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Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

Purpose – This chapter (a) summarizes leader–member exchange (LMX) measurement practices since the influential reviews by Schriesheim, Castro, and Cogliser (1999) and Gerstner and Day (1997), (b) clarifies the status of LMX as a broad construct from a hierarchical factor model, (c) conducts multitrait-multimethod (MTMM) analyses on leader and follower reports of multidimensional LMX, and (d) investigates discriminant validity between Member LMX and satisfaction with supervisor.

Methodology/Approach – We used (a) a literature search of LMX measurement practices, (b) a combination of meta-analysis and factor analysis to specify the broad LMX construct underlying Liden and Maslyn's (1998) (LMX-MDM) multidimensional instrument, (c) MTMM analyses of leader and member ratings of the LMX-MDM, and (d) a combination of meta-analysis and multiple regression to assess incremental validity of Member LMX beyond satisfaction with supervisor.

Findings – Since 1999, 85% of LMX studies now use one of two dominant LMX scales (LMX-7, Scandura, & Graen, 1984; LMX-MDM, Liden & Maslyn, 1998). These two measures are correlated (rcorrected=.9), suggesting the LMX-7 and the LMX-MDM are alternate forms of the same instrument. 94% of studies that used these two measures treat LMX as a single, broad construct rather than as a multidimensional set of constructs. MTMM analyses suggest Leader LMX and Member LMX are two, separate-but-related constructs (i.e., confirming two source factors and no lower-order trait factors). Last, Member LMX meta-analytically correlates with satisfaction with supervisor at rcorrected=.8. There is some incremental validity of LMX, but the pattern is inconsistent across samples.

Social Implications – We point out that LMX researchers have now moved toward standard measurement of LMX – as a broad, higher-order factor that varies between leader and follower. By doing so, we reveal that the stage is set for cumulative and replicable research on leadership as a dyadic, follower-specific phenomenon.

Originality/Value of Paper – Our chapter is the first to reveal consensus in LMX measurement across studies; to summarize the standard treatment of LMX as a single, broad factor; and to apply MTMM analyses to demonstrate separate Leader LMX and Member LMX source factors.

The imperatives of globalization are clear across many industries: firms must look to expand into international markets to survive and thrive. This study complements and extends a growing body of work developing and using overall market opportunity indexes (OMOIs) based on Cavusgil (1997) to rank the attractiveness of potential foreign markets. The index developed in this paper assesses countries’ market potential beyond the traditional measures of market size and economic development by also including political risk, economic freedom, telecommunications as well as physical infrastructure and geographic distance. We provide a current analysis of market attractiveness and opportunity for the largest set of countries indexed and ranked to date, including 24 countries not in previous OMOI studies. The validity of the OMOI is also so assessed for the first time by comparing the ranking of market opportunity to actual subsequent trade flows from the US. Furthermore, we compare the dimensions, variables, samples and results from three of Cavusgil and colleague's previous studies and the two conducted herein. The choice of sample and, to a lesser degree, weights are shown to directly affect the OMOIs and rankings. The modified OMOI is shown to be a flexible, valid and fairly stable tool for preliminary analysis of foreign market opportunity.

This literature review is on advanced computer analytics, which is a major trend in the field of Human Resource Management (HRM). The authors focus specifically on computer-assisted text analysis (CATA) because text data are a prevalent yet vastly underutilized data source in organizations. The authors gathered 341 articles that use, review, or promote CATA in the management literature. This review complements existing reviews in several ways including an emphasis on CATA in the management literature, a description of the types of software and their advantages, and a unique emphasis on findings in employment. This examination of CATA relative to employment is based on 66 studies (of the 341) that bear on measuring constructs potentially relevant to hiring decisions. The authors also briefly consider the broader machine learning literature using CATA outside management (e.g., data science) to derive relevant insights for management scholars. Finally, the authors discuss the main challenges when using CATA for employment, and provide recommendations on how to manage such challenges. In all, the authors hope to demystify and encourage the use of CATA in HRM scholarship.

In this study, the construct validity and effectiveness of a newly identified influence tactic, organizational appeal, is tested. Utilizing a sample of practicing professional accountants, study results show that organizational appeal is distinct from other influence tactics, is perceived to be used frequently by supervisors, and is effective at influencing subordinates. The organizational appeal influence tactic could be particularly useful in situations where accounting supervisors and managers use proactive tactics to influence others to complete tasks or make decisions; to influence outsiders (e.g., suppliers, clients, government agents) over whom they have little authority; and where other influence tactics are not effective or appropriate.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.

Research on the relationship between high job satisfaction human resources (HR) practices and work-related outcomes at the individual level rarely focuses on job crafting and levels of job passion. To fill this knowledge gap, we show how the relationships between perceived job passion by employees and the availability of HR practices and job satisfaction can vary in job crafting. Based on the job demands–resources (JD-R) perspective, this study was conducted primarily to test the effect of job crafting on the relationship between job passion and job satisfaction. Within the scope of the study, the mediator and moderator role of job crafting between these variables was investigated. The study was collected from 790 people working as blue collars in companies operating in the field of industry in Turkey by survey method. According to the findings of the study, it was observed that job crafting affects job satisfaction in the same direction. In addition to these, the partial mediator and moderator role of job crafting has been observed in the effect of job passion on job satisfaction. Considering these results, it has been observed that the passion for work of blue-collar employees affects job satisfaction. When job crafting is added to this effect, the intensity, strength, and direction of the effect between two variables can change. As a result of the study findings, it has been shown that managers and especially HR managers will strengthen the effect between job crafting and job satisfaction by ensuring the passion of blue-collar individuals.