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This study explores the determinants of police officer support for pre-arrest/booking deflection programs that divert people presenting with substance use and/or mental health disorder symptoms out of the criminal justice system and connect them to supportive services.

This study analyzes responses from 254 surveys fielded to police officers in Delaware. Questionnaires asked about views on leadership, approaches toward crime, training, occupational experience and officer’s personal characteristics. The study applies a new machine learning method called kernel-based regularized least squares (KRLS) for non-linearities and interactions among independent variables. Estimates from a KRLS model are compared with those from an ordinary least square regression (OLS) model.

Support for diversion is positively associated with leadership endorsing diversion and thinking of new ways to solve problems. Tough-on-crime attitudes diminish programmatic support. Tenure becomes less predictive of police attitudes in the KRLS model, suggesting interactions with other factors. The KRLS model explains a larger proportion of the variance in officer attitudes than the traditional OLS model.

The study demonstrates the usefulness of the KRLS method for practitioners and scholars seeking to illuminate patterns in police attitudes. It further underscores the importance of agency leadership in legitimizing deflection as a pathway to addressing behavioral health challenges in communities.

The purpose of this paper is to examine Asian Americans' perceptions of the police, specifically how they construct support. Although such literature has been growing in recent years, research on Asian American interactions with the police remains limited. Additionally, this paper is situated within the theoretical framework of system justification theory to account for Asian Americans' views of the police.

This study relies on interview data collected from 20 Asian Americans residing in mid-Atlantic states. Participants were either recruited directly by the researchers or through the snowball-sampling method.

Police support is influenced by perception of neighborhood safety, personal police contact and empathetic feelings toward the police. Specifically, regarding the latter component, humanizing or empathizing with police officers is a form of rationalizing individual police misconduct that reinforced police legitimacy. Most participants had similar characteristics and displayed police justification. Additional research is needed regarding what characteristics or patterns are likely to lead to lower levels of police justification.

This article's findings improve our understanding of system justification among Asian Americans, particularly as it relates to policing.

This study aims to investigate if engineered compression variations using moisture-responsive knitted fabric design can improve breast support in seamless knitted sports bras.

An experimental approach was used to integrate a novel moisture-responsive fabric panel into a seamless knitted bra, and the resulting compression variability in dry versus wet conditions were compared with those of a control bra. Air permeability and elongation testing of between breasts fabric panels was conducted in dry and wet conditions, followed by three-dimensional body scanning of eight human participants wearing the two bras in similar conditions. Questionnaires were used to evaluate perceived comfort and breast support of both bras in both conditions.

Air permeability test results showed that the novel panel had the highest variance between dry and wet conditions, confirming its moisture-responsive design, and increased its elongation coefficient in both wale and course directions in wet condition. There were significant main effects of bra type and body location on breast compression measurements. Breast circumferences in the novel bra were significantly larger than in the control bra condition. The significant two-way interaction between bra type and moisture condition showed that the control bra lost compressive power in wet condition, whereas the novel bra became more compressive when wet. Changes in compression were confirmed by participants’ perception of tighter straps and drier breast comfort.

These findings add to the limited scientific knowledge of moisture adaptive bra design using engineered knitted fabrics via advanced manufacturing technologies, with possible applications beyond sports bras, such as bras for breast surgery recovering patients.

Police procedural justice is essential in shaping police legitimacy and public willingness to cooperate, yet factors that affect police fair treatment of citizens are not fully understood. Using the data of the National Police Research Platform (NPRP), Phase II, this study examines the effects of three key organizational factors (i.e. effective leadership, supervisory justice and department process fairness) on officers’ procedural justice in police stops.

Innovatively, this study links police data with citizens’ data and conducts multilevel analyses on the effects of a host of citizen, officer, incident, and, importantly, agency characteristics on officer behaviors during over 5,000 police stops nested within 48 police agencies.

The results showed that the fairness of the departmental process had a positive effect on officer procedural justice, while the fairness of the supervisor was inversely associated with procedural justice on the street.

The linked data demonstrated that organizational fairness affected street procedure justice.

This case is based on primary and secondary data collection. ABCo’s Founder, Jocelyn Sheppard, sat down with the author for a 75-min recorded interview in July 2022, and she provided follow-up information via email. Interview data was supplemented with secondary data from publicly available sources to fill in portions on the founder, the company’s history and its location; and triangulate the collected interview data (Creswell and Poth, 2018). There are no conflicts of interest that the author needs to disclose related to the founder or company.

The case was piloted at one institution in the Fall 2022, Spring 2023 and Fall 2023 semesters, with 59 undergraduates in an in-person social entrepreneurship course and 165 undergraduates and 33 graduate students in an online asynchronous social entrepreneurship course. All students worked through the case in groups, and as a requirement of their corresponding assignment submission, they provided feedback that was de-identified. In total, 60 groups reported their feedback, which was considered during the subsequent drafts of the case and instructors’ manual IM.

According to the anonymized feedback, the protagonist, product line, desired social impact and experienced challenges of ABCo were all said to be interesting, approachable and relatable for students, and the case piqued the interest of students coming from different majors (e.g. business, environmental issues, human services and criminal justice). Students from rural areas, or those who have family in rural areas, felt the case was particularly interesting; a handful of the students in the asynchronous online class who were unfamiliar with such settings suggested providing students with some additional contextualization of rural environments, either through class discussion with other students who had experience in those environments or additional media or text-based supports. Further adjustments also included removing a reading and a corresponding question and revising elements within the Teaching Approaches section of the IM to support the additions they suggested within the feedback (i.e. spending time to define and walk through the provided model and highlight the differences of rural entrepreneurship and entrepreneurship in the rural as a class before engaging in the related write-ups for that question).

Jocelyn Sheppard, Founder of Appalachian Botanical Company (“ABCo”), had built her company not just on a vision of revitalizing reclaimed coal mine land through planting and producing products with lavender, but also to have a social impact on the rural town of Ashford and its greater region of Boone County in West Virginia, USA. While she understood that hiring workers in need of a second chance would present its challenges, she was shocked by the depth of social need her new employees presented, which contributed to many employees’ disruptive behaviors and turnover. To approach the problem at hand, Sheppard needed to reflect on the resources around her, namely, other entities and organizations who might be able to support her efforts to improve how ABCo delivers on its social mission and, thus, helps to improve the local community and its economy. The case draws upon literature and models within rural entrepreneurship and community development to have students advise Sheppard on what she should do next to improve the social outcomes for ABCo and its employees.

This case is geared for both upper-level undergraduate and graduate courses in entrepreneurship, including in social, environmental and rural entrepreneurship courses and course modules. The case introduces students to a social enterprise struggling to get its footing in a rural context. The case would be suitable for both introductory and advanced courses, especially when placemaking/place-based entrepreneurship or ecosystem building are discussed.

The diffusion of technologies from other sectors, and innovations in kitchen equipment, fueled structural changes within the foodservice industry. However, this change comes at a price of disrupting the critical step of assessing the demand forecast accuracy. This study aims to explore a surprisingly unique and elevated complexity when assessing the critically important demand forecast accuracy.

The paper develops a mathematical model to describe and explore the nature of the problem in structural biased demand forecast accuracy assessment. It then uses numerical simulation to construct a market example to gain better insights on the bias characteristics. Finally, the forecast accuracy measurement’s inherent bias is contrasted with that of other typical hospitality forecasting setups.

This paper outlines the theoretical underpinnings of how demand forecasts in the central kitchen setup are dynamic and thus produce a structural bias. More specifically, this paper discovers how, in this context of orders from a central location, the forecasts set the capacity constraints, and, consequently, generate a considerably more biased forecast accuracy measure. Relying on such forecast accuracy measures can lead to serious negative business outcomes.

To the best of the author’s knowledge, this study is the first to show that in the unique new technology enabled environment of central kitchen operation, where daily dish demand forecasts set the daily constrained capacity levels, the accuracy measure is severely biased, and consequently accuracy is likely to deteriorate, which in turn, could lead to suboptimal decisions. The major theoretical contribution of this study is a novel analytical model which explains and describes the bias in the accuracy measurement.

技术从其他行业的传播以及厨房设备的创新推动了餐饮业内的结构变化。然而, 这种变化直接影响了评估需求预测准确性。本研究探讨了在餐饮业结构改变后，评估至关重要的需求预测准确性时所面临的令人独特和复杂性。

本文自研了一个数学模型来描述和探讨评估需求预测准确性中的结构性偏差的本质。然后, 使用数值模拟构建一个市场示例, 以更好地了解上述偏差的特征。最后, 将这种预测准确性评估的系统性偏差与其他传统的餐饮业需求预测情境进行对比。

本文概述了中央厨房运营中需求预测是动态的, 因此产生了结构性偏差的理论基础。更具体地说, 在使用中央厨房并集中订单的情境下, 本文发现需求预测直接设定了容量限制, 因此产生了在需求预测准确度衡量中的结构性偏差。依赖这样的预测准确性度量可能产生严重的负面商业结果。

这项研究首次表明, 在中央厨房运营的独特的新环境中, 由于新的设定即每日菜品需求预测直接决定每日容量水平, 需求预测准确度衡量标准有着严重偏差, 长期来讲准确性可能下降, 从而导致次优的商业决策。本研究的主要理论贡献是提供一个餐饮企业在新运营环境中解释和描述需求预测准确度中结构性偏差的全新分析模型。

Blockchain technology has created possibilities for environmental supply chain sustainability and climate protection. However, because of its early development stage, users tend to resist the adoption of this new technology. The purpose of this study is to investigate the effects of resistance on blockchain adoption intentions in the context of the foodservice industry. This study further explores if public pressures and climate change awareness could possibly weaken the negative relationships between blockchain resistance and adoption intentions.

Data were collected from managers and full-time employees in the foodservice industry, using an online research panel survey. A structural equation model was developed and tested to examine the hypothesized relationships. Additionally, a multi-group analysis was performed to test the moderating roles of public pressures and climate change awareness.

The findings from this study confirmed that foodservice employees’ characteristics, including traditional barriers, and blockchain technology factors, like perceived risk, are both significant in forming resistance to blockchain. This study also demonstrated the significant roles of internal and external stakeholders in weakening the negative associations between blockchain resistance and adoption intentions.

This study recommends that foodservice companies address how to reduce their employees’ negative perceptions about changes imposed by blockchain adoption. This study also suggests the joint consideration of the pressures from internal and external stakeholders to provide continued insights into developing environmental practices for the foodservice industry.

This study extends the theoretical underpinning of the innovation resistance theory by incorporating the stakeholder theory as a strong foundation for understanding how external pressures and internal awareness may influence foodservice employees’ responses to the implementation of blockchain technology to mitigate climate change.

This paper aims to provide an elucidative and explanatory overview of decision-making theory that human resource management and development (HR) researchers and practitioners can use to explore the impact of heuristics and biases on organizational decisions, particularly within HR contexts.

This paper draws upon three theoretical resources anchored in decision-making research: the theory of bounded rationality, the heuristics and biases program, and cognitive-experiential self-theory (CEST). A selective narrative review approach was adopted to identify, translate, and contextualize research findings that provide immense applicability, connection, and significance to the field and study of HR.

The authors extract key insights from the theoretical resources surveyed and illustrate the linkages between HR and decision-making research, presenting a theoretical framework to guide future research endeavors.

Decades of decision-making research have been distilled into a digestible and accessible framework that offers both theoretical and practical implications.

Heuristics are mental shortcuts that facilitate quick decisions by simplifying complexity and reducing effort needed to solve problems. Heuristic strategies can yield favorable outcomes, especially amid time and information constraints. However, heuristics can also introduce systematic judgment errors known as biases. Biases are pervasive within organizational settings and can lead to disastrous decisions. This paper provides HR scholars and professionals with a balanced, nuanced, and integrative framework to better understand heuristics and biases and explore their organizational impact. To that end, a forward-looking and direction-setting research agenda is presented.

Images and caption descriptions serve as important visual stimuli that influence consumer preferences; therefore, the current study focuses on property images and captions illustrated on the home pages of accommodation-sharing platforms. Specifically, this study investigates the relative importance of hue, brightness and saturation of a property image and caption description styles on potential consumers’ preferences.

A mixed-method approach was used, and a total of 293 valid responses were collected through a discrete choice experiment approach. Interviews were conducted for additional analyses to explore the detailed explanations.

The utility model demonstrated that the image’s saturation was the most critical attribute perceived by the respondents, followed by caption description style, hue and brightness.

This is one of the first studies to investigate the display of attributes on a digital accommodation platform by exploring potential customers’ stated preferences. This study focuses explicitly on images and captions illustrated on the home page of an accommodation booking platform. Detailed image investigation is also a new research area in sharing economy-related research.

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 (RM). 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.

The study demonstrates the ability of the MSapeMER to reduce the asymmetry bias generated by MAPE. Furthermore, this study demonstrates that MSapeMER is more effective than previous attempts to correct for asymmetry bias. The results show via simulation and empirical investigation that the error metric is more stable and less swayed by the presence of over and under forecasts.

It is recommended that hospitality RM researchers and professionals adopt MSapeMER when using MAPE to evaluate forecasting performance. The MSapeMER removes the potential bias that MAPE invites due to its calculation and presence of over and under forecasts. Therefore, forecasting evaluations may be less affected by the presence of over and under forecasts and their ability to bias forecasting results.

Hospitality RM should adopt this measure when MAPE is used, to reduce biased decisions driven by the “asymmetry of MAPE.”

The MAPE error metric exhibits an asymmetry problem, and this paper proposes a more effective solution to reduce biased results with two major methodological contributions. It is first to systematically study the characteristics of MAPE’s asymmetry, while proposing and testing a measure that considerably reduces the amount of asymmetry. This is a critical contribution because MAPE is the primary forecasting metric in hospitality and tourism studies. The second methodological contribution is a procedure developed to “quantify” the asymmetry. The approach is demonstrated and allows future research to compare asymmetric characteristics among various accuracy measures.