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This chapter applies three of the most prominent theories in vocational and career psychology to further illuminate the turnover process. Prevailing theories about attrition have rarely integrated explanatory constructs from vocational research, though career (and job) choices clearly have implications for employee affect and loyalty to a chosen job in a career field. Despite remarkable inroads by new perspectives for explaining turnover, career, and vocational formulations can nonetheless enrich these – and conventional – formulations about why incumbents stay or leave their jobs. To illustrate, vocational theories can help clarify why certain shocks (critical events precipitating thoughts of leaving) drive attrition and what embeds incumbents. In particular, this chapter reviews Super's life-span career theory, Holland's career model, and social cognitive career theory and describes how they can fill in theoretical gaps in the understanding of organizational withdrawal.

The overall goal of this chapter is twofold. First, the authors aim to identify indigenous phenomena that influence employee turnover and retention in the Chinese context. Second, the authors link these phenomena to the contextualization of job embeddedness theory. To achieve the goal, the authors begin by introducing three macro-level forces (i.e., political, economic, and cultural forces) in China that help scholars analyze contextual issues in turnover studies. The authors then provide findings in the literature research on employee retention studies published in Chinese academic journals. Next, the authors discuss six indigenous phenomena (i.e., hukou, community in China, migrant workers, state-owned companies, family benefit prioritization, and guanxi) under the three macro-level forces and offer exploratory propositions illustrating how these phenomena contribute to understanding employee retention in China. Finally, the authors offer suggestions on how contextualized turnover studies shall be conducted in China.

In today’s competitive industrial environment, it is essential that companies are able to focus on their core activities and collaborate with business partners to achieve the common objective of meeting the best satisfaction of customer demands. However, selecting partners based on accumulated experience may not be effective due to subjective judgement and lack of systematic analysis. This paper attempts to propose a partners benchmarking assessment system (PBAS) which incorporates computational intelligence technologies into partners’ benchmarking process to support decision making. Evidence suggests that the undesirables occur in companies such as extensive delays in the planned schedule, serious quality problems and cost overruns are, to a certain extent, related to the unfulfilled promises of business partners. In this paper, the PBAS is designed to propose an alternative approach to benchmark the business partners based on case‐based reasoning and neural network. To validate the proposed system, a prototype has been developed and tested in an emulated industrial environment. The case example is outlined with analysis of the feasibility of this proposed system based on test results.

This chapter describes how to use popular software programs (Hierarchical Linear Modeling, LISREL) to analyze multiwave panel data. We review prevailing methods for panel data analyzes in strategic management research and identify their limitations. Then, we explain how multilevel and latent growth modeling provide more rigorous methodologies for studying dynamic phenomena. We present an example illustrating how firm performance can initiate temporal change in the human and social capital of members of Board of Directors, using hierarchical linear modeling. With the same data set, we replicate this test with first-order factor latent growth modeling (LGM). Next, we explain how to use second-order factor LGM with panel data on employee cognitions. Finally, we review the relative advantages and disadvantages of these new data-analytical approaches.

The elements of sales comparison for residential property depend on a package of inherent attributes that are valued by consumers. These attributes can be classified into the following categories: structural, physical, neighbourhood and environmental. A model that incorporates adjustments for floor area, age, views and amenities such as availability of recreational facilities is presented and discussed. A multiple regression analysis with transactions‐based data, using weighted least square to determine the discrete estate‐type induced price effect in the Hong Kong housing market, is also presented. The paper demonstrates how the view of negative housing attributes is capitalised into house prices. Specifically, it is shown that the residential property values are higher for estate‐type housing properties, and lower for dwelling units with a cemetery view.

Research on employee mobility has proliferated in the past four decades across four research traditions: Economics, sociology, management, and organizational behavior/human resource management. Despite significant overlap in interest and focus, these four streams of research have evolved independent from each other, resulting in a structural divide. We provide a detailed account of the research on employee mobility and the structural divide across disciplines. We document that the payoff from this profusion of research and increasing interest has been disappointing, as reflected in the limited number of cross-disciplinary citations, even among common topics of interest. However, our analysis also provides some encouraging signs in the form of specific journals and individuals who provide a bridge for cross-disciplinary fertilization.

Turnover in high-tech companies has long been a concern for managers and executives. Recent meta-analyses from the general turnover literature consistently show that job satisfaction is a major attitudinal antecedent to turnover intention and turnover behavior. Additionally, the available research on information technology (IT) employees focuses primarily on turnover intentions and not on a risky decision-making perspective and actual turnover (turnover behavior). The paper aim is to focus on that.

This study uses hierarchical ordinary least squares, process (Preacher and Hayes, 2004) and logistic regression.

The main predictor of actual turnover is risky decision-making, whereas job satisfaction is the main predictor of turnover intention.

The joint effects of risk and job satisfaction on turnover intention and behavior have not been studied in the IT domain. Hence, this study extends our understanding of turnover in general and particularly among IT employees by studying the combined effect of risk and job satisfaction on turnover intentions and turnover behavior. The study’s theoretical and practical implications are likewise discussed.

M. Ronald Buckley holds the JC Penney Company Chair of Business Leadership and is a professor of management and a professor of psychology in the Michael F. Price College of Business at the University of Oklahoma. He earned his Ph.D. in industrial/organizational psychology from Auburn University. His research interests include, among others, work motivation, racial and gender issues in performance evaluation, business ethics, interview issues, and organizational socialization. His work has been published in journals such as the Academy of Management Review, Personnel Psychology, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, and the Journal of Management.

Welcome to the fourth volume of Research Methodology in Strategy and Management (RMSM). The publication of our fourth volume provides a source of satisfaction because our original contract with Elsevier only guaranteed a three volume run for the series. The popularity of RMSM led our contacts at the publisher to be eager to continue beyond their original commitment. We are excited about the future of the series, and have begun assembling Volume 5.