Search results

This research aims to explore the dark side of mobile applications by investigating the role of apps' technicality and app security in the mechanism of user satisfaction, app intention and customers' continuance tendency to make in-app purchases.

Drawing on attitude-behavior-context (ABC) theory, the study proposed a conceptual framework and examined the framework using a structural equation modeling (SEM) approach based on data collected from app users from New Zealand.

The results reveal the correlation between user satisfaction and in-app purchase with a mediator of app continuance intention (ACI). In particular, the results show that app technicality (AT) has a positive correlation with user satisfaction as an antecedent. App security and hedonic value are positively correlated with user satisfaction.

The research has three critical research implications. First, this research advances the understanding of the dark side of mobile apps by showing how app security influences customers' in-app purchases. Secondly, this study reveals and offers empirical evidence for the mechanism between app security and user satisfaction. Finally, the study provides empirical evidence of AT as a distal antecedent for in-app purchases.

This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information.

This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”.

A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations.

This is an opinion piece, and the proposed model still needs to be empirically verified.

It is suggested that academics and practitioners in KM must be capable of controlling the application of big data/analytics, and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation.

The BDA-KM model is one of the early models placing knowledge as the primary consideration in the successful organizational use of big data/analytics.

Drawing on a strategic agility perspective, the authors develop a theoretical framework and empirically examine how digital platform adoption and capability impact business performance via digital-enabled strategic agility in the context of professional service firms.

The authors propose and examine a conceptual framework based on survey data from 127 professional service firms in New Zealand.

This study reveals the impact of digital platform capability on the business performance of professional service firms that employ digital platform technologies. The results suggest that organizational innovation and managers' creative efficacy will be used as distal antecedents and contribute to digital platform capabilities. In addition, digital strategic agility can mediate the link between digital platform capabilities and business performance.

This study is one of the first to investigate when and how digital platforms empower professional service firms. This study reveals the role of digital strategic agility and digital platform capabilities in knowledge-intensive enterprises. This research advances the development of knowledge-based economy in the information age by applying and extending strategic agility to the uncertain and volatile business environment. The authors' new conceptualization provides a deeper understanding of how and why professional services business and organizations can adapt to the post-COVID era smoothly and successfully.

This study aims to identify the main knowledge processes associated with organizational knowledge culture. A diverse range of knowledge processes have been referred to in the extant literature, but little agreement exists on which knowledge processes are critical and should be supported by organizational culture.

Using a systematic literature review methodology, this study examined the primary literature – peer-reviewed and scholarly articles published in the top seven knowledge management and intellectual capital (KM/IC)-related journals.

The core knowledge processes have been identified – knowledge sharing, knowledge creation and knowledge implementation. The paper suggests that a strategy for implementing successful organizational KM initiatives requires precise understanding and effective management of the core knowledge infrastructures and processes. Although technology infrastructure is an important aspect of any KM initiative, the integration of knowledge into management decisions and practices relies on the extent to which the organizational culture supports or hinders knowledge processes.

The focus of the study was on the articles published in the top seven KM/IC journals; important contributions in relevant publications in other KM journals, conference papers, books and professional reports may have been excluded.

Practitioners will benefit from a better understanding of knowledge processes involved in KM initiatives and investments. From a managerial perspective, the study offers an overview of the state of organizational knowledge culture research and suggests that for KM initiatives to be successful, the organization requires an integrated culture that is concerned with knowledge processes as a set of inextricably inter-related processes.

For the first time, a comprehensive list of diverse terms used in describing knowledge processes has been identified. The findings remove the conceptual ambiguity resulting from the inconsistent use of different terms for the same knowledge process by identifying the three major and overarching knowledge processes. Moreover, this study points to the need to attend to the inextricably interrelated nature of these three knowledge processes. Finally, this is the first time that a study provides evidence that shows the KM studies appear to be biased towards Knowledge sharing.

This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not.

This study is based on an event study using data from two stock markets in China.

The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along.

This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets.

Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

Current research provides an incomplete picture of the challenges facing human resource personnel (HRP) tasked with managing a workplace bullying complaint. The purpose of this paper is to provide a holistic model of the complaint management process in order to advance the theorising of HRP’s role in this important process, and the challenges they face in undertaking it.

Cases of workplace bullying heard before the legal system were analysed – a novel data source in research on workplace bullying. Thematic analysis was undertaken on the case determinations to identify the challenges HRP faced that prevented the resolution of the complaint.

The analysis indicated two key phases in the complaints management process with five associated challenges. The first two challenges were related to HRP’s ability to assess the substance of the complaint. HRP’s ability or inability to “sort out” conflicting accounts and to follow the process saw the complaint follow one of three “resolution pathways”. Three further challenges were associated with HRP communicating the outcome to the complainant. Failure to overcome these challenges left the complainant aggrieved at the unfairness in which their complaint had been handled – triggering legal action.

This paper draws on a novel data source to provide a holistic model of the complaint management process related to workplace bullying which details the various components and challenges related to HRP throughout the process. Alongside advancing theory, this research has practical value for improving HR practice.

This paper aims to investigate whether corporate social responsibility (CSR), as evidenced in annual financial reports, is associated with a firm’s financial performance in New Zealand.

A word count approach of several key CSR indicators found in the audited financial reports of NZX50 constituent firms is used. Several variables are constructed that measure the presence of CSR within the annual report such as sustainability, responsibility, social, environment, diversity, employee and community, and eight other variables within the annual report that measure the penetration of stakeholder engagement. Control variables and alternative measures of CSR are also included. Descriptive statistics and results of both univariate and multivariate tests are provided.

The findings establish a positive connection between CSR and financial performance. It is shown that firms that are unable to focus their attention on key stakeholders, but instead waste managerial capital on vague social policies and activities, are associated with weaker performance. Firms that consider the protected indigenous peoples as key stakeholders are associated with superior performance, especially when the firm is seeking regulatory approval.

Evidence is provided that CSR and Maori stakeholder engagement is implied by financial reports that have a significant association with corporate financial performance.

The results provide one of the first analyses linking the interplay between CSR, Maori and corporate financial performance using information publicly observable in annual financial reports. Evidence of an association between firms that indicate awareness of their community and higher levels of return on assets (ROA) is provided.

This paper aims to examine alternative methods for recording and treating costs in studies of bank efficiency.

This study used stochastic frontier analysis (SFA) models with core costs and total costs to estimate the cost efficiency of banks in two different economies, Vietnam where the banking system is under-developed (and thus is dominated by traditional banking activities) and New Zealand where the banking system is well-developed (and thus non-traditional banking activities play an important role).

The authors found that models using total cost tend to underestimate the banks’ cost efficiency. This underestimation relates to the extent of modern activities in a banking system: it is larger in an advanced banking system (i.e. New Zealand) and smaller in a less-developed banking system (i.e. Vietnam).

Research is limited to two countries, and it would be useful to apply the same technique to other data sets.

The paper suggests a new approach to cost SFA studies in banking.

The paper provides a much more searching analysis of costs in banking than has generally been seen in previous research.

This paper aims to examine alternative methods for treating nonperforming loans (NPLs) in bank cost-efficiency studies using stochastic frontier analysis (SFA).

The authors consider three methods of treating NPLs in SFA: as an additional control variable, as an environmental factor or as a deduction from total loans. Using data from the Vietnamese banking system (2003-2010), the authors then compare these results with those of the base model (where total loans is used regardless of the NPLs) to see which one is more appropriate for this study.

The authors observed that the first two methods are inappropriate for the analysis: one cannot find the significant relationship between NPLs and the banks’ total cost, and the other cannot account for any inefficiency at all. The authors suggested that the third method of separating NPLs from total loans can provide better insights. Using the proposed method, the authors showed that the cost-efficiency of Vietnamese banks over the period examined was moderate with a slight decreasing trend. When NPLs are separated, the cost-efficiency decreases in state-owned banks and big banks, whereas it increases in small and private banks.

Research is limited to Vietnamese banks during a certain period, and it would be useful to apply the same technique to other data sets.

The paper suggests a new approach to account for NPLs in cost SFA studies in banking.

The paper provides a much more searching analysis of NPLs in banking than has generally been seen in previous research.

The outbreak of COVID-19 is one of the most serious health events in recent times. In the business landscape, its effects may be more detrimental to micro-, small- and medium-sized enterprises (MSMEs) because they tend to have limited financial and human resources to manage the challenges caused by COVID-19. To help MSMEs enhance their resilience, this paper aims to discuss how they can leverage mass collaboration to build social media-based knowledge ecosystems to manage interactions among internal and external stakeholders for knowledge creation and innovation.

The paper proposes a model for MSMEs to build an online knowledge ecosystem and a standalone text analytics tool to use the advanced data analytics, e.g. topic modeling, to analyze and aggregate collective insights. Design science research methodology is used to develop the model and the tool.

Through mass collaboration using social media and advanced data analytics technology, MSMEs can generate new business ideas, leading to enhanced resilience to meet the challenges caused by COVID-19 or other unexpected or extraordinary circumstances, such as natural disasters and financial crises.

To the best of authors’ knowledge, this paper is one of the first papers in social media adoption for knowledge creation and innovation research, providing detailed approaches for MSMEs to build a knowledge ecosystem on social media and to use advanced data analytics to mine the meaning of the generated data.