.P.K. and A.C.; Writing—review and editing, D.A., A.G., J.S., Y.P.K. and A.C.; Visualization, D.A. and A.G.; Project administration, D.A. and A.G.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Operational Variables.
Table 1. Operational Variables.
VariableDimensionIndicatorIndex
Information technology infrastructure (ITI)
[10,21,22,45]		Hardware (ITI1)The hardware provided is always updated at least every two years.ITI1.1
The hardware provided makes me want to use it.ITI1.2
The hardware provided is adequate for the needs of the business unit.ITI1.3
Network (ITI2)There are facilities to access data on the firm’s computers from outside of the firm.ITI2.1
The network speed to access the firm’s data is adequate.ITI2.2
The firm’s network security is well maintained.ITI2.3
Software (ITI3)The software provided is suitable for knowledge sharing needs.ITI3.1
The software provided can be accessed through various tools (PC/laptop, mobile phone, etc.).ITI3.2
The software provided can speed up the completion time of work.ITI3.3
Database (ITI4)Data entered by a section can be accessed instantly by another section.ITI4.1
Data can be accessed by section in accordance with its authority.ITI4.2
Reports are tailored to the needs of each section.ITI4.3
Procedure (ITI5)The firm has clear regulations regarding knowledge sharing obligations.ITI5.1
The coding system, problem categorization or knowledge categorization in the firm is clear.ITI5.2
The firm focuses on collaborative work.ITI5.3
Support staff (ITI6)The firm provides a sufficient number of staff to help solve routine operational issues in using the existing system.ITI6.1
The firm provides a sufficient number of staff to create custom applications that can meet the needs of the business units.ITI6.2
Staff assigned to help to solve problems in business units related to systems are IT experts.ITI6.3
Knowledge-sharing intentions
[33,34,35,36,37]		Type of knowledge (KSI1)I am willing to share my work report and official firm documents.KSI1.1
I am willing to share manuals, methodologies, and models that I have created.KSI1.2
I am willing to share my work experience (know-how).KSI1.3
I am willing to share my work relationships (know-where or know-whom).KSI1.4
I am willing to share the skills gained from the education or training that I have attended.KSI1.5
Closeness (KSI2)I am willing to share knowledge with my close friends in the same department.KSI2.1
I am willing to share knowledge with anyone in the same department.KSI2.2
I am willing to share knowledge with my close friends working in different departments.KSI2.3
I am willing to share knowledge with anyone working in different departments.KSI2.4
Subject benefit knowledge (KSI3)I am willing to share knowledge if it is required by the firm.KSI3.1
I am willing to share knowledge if I believe it will be useful for my co-workers.KSI3.2
Table
Table 2. Validation Test Result.
Table 2. Validation Test Result.
No.IndicatorPearson’s rr-TableResultNo.IndicatorPearson’s rr-TableResult
1ITI1.10.515 **0.1053valid1KSI1.10.504 **0.1053valid
2ITI1.20.610 **0.1053valid2KSI1.20.593 **0.1053valid
3ITI1.30.647 **0.1053valid3KSI1.30.735 **0.1053valid
4ITI2.10.455 **0.1053valid4KSI1.40.729 **0.1053valid
5ITI2.20.562 **0.1053valid5KSI1.50.672 **0.1053valid
6ITI2.30.576 **0.1053valid6KSI2.10.653 **0.1053valid
7ITI3.10.756 **0.1053valid7KSI2.20.716 **0.1053valid
8ITI3.20.725 **0.1053valid8KSI2.30.734 **0.1053valid
9ITI3.30.778 **0.1053valid9KSI2.40.722 **0.1053valid
10ITI4.10.508 **0.1053valid10KSI3.10.547 **0.1053valid
11ITI4.20.553 **0.1053valid11KSI3.20.589 **0.1053valid
12ITI4.30.703 **0.1053valid
13ITI5.10.744 **0.1053valid
14ITI5.20.759 **0.1053valid
15ITI5.30.638 **0.1053valid
16ITI6.10.660 **0.1053valid
17ITI6.20.703 **0.1053valid
18ITI6.30.667 **0.1053valid
** p < 0.01.
Table
Table 3. Reliability Test Result.
Table 3. Reliability Test Result.
VariableCronbach’s AlphaNInference
ITI (X)0.91218Reliable
KSI (Y)0.85311Reliable
Note. ITI = information technology infrastructure; KSI = knowledge-sharing intentions.
Table
Table 4. Pearson’s Correlation Test Result.
Table 4. Pearson’s Correlation Test Result.
Sig.r
ITI → KSI0.0000.331
Note. ITI = information technology infrastructure; KSI = knowledge-sharing intentions; r = Pearson’s r coefficient.
Table
Table 5. Regression Test Result.
Table 5. Regression Test Result.
HypothesisR2tSig. Inference
ITI → KSI0.1524.0390.000Significant
Note. ITI = information technology infrastructure; KSI = knowledge-sharing intentions.
Table
Table 6. Independent t-Test Result.
Table 6. Independent t-Test Result.
Variable NMeanSDt-TestSig. (2-Tailed)
ITIProfessional bureaucracy2554.16730.16344−2.7140.007
Operating adhocracy924.22130.16347
Note. ITI = information technology infrastructure.
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MDPI and ACS Style

Afandy, D.; Gunawan, A.; Stoffers, J.; Kornarius, Y.P.; Caroline, A. Improving Knowledge-Sharing Intentions: A Study in Indonesian Service Industries. Sustainability 2022, 14, 8305. https://doi.org/10.3390/su14148305

AMA Style

Afandy D, Gunawan A, Stoffers J, Kornarius YP, Caroline A. Improving Knowledge-Sharing Intentions: A Study in Indonesian Service Industries. Sustainability. 2022; 14(14):8305. https://doi.org/10.3390/su14148305

Chicago/Turabian Style

Afandy, David, Agus Gunawan, Jol Stoffers, Yoke Pribadi Kornarius, and Angela Caroline. 2022. "Improving Knowledge-Sharing Intentions: A Study in Indonesian Service Industries" Sustainability 14, no. 14: 8305. https://doi.org/10.3390/su14148305

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