Social media affordances in entry‐level employees’ socialization: employee agency in the management of their professional impressions and vulnerability during early stages of socialization

This study examined how entry‐level employees interacted with social media during three stages of organizational socialization. They navigated between four different media affordances (persistence, editability, visibility, and association) while experiencing them as both enabling and constraining in different socialization stages. Qualitative interview data analysis revealed during anticipatory socialization, job applicants realized visibility and persistence in relation to institutional and individualized socialization. During encounter, new employees managed personal and professional life boundaries carefully against the association and visibility affordances. Although some participants used both public and enterprise social media for obtaining job‐related information and understanding coworkers and company culture, during metamorphosis, most interviewees adopted passive information seeking strategies and experienced a paradoxical tension between the enabling and constraining affordances of social media. Findings are discussed with regards to employees’ exertion of agency in managing their professional impressions and coping with high levels of uncertainty and vulnerability during early stages of socialization.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

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.     

INSURANCE‐INDUCED MORAL HAZARD: A DYNAMIC MODEL OF WITHIN‐YEAR MEDICAL CARE DECISION MAKING UNDER UNCERTAINTY

This study quantifies the moral hazard effect of health insurance on medical expenditure by estimating a dynamic model of within‐year medical care consumption that allows for insurance selection, endogenous health transitions, and individual uncertainty about medical care prices in an environment where insurance has nonlinear cost‐sharing features. The results suggest that moral hazard accounts for 53.1%, on average, of total annual medical expenditure when insured. This estimate is significantly different, and generally larger, than that produced by an alternative model that is representative of the annual medical care decision‐making models commonly found in the literature.     

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.     

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.     

Telling their own stories: Māori entrepreneurship in the mainstream screen industry

We examined how factors from Indigenous entrepreneurship research (social capital, cultural capital, self-efficacy) help explain the high level of Māori entrepreneurial performance in the mainstream screen industry. Results, based on ten case studies and a one-year series of structured interviews, extend prior research by showing that these Indigenous entrepreneurs benefit jointly from two forms of capital: cultural and social. We found high levels of both forms to increase the desire for emancipation of cultural and community identity – not just individual identity – through entrepreneurship. Self-efficacy and storytelling helped ameliorate discontinuities across Indigenous and mainstream contexts. Our research sheds new light on how Indigenous ventures can pursue mainstream entrepreneurship while maintaining cultural identity. It also makes several distinct contributions to the Indigenous entrepreneurship literature. First, it provides an integrative theoretic review. Second, it illustrates a culturally appropriate methodology for researching Māori entrepreneurs with implications for other Indigenous communities. Third, it proposes cultural capital and social capital as a two-part framework for explaining Indigenous entrepreneurial action. Fourth, it shows how entrepreneurship can be empowering for Indigenous communities. Finally, our paper demonstrates that entrepreneurship is a promising mechanism for preserving and promoting the cultures of Māori and other Indigenous peoples.     

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.     

There and Back Again: The De‐Licensing and Re‐Licensing of Barbers in Alabama

The economic effects of occupational licensing remain an understudied topic, but even less is known about the effects of the removal of licensing legislation. In this article, we take advantage of a natural experiment that occurred in the state of Alabama. Alabama was the last state to begin licensing barbers in 1973 and also the only state to de‐license barbers (in 1983). Relying on data from 1974 to 1994, we find evidence that barber de‐licensing reduced the average annual earnings of barbers as well as the number of cosmetologist employees per million residents in Alabama, although not all our results are statistically significant. We also find evidence that de‐licensing resulted in small increases in the number of barber shops and decreases in the number of cosmetology shops in Alabama. In recent decades, a number of attempts have been made to re‐license the occupation — most recently with a barber licensing bill that became law in September 2013. The result is that barbering in Alabama is once again a licensed occupation. Our limited evidence suggests that the re‐licensing of barbers in Alabama may already have had an effect on pay and on the number of barber shops.