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This study examines how firms’ use of competitor-focused accounting information, specifically competitor monitoring information, impacts their pricing, demand, and overall revenue performance. The monitoring activities examined are the scope of monitoring, monitoring above and below one’s own hotel class (i.e., market segment), and the extent of reciprocity of monitoring. Competitor analysis is a central element in strategic management accounting (SMA), yet little empirical research has been done since companies do not disclose competitor monitoring activities. Proving the value of competitive monitoring provides strong support for SMA. Archival, proprietary monitoring information regarding pricing, demand, and revenue were obtained from one of the largest hotel markets in the United States. Using regression, we modeled the relationships between performance measures (pricing, demand, and revenue) and monitoring behaviors, while controlling for quality (hotel characteristics and management skill), competitive intensity, hotel class, geographic location, and ownership type. Our results indicate that two aspects of competitor monitoring impact hotel pricing that, in turn, impacts hotel demand and revenue performance. Specifically, a hotel monitoring more competitors (what we refer to as Scope) achieves higher prices with unchanged demand, resulting in higher revenue performance. Most hotels monitor within their class. However, deviating from one’s class has profound outcomes: looking at lower (higher) quality hotels results in a hotel setting lower (higher) prices, resulting in higher (unchanged) demand and lower (higher) revenue performance. Surprisingly, we did not find support for the reciprocity of monitoring. That is, whether the competitors monitored by a hotel, in turn follow the target, has no impact on hotel revenue performance outcomes. While the SMA literature notes the importance of competitor monitoring, this study fills a gap in an important, under-researched area by documenting the link between competitor monitoring behaviors and organizational revenue performance. This may help promote greater diffusion of SMA practices.

We investigate outcomes associated with the turnover of unskilled workers, isolating its effects on revenue, cost, and profit. Little attention from researchers has been given to unskilled workers, a significant portion of the workforce.

This study investigates the relation between turnover among unskilled workers and financial performance using data from 527 hotels owned by the same lodging chain. The workers in our sample are full-time housekeepers and front desk attendants.

We find that the relation between turnover and performance differs by turnover type (voluntary vs. involuntary) and category of unskilled worker, reiterating the need to differentiate between turnover type and the importance of context in studying turnover. We challenge the assumption that voluntary turnover is categorically harmful and our results for front desk attendants support the view that organizations choose turnover levels that maximize performance. We also provide new evidence on the effects of involuntary turnover. Contrary to the established notion that dismissing less able employees should improve performance, we find that involuntary turnover has negative consequences.

Our results demonstrate the importance of distinguishing voluntary turnover from involuntary turnover and the need to include both in models predicting turnover’s performance effects.

In recent years professional accountancy bodies (e.g., CPA), accreditation institutions (e.g., AACSB) and employers have steadily raised, and continue to raise expectations regarding the need for accounting graduates to demonstrate skills in data analytics. One of the obstacles accounting instructors face in seeking to implement data analytics, however, is that they need access to ample teaching materials. Unfortunately, there are few such resources available for advanced programming languages such as R. While skills in commonly used applications such as Excel are no doubt needed, employers often take these for granted and incremental value is only added if graduates can demonstrate knowledge in using more advanced data analytics tools for decision-making such as coding in programming languages. This, together with the current dearth of resources available to accounting instructors to teach advanced programming languages is what drives motivation for this chapter. Specifically, we develop an intuitive, two-dimensional framework for incorporating R (a widely used open-source analytics tool with a powerful embedded programming language) into the accounting curriculum. Our model uses complexity as an integrating theme. We incorporate complexity into this framework at the dataset level (simple and complex datasets) and at the analytics task level (simple and complex tasks). We demonstrate two-dimensional framework by drawing on authentic simple and complex datasets as well as simple and complex tasks that could readily be incorporated into the accounting curriculum and ultimately add value to businesses. R script programming code are provided for all our illustrations.