Testing the Empirical Relevance of the ‘Saving for a Rainy Day’ Hypothesis in US Metro Areas

The joint implication of the consumption Euler equation and cointegration between income and consumption is that savings predict future income declines, the ‘saving for a rainy day’ hypothesis. The empirical relevance of this hypothesis plays a key role in discussions of fiscal policy multipliers, and it holds under the null that the permanent income hypothesis is true. We find little support for this hypothesis using time series data for the 100 largest US Metropolitan Statistical Areas for the period 1980q1–2015q4. Our approach is to test for cointegration and weak exogeneity between income and consumption, and by exploring the direction of Granger causality between the two time series. We find that income more often predicts consumption and saving than the converse. We also give evidence that house price changes played a role in US income and consumption dynamics, before, during and after the Great Recession.     

How much are electric vehicles driven?

The prospect for electric vehicles as a climate change solution hinges on their ability to reduce gasoline consumption. But this depends on how many miles electric vehicles are driven and on how many miles would have otherwise been driven in gasoline-powered vehicles. Using newly-available U.S. nationally representative data, this paper finds that electric vehicles are driven considerably fewer miles per year on average than gasoline-powered vehicles. The difference is highly statistically significant and holds for both all-electric and plug-in hybrid vehicles, for both single- and multiple-vehicle households, and both inside and outside California. The paper discusses potential explanations and policy implications. Overall, the evidence suggests that today’s electric vehicles imply smaller environmental benefits than previously believed.     

Cross‐Functional Integration: Concept Clarification and Scale Development

The centrality of cross‐functional integration (CFI) to supply chain theory and practice has long been recognized. Yet researchers continue to struggle with consistently defining or measuring the CFI construct, thus limiting the utility of CFI research. This research develops (1) a comprehensive definition of CFI that synthesizes previous supply chain research and (2) a valid set of scale items that measure the conceptual domain outlined by this definition. The goal is to build a common foundation for extending knowledge on CFI's antecedents and consequences, and ultimately to improve scholars’ ability to guide a broader practitioner community still struggling to achieve integration in their organizations.     

Revisiting the expatriate failure concept: A qualitative study of Scandinavian expatriates in Hong Kong

This article advances the understanding of expatriate failure, which remains a contested social phenomenon in international work life as well as scholarly research. The study challenges the definition of expatriate failure and its inherent biases, i.e., the epistemological primacy of the firm level and the failure/success binary. We argue that this qualitative study of 51 Scandinavian expatriates in Hong Kong can contribute to advancing theory on the expatriate failure concept by asking individual expatriates what constitutes failure to them. By applying social constructionist and social anthropological ideas to the expatriate failure concept debate, we develop the internationality thesis which demonstrates a discrepancy between the expatriates’ perceptions of successful international assignments and the actual nature of their lived lives; many expatriates desire to enrich their lives through experiencing an international/intercultural and adventurous lifestyle, but, in fact, living lives with limited intercultural exposure and interaction. We conclude by proposing a reconceptualisation of expatriate failure in terms of offering both a new definition and approach to researching expatriate failure in which time/duration, context, and geographical location need to be taken into account. We believe the new approach can overcome some of the empirical unsoundness of mainstream definitions.     

Reconciling Alternative Theories for the Safety of Owner–Operators

Motor carrier safety is an important concern of shippers, carriers, policy makers, consignees, insurance providers, and the motoring public. One aspect of carrier safety that has garnered substantial attention is whether carriers making greater use of owner–operators are more or less safe vis‐à‐vis carriers making greater use of employee drivers. Currently, conflicting theoretical predictions exist regarding the direction of this relationship. In this article, we offer a reconciliation of the alternative theoretical predictions by developing a coherent theory that merges sociological rational choice theory and theory regarding motor carrier safety. We subject our theory to empirical testing by fitting a series of seemingly unrelated regression models to a vector of safety measures tracked as part of the Federal Motor Carrier Safety Administration's Compliance, Safety, and Accountability program. Our results are consistent with our proposed theory of owner—operator safety and provide meaningful theoretical and managerial implications and directions for future research.     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

Moderation by goal commitment of the feedback–performance relationship: Theoretical explanation and preliminary study

This paper presents goal commitment as one stage of a multidimensional goal striving process that affects feedback acquisition, processing, and use during the achievement of performance goals. Specifically, this paper argues that higher goal commitment leads to more effective acquisition, processing, and use of feedback that in turn fosters higher performance than does lower goal commitment. To provide a preliminary test of this proposition, data from an earlier field study conducted by the author and colleagues [J. Occup. Organ. Psychol. 72 (1999) 107] were used to examine the moderating effects of goal commitment on the relationship between perceived task feedback amount and work performance of 196 rehabilitation counselors. Results were consistent with this proposed interaction, as perceived task feedback amount had a positive linear relationship with work performance for higher goal commitment rehabilitation counselors and a negative linear relationship with performance for lower goal commitment counselors. The discussion urges researchers to broaden their views of goal commitment beyond the goal difficulty–performance relationship and to devote more attention to moderators of the feedback–performance relationship. Directions for future research and practical implications are also discussed.     

Predicting Household Poverty: A Methodological Note with a Kenyan Example

Household surveys provide data that is used for identifying and measuring the poverty status of households and individuals. However, carrying out such surveys is expensive, especially in poor developing countries. Thus it is important to make maximum use of the available survey data in developing countries, especially in sub‐Saharan Africa, where such data are expensive to collect and analyse. This paper develops a simple method for using poverty indices derived from survey data for a given year, to predict poverty rates for subsequent periods without having to conduct a new household survey. We illustrate the workings of the method with data from Kenyan household surveys for 1994 and 1997.