Many CEOs and managers understand the importance of knowledge sharing among their employees and are eager to introduce the knowledge management paradigm in their organizations. However little is known about the determinants of the individual’s knowledge sharing behavior. The purpose of this study is to develop an understanding of the factors affecting the individual’s knowledge sharing behavior in the organizational context. The research model includes various constructs based on social exchange theory, self-efficacy, and theory of reasoned action. Research results from the field survey of 467 employees of four large, public organizations show that expected associations and contribution are the major determinants of the individual’s attitude toward knowledge sharing. Expected rewards, believed by many as the most important motivating factor for knowledge sharing, are not significantly related to the attitude toward knowledge sharing. As expected, positive attitude toward knowledge sharing is found to lead to positive intention to share knowledge and, finally, to actual knowledge sharing behaviors.
This article helps identify the main factors influencing the performance of small and medium agribusiness enterprises in Kenya. The study proposes five research hypotheses, each tested on a sample of 150 agribusiness enterprises using multiple regression analysis. The results show that the use of external partners, such as scientific research establishments and commercial consultants, influences the firm's performance. This influence is moderated by factors like internal capabilities and the firm's degree of openness to innovation.
The integration of social media and e-commerce leads to the emergence of social commerce. Although previous research has examined social commerce user behaviour from multiple perspectives, it has focused on the effect of instrumental beliefs, such as perceived value, and has seldom examined the effect of emotional factors, such as sense of community on user behaviour. The purpose of this research is to draw on the stimulus-organism-response (SOR) model to examine the effect of sense of community on users' social commerce usage intention. The results indicate that both social support and service quality (stimulus) affect the sense of community (organism), which in turn affects users' sharing and participation intention (response). The results imply that service providers need to develop the user's sense of community in order to facilitate his or her social commerce usage intention.
This study is designed to examine factors influencing the acceptance of m-learning apps by entrepreneurs. The constructs used to develop the proposed model were drawn from the social cognitive and motivational theories. The model was validated using 218 valid responses from entrepreneurs in South Africa. The results showed that both intrinsic (i.e. perceived enjoyment) and extrinsic (i.e. perceived usefulness and social influence) motivational factors had a direct positive influence on the behavioral intentions to adopt m-learning apps. Also, perceived usefulness, which showed the strongest direct influence on behavioral intentions, was directly influenced by outcome expectancy, and indirectly influenced by self-efficacy. The study also evaluated the outcomes of use behavior. For one, entrepreneurs who used m-learning apps were more likely to recommend the m-learning apps to others. Moreover, use behavior was shown to have a significant positive influence on entrepreneurial self-efficacy.
To better assist decision-makers (e.g., enterprise executives) in selecting the most desirable IT portfolio, this study proposes a new IT Portfolio Efficient Frontier model that incorporates the decision-maker's risk tolerance levels. The proposed model, built on portfolio optimization along with experimental design and simulation data, considers three IT portfolio scenarios: even distribution-based IT portfolios, uneven distribution-based IT portfolios, and dominant IT portfolios. Our findings show that the IT portfolio efficient frontiers derived from both an even distribution-based IT portfolio and an uneven distribution-based IT portfolio have a relatively positive relationship between IT portfolio risk and return. Our findings also indicate that if IT investments are part of a dominant IT portfolio, an inflection point of the IT portfolio efficient frontier appears under the decision-maker's medium risk tolerance level, and the most desirable IT portfolio is generated when a decision maker's risk tolerance level is medium or higher.
This article aims to empirically investigate the factors that affects the adoption of big data analytics by firms (adopters and non-adopters). The current study is based on three feature that influence BDA adoption: technological context (relative advantage, complexity, compatibility), organizational context (top management support, technology readiness, organizational data environment), and environmental context (competitive pressure, and trading partner pressure). A structured questionnaire-based survey method was used to collect data from 231 firm managers. Relevant hypotheses were derived and tested by partial least squares. The results indicated that technology, organization and environment contexts impact firms' adoption of big data analytics. The findings also revealed that relative advantage, complexity, compatibility, top management support, technology readiness, organizational data environment and competitive pressure have a significant influence on the adopters of big data analytics, whereas relative advantage, complexity and competitive pressure have a significant influence on the non-adopters of big data analytics.
Network-centric sharing of data between all Air Traffic Management (ATM) stakeholders can improve the aviation network substantially. The System Wide Information Management (SWIM) platform is a platform for the open sharing of all information between aircraft operators, airports, air navigation services providers (ANSPs), and meteorology services, but has struggled to find a following. This article aims to identify the potential reasons for the slow adoption of the SWIM platform, and to investigate how to better communicate its potential. To gain insight into the drivers for each of the stakeholders, a series of semi-structured interviews was conducted with airlines, airports and ANSPs. Moreover, an Airport Collaborative Decision Making (A-CDM) initiative at the airport in Dublin was included as a case study. Recommendations are provided on how to address the results from a governance point of view.
This article aims to examine how hotel responses to online reviews influence how potential consumers perceived the helpfulness of the online reviews. Response length and voice were employed to measure the hotel's response quality. 637 reviews with responses were used for empirical analysis. The study identified three different types of response voices: disputed voice, professional voice, and empathetic voice. The results show that both response length and response voice have significant effects on the helpfulness perceived by potential consumers. Moreover, they also have some interaction effects with star ratings, review length, and review image. This study suggests that hotels should strategically respond to both positive and negative online reviews so as to both create a positive interaction atmosphere and resolve consumer complaints. The findings of this study can, to some extent, help manage word of mouth reputations.
This article examines the use intention of open banking technology in the context of an emerging economy like India using the theoretical framework of Technology Readiness and Acceptance Model (TRAM). To study the use intention for open banking in India, a primary survey was conducted among 945 customers using a structured questionnaire. The PLS-SEM technique was used to analyze the data. Findings – The results show that Optimism (OPM) contributes positively to the Perceived Ease of Use (PES) and Perceived Usefulness (PUF) of the open banking technology. Innovativeness (INO) of the customers is a significant predictor of PES and PUF. It was found that Discomfort (DCF) negatively contributes to PES and PUF; however, it significantly influences PES and has no significant influence on PUF. Insecurity (INT) is negatively significant to PUF and it has no significant influence on PES. It is observed that PES positively contributes to PUF. The results show that PES and PUF are significant predictors of Perceived Customer Value (PCV). PCVs contribution to the Use Intention (UNT) is significantly positive for open banking technology. The stickiness to traditional banking moderates the relationship between PCV and UNT for open banking.
This article investigates individual knowledge sharing behaviors (KSBs) in company-hosted virtual communities of practice (CVCs), where KSBs are guided mainly by the sense of virtual community (SVC) and the shared meanings that are formed via the recurring communicative patterns and emotional responses in the interpersonal communication processes. The symbolic convergence theory (SCT) addresses the significance of the shared meanings of a social group in facilitating the harmony among and the favorable behaviors of the members of the group. By adopting SCT, the authors examine the effects of SVC and shared-meaning-related factors on KSBs in CVCs. Data collected from 159 CVC participants were analyzed to examine the research model. The authors found that shared language and shared emotional connection significantly influence KSBs both directly and indirectly via SVC. The research findings highlight the importance of achieving shared meaning among individuals in CVCs to encourage interpersonal knowledge sharing via effective communication processes.
Small to medium-sized enterprises (SMEs) in North America do not always adequately address security. Based on responses from 232 SME owners and managers, the authors found that the adoption of security recommendations made by experts appear to be significantly influenced by the decisions of other local SMEs. A hot-spot analysis of information security practices suggested that local trends lead to prioritizing certain security practices and not adopting others. Follow-up interviews with business owners and Chamber of Commerce directors provided insights on how security hotspots developed or not. The study identified both hot spot and cold spot communities, and sought to assess how local business networking conduits like chambers of commerce help promote best security practices
This article investigates the adoption of Industrial Internet-of-Things (IIoT) in Auto-Component Manufacturing SMEs (ACM SMEs) in the context of a developing country like India by using the Technology-Organization-Environment (TOE) framework. This research surveyed Information & Communication Technology (ICT) officers, managers and owners of 320 ACM SMEs in India using a structured questionnaire to understand the adoption of IIoT. The primary data was analyzed using the PLS-SEM technique. It was found that IIoT expertise, IIoT infrastructure, relative advantage, compatibility, cost, security, organizational readiness, top management support, competitive pressure, and support from technology vendors are factors that affect the adoption of IIoT. This article considered organization size as the control variable. The results show that it does not have a significant effect on the adoption of IIoT. ACM SMEs are one of the important sectors adopting IIoT. This article provides valuable insights to their managers and IIoT vendors. It also suggests key inputs to the government officials involved in the ‘Make in India' initiative of Government of India (GoI) and Ministry of Micro, Small and Medium Enterprises (MSME).
The best companies compete with people analytics. They maximize the business value of their people to gain competitive advantage. This article proposes a network data science approach to people analytics. Using data from a software development organization, the article models developer contributions to project repositories as a bipartite weighted graph. This graph is projected into a weighted one-mode developer network to model collaboration. Techniques applied include centrality metrics, power-law estimation, community detection, and complex network dynamics. Among other results, the authors validate the existence of power-law relationships on project sizes (number of developers). As a methodological contribution, the article demonstrates how network data science can be used to derive a broad spectrum of insights about employee effort and collaboration in organizations. The authors discuss implications for managers and future research directions.
This research investigates the software vendor-based relationships between software vulnerability and application security risk. The data is obtained from the China National Vulnerability Database of Information Security (CNNVD). At first, we use the latent class model to classify the software vendors into three categories, and then employ regression models to estimate relationships between software vulnerability and application security risk for each of the three categories of the software vendors. The results show the relationships vary across the software vendors. The findings suggest that an IT vendor should learn specific vulnerability features according to its type to effectively avoid vulnerability generation on their products.
Sustainability reporting is now a best practice for most companies around the world. The Web, with its advanced technologies enables companies to create a positive impact on stakeholder perceptions of transparency in reporting. Using empirical data from websites of companies in the Dow Jones Industrial Average, the authors analyze design and content of sustainability information. We conclude that while nearly all companies provide routine content disclosure using minimal web design features, they stop short of issuing a proactive display of compliance and due diligence content. Companies with higher Newsweek Green Ranking scores employ better disclosure than others. This asymmetry in self-governance arises from a combination of factors, including low level of transparency, incomplete information, and a lack of comparability of sustainability reports. The authors develop a prescriptive framework to help companies improve their web sites by including more content that reflects due diligence and transparency for sustainability.
The aim of the research is to study the employee engagement strategies utilised in Taiwan's SMEs from the perspective of the employees. In doing so, a qualitative research method was employed using semi-structured interviews. Nine participants from five sectors of SMEs (electronics & information, metal transportation, machinery and equipment, food manufacturing, and textile) were interviewed. The authors' results reveal that having high levels of engagement amongst employees in Taiwan's SMEs will bring about an increase in work commitment amongst employees which encourages productivity for the organisation. These findings hold several important theoretical and practical implications.
This article explores the process by which e-mentoring unfolds in organizational settings, emphasizing the crucial role of learning that acts as the intermediate step between mentoring functions and organizational outcomes. Specifically, the authors investigate how e-mentoring functions—career support, psycho-social, and role modeling—support the protégé's learning, and how learning increases organizational commitment of the protégés. Data was collected through an online survey of 59 individuals in two multinational organizations that had mentoring programs. Analysis using SmartPLS 3.0 showed that career and role modeling support are statistically significant predictors of learning, and learning predicts organizational commitment as expected. Contrary to face-to-face mentoring literature, the direct link between the mentoring functions and organizational commitment is not supported. The authors conclude with implications for research and practice.
This article sought to identify the drivers of Big Data adoption within the manufacturing and services sectors in India. A questionnaire-based survey was used to collect data from manufacturing and service sector organizations in India. The data was analyzed using exploratory and confirmatory factor analyses. Relevant hypotheses were then derived and tested by SEM analysis. The findings revealed that the following factors are important for both sectors: relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management and data privacy. Statistically significant differences between the service and the manufacturing sectors were found. In other words, the relative importance of the factors for Big Data adoption differs between the sectors. The only exception was complexity, which was found to be insignificant in regard to the manufacturing sector. The factors identified can be used to facilitate Big Data adoption outcomes in organizations.
To improve their long-term performance, organizations must maintain their business operations and practices over time. They can do so by engaging in sustainable practices aimed at meeting the interests of the enterprise, and of its suppliers, employees, and customers in the long run. Not surprisingly, the implementation of sustainability practices has expanded in the healthcare industry. Information technology (IT) is a way to promote quality, security, and efficiency in healthcare. IT brings vital information, and so important support to the care point for decision-making. It also allows the assessment of everyday quality turn into as a measured reality. In the present study, the factors affecting the sustainability of electronic supply chains in healthcare centers were identified using library methods and a keyword review of the literature. Then, the relationships between these factors were analyzed using an interpretive- structural modeling approach. The results reveal that infrastructure management and technology management should be considered the most important factors affecting the sustainability of electronic supply chains in healthcare centers.
Security systems are often the target of cyber-criminals and professional hackers, but often they fail in hiding all traces of the attack, thereby leaving critical evidence that could lead to identifying and arresting the criminal. However, hacking skills vary from one hacker to another depending on the hacker's personal traits, behavior, and intellectual tendencies. The aim of this study is to develop a proposed descriptive model of the behavioral patterns and motives of hackers based on programmable psychological theories, modeled using object-oriented programming models. The study proposes a descriptive model of an inverse algorithm that simulates Holland's Theory of Behavioral Patterns. Findings show that this descriptive model is applicable to be produced as a code map for the human resources of an investigative nature.