Using the Pack‐and‐a‐Half Rule to Eliminate Backroom Inventories in Retail Operations

In the retail industry, backroom inventories are typically associated with higher labor costs and greater operational complexity. Thus, retailers look for ways to eliminate backroom inventories. A heuristic used for this purpose is the pack‐and‐a‐half rule which suggests that the shelf space allocated for a product should be at least 50% larger than the case pack quantity in which the product is delivered. Despite its popularity among retailers, the pack‐and‐a‐half rule has been ignored in the academic literature. We introduce the pack‐and‐a‐half rule, assess its impact on a retailer's profits, identify cost, demand, and product characteristics driving this impact, and propose a modification. Based on an analysis of data obtained from a retailer on 1,986 SKUs in 20 categories, we find that the pack‐and‐a‐half rule decreases a retailer's profits, on average, by 10% when applied uniformly across all SKUs. Further, this decrease is significantly affected by product depth, product width, demand elasticity, case pack quantity, and inventory carrying cost. Finally, we develop a set of modifications based on these variables where the pack‐and‐a‐half rule is applied selectively and in a stepwise fashion. These modifications limit the decrease in a retailer's profits to a range between 6% and 7%.     

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.     

DO FIRMS SEEK A TARGET BOARD STRUCTURE? EVIDENCE FROM THE POST‐SOX PERIOD

We investigate whether firms restructure board composition to align with changes in their contracting environment. Board size and independence increase with firm complexity, consistent with theoretical predictions. However, the hypothesized negative relation between board independence and information costs is evident only for firms completing acquisitions. Furthermore, board independence increases to offset increases in CEO power in a sample of firms making acquisitions, but decreases when CEO power increases in a large cross‐section of firms. We conclude that after the Sarbanes–Oxley Act of 2002, firms face constraints adjusting to target board structure, but these constraints can be mitigated by a shock to the contracting environment via acquisition.     

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.     

CEO internal experience and voluntary disclosure quality: Evidence from management forecasts

Internally‐promoted CEOs should have a deep understanding of their firm's products, supply chain, operations, business climate, corporate culture, and how to navigate among employees to get the information they need. Thus, we argue that internally‐promoted CEOs are likely to produce higher quality disclosure than outsider CEOs. Using a sample of US firms from the S&P1500 index from 2001 to 2011, we hand‐collect whether a CEO is hired from inside the firm and, if so, the number of years they worked at the firm before becoming CEO. We then examine whether managers with more internal experience issue higher quality disclosures and offer three main findings. First, CEOs with more internal experience are more likely to issue voluntary earnings forecasts than those managers with less internal experience as well as those managers hired from outside the firm. Second, CEOs with more internal experience issue more accurate earnings forecasts than those managers with less internal experience as well as those managers hired from outside the firm. Finally, investors react more strongly to forecasts issued by insider CEOs than to those issued by outsider CEOs. In additional analysis, we find no evidence that these results extend to mandatory reporting quality (i.e., accruals quality, restatements, or internal control weaknesses), perhaps because mandatory disclosure is subjected to heavy oversight by the board of directors, auditors, and regulators. Overall, our findings suggest that when managers have work experience with the firm prior to becoming the CEO, the firm's voluntary disclosure is of higher quality.     

Uncertainty,Control, and Karl Polanyi’s Protective Response

Post-Keynesian institutionalist economists like Wallace Peterson and John Kenneth Galbraith recognized that the impact of uncertainty on economic wellbeing depends in part on the degree of control people have over the sources and consequences of it. Given the inability of government and other large institutions to reduce uncertainty or to provide citizens with the ability to manage it, mediating structures are considered as an alternative means of promoting economic security. The article concludes by describing and evaluating several of these alternatives.     

Interventions in Markets with Adverse Selection: Implications for Discount Window Stigma

I study the implications for central bank discount window stigma of a workhorse model of adverse selection in financial markets. In the model, firms (banks) need to borrow to finance a productive project. There is limited liability and firms have private information about their ability to repay their debts, which gives rise to the possibility of adverse selection. The central bank can ameliorate the impact of adverse selection by lending to firms. Discount window borrowing is observable and it may be taken as a signal of firms' credit worthiness. Under some conditions, firms borrowing from the discount window may pay higher interest rates to borrow in the market, a phenomenon often associated with the presence of stigma. I discuss these and other outcomes in detail and what they suggest about the relevance of stigma as an empirical phenomenon.     

Pricing Defaultable Bonds Using a Lévy Jump‐Diffusion Model

This paper uses a reduced‐form approach to derive a closed‐form pricing formula for defaultable bonds. The authors specify the default hazard rate as an affine function of multiple variables which follow the Lévy jump‐diffusion processes. Because such specification allows greater flexibility in the generation of a valid probability of default, their pricing model should be more accurate than the valuation models in traditional studies, which ignore the jump effects. This paper also proposes a new method for estimating the parameters in a Lévy Jump‐diffusion process. The real data from the Taiwanese bond market are used to illustrate how their model can be applied in practical situations. The authors compare the pricing results for the influential variables with no jump effects, with jump magnitudes following the normal distribution, and with jump magnitudes following the gamma distribution. The results reveal that the predictive ability is the best for the model with the jump components. The valuation model shown in this paper should help portfolio managers more accurately price defaultable bonds and more effectively hedge their portfolio holdings.