Faculty members from UD’s Alfred Lerner College of Business and Economics published more than 25 research papers in their respective fields’ leading refereed journals in 2023. This included the top 50 journals used in the Financial Times research ranking, journals cited in the University of Texas (UT) Dallas Top 100 Business School Research Rankings and journals identified as highly selective “A” journals by the Lerner College’s five academic departments. This list is just one way the Lerner College recognizes and celebrates high-quality scholarship created and published by its faculty.
“Our mission is to produce high-impact research that addresses grand challenges and Increase the number of publications in the top business and economics journals,” said Lerner College Dean Oliver Yao.
Lerner College 2023 “A” Journal Publications
Accounting and Management Information Systems
Amanda Convery, Associate Professor of Accounting
Condie, E., Convery, A. M., & Zehms, K. J. (2023). Fraud firms’ non-implicated CFOs: An investigation of reputational contagion and subsequent employment outcomes. Contemporary Accounting Research, 40(1), 704–728. https://doi.org/10.1111/1911-3846.12817
We investigate labor market consequences for CFOs employed by fraud firms, focusing on reputational contagion for those who are not implicated. These individuals provide an opportunity to understand reputational contagion and the nuanced meaning of “guilt” because the labor market may suspect complicity or infer negligence regardless of whether that is truly the case. We compare these CFOs to a matched sample of non-fraud CFOs and track both turnover and subsequent employment positions. Non-implicated CFOs are more likely to experience turnover compared to non-fraud CFOs, driven in particular by the public revelation of fraud to the labor market. We further find that non- implicated CFOs are more likely to obtain comparable subsequent employment than non- fraud CFOs before the fraud is publicly revealed, but not after. In supplementary analyses, we find that turnover rates are highest for non-implicated CFOs who started their employment with the firm before the fraud began as compared to non-implicated CFOs who started their employment after the fraud began. These results highlight the labor market significance of the public revelation of fraud and imply that the labor market does not fully distinguish between fraud firm association and general firm performance when making executive hiring decisions.
Xiao Fang, Professor of MIS, JP Morgan Chase Fellow
Xu, D., Hu, P., & Fang, X. (2023). A deep learning-based imputation method to enhance crowdsourced data for online business directory platforms. Journal of Management Information Systems, 40(2), 624-654. https://doi.org/10.1080/07421222.2023.2196770
Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform.
Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method’s greater imputation effectiveness relative to prevalent methods. To illustrate the method’s practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services.
Xiao Fang, Professor of MIS, JP Morgan Chase Fellow; Jing He, Associate Professor of Accounting
Zhao, X., Fang, X., He, J., & Huang, L. (2023). Exploiting expert knowledge for assigning firms to industries: A novel deep learning method. MIS Quarterly, 47(3), 1147-1176. https://doi.org/10.48550/arXiv.2209.05943
Industry assignment, which assigns firms to industries according to a predefined Industry Classification System (ICS), is fundamental to a large number of critical business practices, ranging from operations and strategic decision making by firms to economic analyses by government agencies. Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, and overlook definition-based and structure-based knowledge. Moreover, these methods only consider which industry a firm has been assigned to, but ignore the time- specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account.
Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time- specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability.
Jiaheng Xie, Assistant Professor of MIS
Xie, J., Chai, Y., & Liu, X. (2023). Unbox the black-box: Predict and interpret YouTube viewership using deep learning. Journal of Management Information Systems, 40(2), 541-579. https://doi.org/10.48550/arXiv.2101.01076
Predicting video viewership is a top priority for content creators and video-sharing sites. Content creators live on such predictions to maximize influences and minimize budgets. Video-sharing sites rely on this prediction to promote credible videos and curb violative videos. Although deep learning champions viewership prediction, it lacks interpretability, which is fundamental to increasing the adoption of predictive models and prescribing measurements to improve viewership. Following the design-science paradigm, we propose a novel interpretable IT system, Precise Wide and Deep Learning (PrecWD), to precisely interpret viewership prediction. Improving upon state-of-the-art frameworks, PrecWD offers precise feature effects and designs an unstructured component. PrecWD outperforms benchmarks in two contexts: health video viewership prediction and misinformation viewership prediction. A user study confirms the superior interpretability of PrecWD. This study contributes to IS design theory with generalizable design principles and an interpretable predictive framework. Our findings provide implications to improve video viewership and credibility.
Oliver Yao, Dean of Alfred Lerner of College of Business and Economics, Professor of MIS
Zhang, D. (D.), Peng, G., Yao, Y., & Browning, T. R. (2023). Is a college education still enough? The IT-labor relationship with education level, task routineness, and artificial intelligence. Information Systems Research. Advanced online publication. https://doi.org/10.1287/isre.2021.0391
Although information technology (IT) is increasingly replacing human labor, the IT-labor relationship is more nuanced than it appears. We examine the IT-labor relationship in terms of various levels of education, intensities of routine tasks, and exposure to artificial intelligence (AI). Making use of an industry-level data set covering 60 U.S. industries from 1998 to 2013, we adopt an innovative measure of elasticity of substitution that enables us to capture the asymmetric price impact between IT and labor. Our findings indicate that IT generally complements high-education labor (master’s degree or above), while substituting for low-education labor (high school degree or below). For middle- education labor (bachelor’s or associate’s degree), however, the IT-labor relationship is more nuanced: They are complements in non-routine-intensive industries, but substitutes in routine-intensive industries. We also find that IT is a complement (substitute) with high-education labor in industries with lower (higher) AI exposure and remains a net substitute for low- and middle-education labor, regardless of their AI exposure. Our findings suggest that even college-educated labor has now become susceptible to IT displacement, whereas labor with graduate education largely remains a strong complement to IT (with an exception in high-AI-exposure industries). Theoretical and policy implications are discussed.
Zhang, X., Cui, R., & Yao, O. (2023). The version effect of apps and operating systems in mobile commerce. Production and Operations Management, 32(2), 637-654.
Different versions of mobile operating systems or shopping apps enable different functionalities and information flows, thus creating various mobile shopping environments. In general, up‐to‐date versions provide better information flows and richer functionalities. However, mobile operating systems and shopping apps affect consumer behavior through different mechanisms. An up‐to‐date mobile operating system reduces the system response time, while an up‐to‐date shopping app improves algorithms for better search accuracy. The former encourages consumer explore and search, whereas the latter improves consumer search efficiency and reduces chances for consumers to discover more products. Using a unique large‐scale clickstream data set from a mobile commerce retailer, we examine the effect of mobile operating system and app versions on consumer search and impulse purchase behaviors in mobile commerce. Our results show that consumers with an up‐to‐date mobile operating system or a previous version of a shopping app conduct more searches in terms of increased product page views and time spent on product pages, which results in a higher probability of consumer impulse purchase. However, consumers’ search for individual products is not affected by versions. Surprisingly, though more page views or time spent may boost purchases, we find that consumer search affects impulse purchases nonlinearly in that page views and time spent have a decreasing rate of impact. Our computations show that using an up‐to‐date operating system increases consumer search activities by 60.45 product pages or 521.30 seconds spent browsing compared to using a previous version of an operating system.
Using an up‐to‐date app decreases consumer search activities by 129.34 product pages or 1446.48 seconds spent browsing compared to using a previous version of a mobile app.
Nir Yehuda, Professor of Accounting and MIS
Jang, Y., Liu, C. H., Weinbaum, D., & Yehuda, N. (2024). Performing up to par: Hospitality firms after ASU 2016-02. Cornell Hospitality Quarterly. https://doi.org/10.1177/19389655241230229
Relative to sales, the average operating lease commitments of hospitality firms are 4 times larger than those of other publicly traded firms. In response to the recently enacted accounting standards update No. 2016-02 (ASU 2016-02) that requires lessees to recognize operating leases on their balance sheet, hospitality firms decreased their use of operating leases, switching to shorter-term off-balance sheet leases. We find that this change did not have negative consequences on firm performance, shareholders, or employees. The only significant effect we do find is an improvement in credit ratings for firms that reduced operating leases in response to the new standard. Our findings are inconsistent with the concerns some hospitality managers and academics expressed prior to the introduction of the standard
Yehuda, N., Armstrong, C., Cohen, D., & Zhou, X. (2023). Labor unemployment risk and debt- contract design. The Accounting Review, 98(6), 467–504. https://doi.org/10.2308/TAR-2019- 0150
We examine how firms’ contractual relationships with their employees affect the design of their debt contracts, and the use of financial covenants in particular. Viewing the firm as the nexus of both explicit and implicit contractual relationships, we argue that managers cater to their employees’ preferences when negotiating contractual terms with creditors. We argue that an increase in unemployment-insurance benefits reduces employees’ cost of job loss, which, in turn, allows managers to take more risk. First, we show that more generous benefits are associated with a higher operating leverage, operating cash flow volatility, and product-development frequency. We then find that loans initiated following an increase in unemployment-insurance benefits include a higher proportion of performance, rather than capital covenants. Overall, our study demonstrates how the design of debt contracts changes in response to arguably exogenous changes in employees’ collective tolerance—and, in turn, managers’ preferences—for risk.
Business Administration
Bintong Chen, Professor of Operations Management; Xiao Fang, Professor of MIS, JP Morgan Chase Fellow
Yin, K., Fang, X., Chen, B., & Liu Sheng, O. R. (2023). Diversity preference-aware link recommendation for online social networks. Information Systems Research, 34(4), 1398-1414. https://doi.org/10.1287/isre.2022.1174
Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a user but overlook the user’s diversity preference, although social psychology theories suggest the criticality of diversity preference to link recommendation performance. In recommender systems, a field related to link recommendation, a number of diversification methods have been proposed to improve the diversity of recommended items. Nevertheless, diversity preference is distinct from diversity studied by diversification methods. To address these research gaps, we define and operationalize the concept of diversity preference for link recommendation and propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then analyze key properties of the new link recommendation problem and develop a novel link recommendation method to solve the problem. Using two large-scale online social network data sets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over representative diversification methods adapted for link recommendation as well as state-of-the-art link recommendation methods.
Andong Cheng, Assistant Professor of Marketing
Cheng, A., & Ross, G. (2023). Tiered discounts as multiple reference points for spending.Journal of Consumer Psychology, 33(2), 424-431. https://doi.org/10.1002/jcpy.1339
Tiered discounts offer larger discounts as consumers meet higher spending thresholds (e.g., spend $100+, receive 10% off; spend $200+, receive 20% off). This research investigates how consumers treat these multiple dollar thresholds as reference points for spending. We find that tiered discounts with smaller increments between thresholds encourage higher spending compared to those with larger increments. This effect occurs because consumers treat thresholds as motivational spending goals when the distance to higher thresholds is smaller (vs. larger). Consistent with this reasoning, signaling goal progress (i.e., displaying cart amount while shopping) attenuates the spending difference smaller versus larger increment sizes yield. Additionally, the effect of tier increment size on spending is more prominent for maximizers. From a theoretical perspective, this work contributes to our understanding of how individuals process multiple reference points within a single promotion and identifies that spending thresholds in price promotions may be treated as spending goals. From a managerial perspective, this work investigates the relationship between tiered discount design and consumer spending.
Saleem “Sal” Mistry, Associate Professor of Management
Mistry, S., Kirkman, B. L., Moore, O. A., Hanna, A. A., & Rapp, T. L. (2023). Too many teams? Examining the impact of multiple team memberships and permanent team identification on employees’ identity strain, cognitive depletion, and turnover. Personnel Psychology, 76(3), 885–912. https://doi.org/10.1111/peps.12515
As the prevalence of multiple team membership (MTM) arrangements continues to grow, researchers have argued that shifting between teams and work roles induces MTM identity strain and other harmful outcomes. Drawing from work role transitions research on role identity and integrating it with social identity theory, we investigate this line of reasoning by conducting two studies, one field and one online panel study, focusing on blended MTMs, in which employees are concurrently assigned to a permanent team and several temporary project teams. Specifically, we examine the theoretical mechanisms explaining a positive relationship between number of temporary teams and turnover decisions. In Study 1, we surprisingly found that number of temporary teams negatively related to turnover decisions through MTM identity strain with permanent team identification strengthening this effect. In contrast, in Study 2, we found support for the hypothesized relationships: number of teams indirectly positively related to turnover intentions, mediated by MTM identity strain and cognitive depletion, and permanent team identification weakened the indirect effect. We provide explanations for these mixed findings and suggest theoretical and practical implications for MTM research.
Liying Mu, Associate Professor of Operations Management
Dawande, M., Mehta, S., & Mu, L. (2023). Robin Hood to the rescue: Sustainable revenue- allocation schemes for data cooperatives. Production and Operations Management, 32(8), 2560– 2577. https://doi.org/10.1111/poms.13995
The promise of consumer data along with advances in information technology has spurred innovation not only in the way firms conduct their business operations but also in the manner in which data are collected. A prominent institutional structure that has recently emerged is a data cooperative—an organization that collects data from its members, and processes and monetizes the pooled data. A characteristic of consumer data is the externality it generates: Data shared by an individual reveal information about other similar individuals; thus, the marginal value of pooled data increases in both the quantity and quality of the data. A key challenge faced by a data cooperative is the design of a revenue-allocation scheme for sharing revenue with its members. An effective scheme generates a beneficial cycle: It incentivizes members to share high-quality data, which in turn results in high-quality pooled data—this increases the attractiveness of the data for buyers and hence the cooperative’s revenue, ultimately resulting in improved compensation for the members. While the cooperative naturally wishes to maximize its total surplus, two other important desirable properties of an allocation scheme are individual rationality and coalitional stability. We first examine a natural proportional allocation scheme—which pays members based on their individual contribution—and show that it simultaneously achieves individual rationality, the first-best outcome, and coalitional stability, when members’ privacy costs are homogeneous. Under heterogeneity in privacy costs, we analyze a novel hybrid allocation scheme and show that it achieves both individual rationality and the first-best outcome, but may not satisfy coalitional stability. Finally, our RobinHood allocation scheme—which uses a fraction of the revenue to ensure coalitional stability and allocates the remaining based on the hybrid scheme—achieves all the desirable properties.
Jackie Silverman, Assistant Professor of Marketing
Silverman, J., & Barasch, A. (2023). On or off track: How (broken) streaks affect consumer decisions. Journal of Consumer Research, 49(6). https://doi.org/10.1093/jcr/ucac029
New technologies increasingly enable consumers to track their behaviors over time, making them more aware of their “streaks”—behaviors performed consecutively three or more times—than ever before. Our research explores how these logged streaks affect consumers’ decisions to engage in the same behavior subsequently. In seven studies, we find that intact streaks highlighted via behavioral logs increase consumers’ subsequent engagement in that behavior, relative to when broken streaks are highlighted. Importantly, this effect is independent of actual past behavior and depends solely on how that behavior is represented within the log. This is because consumers consider maintaining a logged streak to be a meaningful goal in and of itself. In line with this theory, the effect of intact (vs. broken) logged streaks is amplified when consumers attribute a break in the streak to themselves rather than to external factors, and attenuated when consumers can “repair” a broken streak. Our research provides actionable insights for companies seeking to benefit from highlighting consumers’ streaks in various consequential domains (e.g., fitness, learning) without incurring a cost (e.g., reduced engagement or abandonment) when those streaks are broken.
Silverman, J., Barasch, A., & Small, D. (2023). Hot streak! Inferences and predictions about goal adherence. Organizational Behavior and Human Decision Processes, 179, 104281. https://doi.org/10.1016/j.obhdp.2023.104281
When do people make optimistic forecasts about goal adherence? Nine preregistered studies find that a recent streak of goal-consistent behavior increases the predicted likelihood that the individual will persist, compared to various other patterns holding the rate of goal adherence constant. This effect is due to perceiving a higher level of commitment following a streak. Accordingly, the effect is larger when the behavior requires commitment to stick with it, compared to when the same behavior is enjoyable in its own right. Furthermore, the effect is weaker in the presence of another diagnostic cue of commitment: when the individual has a high historic rate of goal adherence. People also behave strategically in ways consistent with these inferences (e.g., are less likely to adopt costly goal support tools following a streak, choose partners with recent streaks for joint goal pursuit). Together, these results demonstrate the significance of streaky behavior for forecasting goal adherence.
Ming Zhao, Associate Professor of Operations Management
Zhao, M., Freeman, N. K., & Pan, K. (2023). Robust sourcing under multi-level supply risks: Analysis of random yield and capacity. INFORMS Journal on Computing, 35(1), 178-195. https://doi.org/10.1287/ijoc.2022.1254
We consider the optimal sourcing problem when the available suppliers are subject to ambiguously correlated supply risks. This problem is motivated by the increasing severity of supply risks and difficulty evaluating common sources of vulnerability in upstream supply chains, which are problems reported by many surveys of goods-producing firms. We propose a distributionally robust model that accommodates (i) multiple levels of supply disruption, not just full delivery or no delivery, and (ii) can use data-driven estimates of the underlying correlation to develop sourcing strategies in situations where the true correlation structure is ambiguous. Using this framework, we provide analytical results regarding the form of a worst-case supply distribution and show that taking such a worst-case perspective is appealing due to severe consequences associated with supply chain risks. Moreover, we show how our distributionally robust model may be used to offer guidance to firms considering whether to exert additional effort in an attempt to better understand the prevailing correlation structure. Extensive computational experiments further demonstrate the performance of our distributionally robust approach and show how supplier characteristics and the type of supply uncertainty affect the optimal sourcing decision.
Economics
Francisco Costa
Costa, F., Marcantonio, A., & Rocha, R. (2023). Stop suffering! Economic downturns and Pentecostal upsurge. Journal of the European Economic Association, 21(1), 215-250. https://doi.org/10.1093/jeea/jvac034
This paper estimates the effects of economic downturns on the expansion of Pentecostal Evangelicalism in Brazil. Regions more exposed to economic distress experienced a persistent rise both in Pentecostal affiliation and in the vote share of candidates connected to Pentecostal churches in national legislative elections. Once elected, these politicians carry out an agenda with greater emphasis on issues that are sensitive to fundamental religious principles. We, therefore, find that recessions led to the rise of religious fundamentalism in tandem with the transfer of political capital to elected Pentecostal leaders.
Manaswini Rao, Assistant Professor of Economics
Rao, M., & Shenoy, A. (2023). Got (clean) milk? Organization, incentives, and management in Indian dairy cooperatives. Journal of Economic Behavior & Organization, 212, 708-722. https://doi.org/10.1016/j.jebo.2023.06.002
Smallholder producers in developing countries often collaborate in teams that take advantage of scale economies and allocate surplus among members. We experimentally evaluate team-level incentive contracts for quality upgrading among Indian dairy cooperatives where there is a risk of free-riding because individual quality cannot be traced. Incentives improve aggregate quality, with evidence of increased effort from both producers and cooperative managers. However, several managers decline incentive payments when they cannot control how payment information is disclosed to cooperative members. Survey evidence indicates publicity lowers managerial returns, suggesting transparency-based efforts to constrain elites can undermine the core policy goal.
Finance
Jack Bao, Associate Professor of Finance
Bao, J., Hou, K., & Zhang, S. (2023). Systematic default and return predictability in the stock and bond markets. Journal of Financial Economics, 149, 349–377. https://doi.org/10.1016/j.jfineco.2023.05.006
We construct a measure of systematic default defined as the probability that many firms default at the same time. We account for correlations in defaults between firms through exposures to common shocks. Systematic default spikes during recessions, is correlated with macroeconomic indicators, and predicts future realized defaults. More importantly, it predicts future equity and corporate bond index returns both in- and out-of-sample. Finally, we find that the cross-section of average stock returns is related to firm-level exposures to systematic default risk.
Matthias Fleckenstein, Associate Professor of Finance
Fleckenstein, M., & Longstaff, F. A. (2023). Private equity returns: Empirical evidence from the business credit card securitization market. Journal of Finance, 78, 389-425. https://doi.org/10.1111/jofi.13200
We present a new approach for estimating small business equity returns. This approach applies the Merton (1974) credit model to the returns on entrepreneurial business credit card debt securitizations and solves for the implied equity returns for the small businesses owned by the cardholders. The estimated small business equity premium is 10.74%. The standard deviation of small business equity returns is 56.37%. We validate the methodology by applying it to investment-grade corporate bonds and recovering a public equity premium of 6.17%.
XiaoXia Lou, Professor of Finance
Choi, H. M., Karpoff, J., & Lou, X. (in press). Enforcement waves and spillover effects. Management Science. https://doi.org/10.1287/mnsc.2023.4711
We document that regulatory enforcement actions for financial misrepresentation cluster in industry-specific waves and that wave-related enforcement has information spillovers on industry peer firms. Waves and spillovers have significant effects on share prices.
Early-wave target firms have the largest short-run losses in share values and the largest information spillovers on industry peer firms. Late-wave targets’ short-run losses are smaller, but not because they involve less costly instances of misconduct. Rather, late- wave targets are subject to more information spillovers from earlier in the wave. These results indicate that prices incorporate changes in the likelihood that a firm will face wave-related enforcement action for financial misconduct. Short-window share-price losses understate the total share-price impact, particularly for firms whose financial misrepresentation is revealed late in an enforcement wave.
Hospitality and Sport Business Management
Sri Beldona, Professor of Hospitality Business Management
Tabatabaei, F., & Beldona, S. (2024). Are eco-friendly hotels inconvenient? An implicit association test. Journal of Hospitality and Tourism Management, 58, 197-208. https://doi.org/10.1016/j.jhtm.2024.01.001
In the extant literature, findings about customers’ attitudes and intentions to patronize eco-friendly hotels are mixed. Both favorable associations, such as satisfaction, and unfavorable links (for example, barriers such as inconvenience) are evident in the literature. These studies examined the explicit attitudes of consumers, which have been deemed sensitive and potentially generate social desirability bias. Conversely, this study employed the Implicit Association Test, which measured reaction times to extract customers’ implicit stereotypes towards eco-friendly hotels, followed by a questionnaire- based survey to assess their explicit attitudes. With a sample of 297 American travelers, the findings of the implicit and explicit tests show moderate and strong relationships between eco-friendly hotels and the convenience concept, respectively. That is, our findings show that customers do not attribute inconvenience to eco-friendly hotels. The implications of the study are significant given the emphasis on responsible tourism as a domain of study in which customer attitudes arguably play a significant role in a changing environment.
Hong Soon Kim, Assistant Professor of Hospitality Business Management
Kim, H. S., & Jang, S. C. (2023). Is a high pay disparity harmful to productivity? Findings from the restaurant industry. International Journal of Hospitality Management, 115, 103580. https://doi.org/10.1016/j.ijhm.2023.103580
This study examined the effect of CEO-employee (executive) pay disparity on restaurant productivity and how restaurant type and franchising moderate the pay disparity- productivity relationship. Based on the notion that tournament theory and equity theory are complementary, this study hypothesized that CEO-employee (executive) pay disparity has a nonlinear effect on restaurant productivity. Furthermore, this study further hypothesized that restaurant type and franchising moderate the relationship between pay disparity and restaurant productivity. The results of this study confirmed that CEO- employee (executive) pay disparity has an inverted U-shaped (U-shaped) relationship with restaurant productivity. This study also found that franchising (restaurant type) significantly moderates the relationship between CEO-employee (executive) pay disparity and restaurant productivity. The results suggest that CEO-employee (executive) pay disparity has an inverted U-shaped (U-shaped) influence on restaurant productivity with a minima (maxima) of 500 (3.75) times. More detailed results and implications are discussed in this paper
Sheryl Kline, Deputy Dean and Aramark Chaired Professor; William “Bill” Sullivan, Adjunct Faculty, Managing Director of the Courtyard by Marriott – Newark
Poorani, A. A., Kline, S. F., DeMicco, F. J., & Sullivan, W. (2023). Hospitality to healthcare: Patient Experience Academy, a successful alliance between the ChristianaCare Health System and the University of Delaware. International Journal of Hospitality Management. 112 https://doi.org/10.1016/j.ijhm.2022.103422
This exemplary case study describes a successful collaboration between a healthcare system and a hospitality management program to bring hospitality to healthcare. ChristianaCare Health System (CCHS) and University of Delaware’s Hospitality and Sport Management partnered to create Patient Experience Academy. The program focused on a training intervention, supported by leadership to enhance the patient and guest experience at the ChristianaCare Health System (CCHS). The highlights of the program included Managing Expectations; Service Recovery; Power of Listening & Empathy in Healthcare Setting; Healthcare Theatre (HT) Interactive Training Platforms & Delivery Systems for Sim Sessions; Transformation; and Achievement Stories. Total participants included 32 Cohorts (nearly 1000 participants), and each cohort was comprised of 25–30 attendees selected by CCHS. Cohorts included Physicians, LPNs, RNs, NAs, MAs, OAs, frontline and other support staff. The results were measured at three levels: 1) Learning outcomes, 2) Application of training to practice, and 3) Patient satisfaction scores. Scores in all three areas showed significant improvements.
Zvi Schwartz, Professor of Hospitality Business Management
Ma, J., & Schwartz, Z. (2023). Revenue analytics: The problem with fixed-tier pricing. Cornell Hospitality Quarterly, 64(3), 289-297. https://doi.org/10.1177/19389655231152456
With the widely used fixed-tier computerized pricing system (e.g., based on the best available rate or BAR), fenced discount rates are set and updated as a fixed percentage of the base rate such as the BAR. This intuitive computer-automated solution to a complex pricing issue is, however, theoretically suboptimal. The study demonstrates why the practice of using fixed-tier pricing is suboptimal, showing that this fixed-tier approach is inferior even when the initial set of fenced rates is optimal and even in the unlikely scenario of the various market segments’ demand curves shifting proportionally. As such, practitioners should avoid using a convenient fixed-tier pricing model (BAR-based or not) where only one pricing optimization is run and the rest of the fenced prices are calculated based on this optimized price using fixed percentages. Instead, a fenced-rate pricing system where individual segments are treated independently, and optimizations are run for each segment should be adopted.
Zvi Schwartz, Professor of Hospitality Business Management; Tim Webb, Associate Professor of Hospitality Business Management
Schwartz, Z., Ma, J., & Webb, T. (2024). The MSapeMER: A symmetric, scale-free and intuitive forecasting error measure for hospitality revenue management. International Journal of Contemporary Hospitality Management, 36(6), 2035-2048. https://doi.org/10.1108/IJCHM-01-2023-0088
Mean absolute percentage error (MAPE) is the primary forecast evaluation metric in hospitality and tourism research; however its main shortcoming is that it is asymmetric. The asymmetry occurs due to over or under forecasts that introduce bias into forecast evaluation. This study aims to explore the nature of asymmetry and designs a new measure, one that reduces the asymmetric properties while maintaining MAPE’s scale- free and intuitive interpretation characteristics. the study proposes and tests a new
forecasting accuracy measure for hospitality revenue management. A computer simulation is used to assess and demonstrate the problem of asymmetry when forecasting with MAPE, and the new measures’ (MSapeMER, that is, Mean of Selectively applied Absolute Percentage Error or Magnitude of Error Relative to the estimate) ability to reduce it. The MSapeMER’s effectiveness is empirically validated by using a large set of hotel forecasts.
Tim Webb, Associate Professor of Hospitality Business Management; Zvi Schwartz, Professor of Hospitality Business Management
Webb, T., Lee, M., Schwartz, Z., & Vouk, I. (2023). Beyond accuracy: The advantages of the k-nearest neighbor algorithm for hotel revenue management forecasting. Tourism Economics. https://doi.org/10.1177/13548166231201199
Revenue management (RM) systems forecast demand and optimize prices to maximize a hotel’s revenue. The RM function operates in coordination between a system and an analyst. Systems provide recommendations while analysts review the forecasts and prices to approve or make subjective adjustments. The recommendations are often a “black box” with little insight regarding how recommendations are derived. This article proposes the k-Nearest Neighbor (k-NN) algorithm as a forecasting approach that can transition the “black box” to a “glass box.” The benefits of the k-NN are discussed in detail and compared with neural networks. The analysis is conducted on 35 hotels in partnership with a leading RM service provider. The results indicate similar performance for both techniques, leading to an important discussion on model evaluation outside of accuracy. In particular, the article discusses some of the unique advantages k-NN provides for the RM discipline.
Joanne Jung-Eun Yoo, Associate Professor of Hospitality Business Management
Jang, E., Yoo, J. J.-E., & Cho, M. (2023). Particulate matter source attribution and restaurant mitigation behavioral intentions: An application of attribution theory. International Journal of Contemporary Hospitality Management, 35(5), 1901–1921. https://doi.org/10.1108/IJCHM-05-2022-0632
Purpose: As commercial cooking is known as a source that generates great concentrations of particulate matter (PM) emissions first accumulating in kitchens before spreading to dining areas, this study aims to explore how to improve restaurants’ efforts to reduce PM emissions by the application of attribution theory.
Design/methodology/approach: Data were obtained from restaurant managers operating their business in South Korea, considered to be qualified to provide accurate information regarding the survey questions. A scenario-based experimental approach was used to test the hypothesized relationships. Cognitive and emotional risk judgments were assessed for its potential interaction effects on the relationships between restaurant perceptions of PM source attributions, prevention attitudes and mitigation behavioral intentions.
Findings: Results revealed that perceptions of PM main sources were attributed to internal rather than external factors, which improved mitigation behavioral intentions. Such an effect was partially mediated through PM pollution prevention attitudes.
Additionally, when applying external source attributions, PM mitigation behavioral intentions were improved by cognitive risk judgements, and PM prevention attitudes were enhanced by affective risk judgements.
Research limitations/implications: Results assist restaurants to better understand their operations that may be emitting significant levels of PM, thereby encouraging them to set more ambitious and effective PM mitigation operational guidelines for their employees and diners.
Originality/value: This study provides a fundamental baseline of management perceptions regarding PM emissions related to restaurant mitigation behavioral intentions. Results are useful in designing appropriate communication strategies addressing restaurant PM pollution issues to improve internal restaurant practices regarding clean air quality.
Trang, N. T., Yoo, J., Joo, D., & Lee, G. (2023). Incorporating senses into a destination image. Journal of Destination Marketing and Management, 27, 100760. https://doi.org/10.1016/j.jdmm.2022.100760
Destination image is a key determinant of tourists’ destination choice and loyalty formation. Despite works suggesting the multi-sensory image as an additional dimension of destination image—alongside the cognitive, affective, and conative images—there has been little quantitative evidence validating the view. In response to the research gap, this study (1) examined whether the multi-sensory image can be integrated into the existing bi-dimensional model (i.e., cognitive and affective images) and form a higher-order structure of destination image and (2) assessed the nomological validity of the higher- order destination image structure in predicting tourist satisfaction and destination loyalty.
In doing so, this study also invented a multi-sensory image scale via mixed-methods research. Results supported the sound psychometric properties of the multi-sensory image scale, the tri-dimensional structure of the destination image consists of the cognitive, affective, and multi-sensory images, and the nomological validity of the higher-order destination image structure. The findings suggest a new understanding of destination image and call for greater use of the multi-sensory images in destination marketing.