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The present study examines a range of police force on the continuum (firearms, TASER/chemical spray and physical force) to see whether they are associated with individual (subject and officer), situational and/or neighborhood factors.

A partial proportional odds model is used to analyze police use of force data from 2003 to 2016 in Dallas. Independent variables are allowed for varying effects across the different cumulative dichotomizations of the dependent variable (firearms vs TASER/chemical spray and physical force and firearms and TASER/chemical spray vs physical force).

Most officer demographic and situational factors are consistently significant across the cumulative dichotomizations of police force. In addition, suspect race/ethnicity (Hispanic) and violent crime rates play significant roles when officers make decisions to use firearms, as opposed to TASER/chemical spray and physical force. Overall, situational variables (subject gun possession and contact types) play greater roles than other variables in affecting police use of force.

Despite the large body of police use of force research, little to no research has used the partial proportional odds model to examine the ordinal nature of police force from physical to intermediate to deadly force. The current findings can provide important implications for policy and research.

Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking, especially for longer-stay travelers. The geographical distance is also considered in understanding the booking behaviors trisecting travel destinations (i.e. Americas, Europe and Asia).

The data were obtained from American Statistical Association DataFest and Expedia.com. Based on the US travelers who made hotel reservation on the website, the study used a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination.

The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay hotel guests. Several similarities and differences were found from the significant determinants of the decision tree, in accordance with the geographic distance among the Americas, Europe and Asia.

This paper presents the extension to an existing machine learning environment, and especially to the decision tree model. The findings are anticipated to expand the understanding of online hotel booking and apprehend the influential determinants toward consumers’ decision-making process regarding the relationship between geographical distance and traveler’s hotel staying duration.

This research brings a meaningful understanding of the hospitality and tourism industry, especially to the realm of machine learning adapted to an online booking website. It provides a unique approach to comprehend and forecast consumer behavior with data mining.

The present study examines the risk of citizens encountering police use of intermediate and deadly force, as opposed to using physical force, given a set of individual, situational and neighborhood variables.

The study uses data from 2003 to 2016 in the Dallas Open Data Portal. Two-level multinomial logistic regression is used to analyze the data.

The effects of citizen race differ across the types of police force. Overall, citizen race plays no significant role in the officer's decision to shoot firearms at citizens. However, there is evidence of intra-racial disparity in officer-involved shootings (OISs) between Hispanic citizens and officers. African American citizens are disproportionately exposed to display-but-don't shoot incidents, while Hispanic citizens have a lower risk of encountering police use of intermediate weapons.

The study helps to understand how citizen and officer race influence and interact across various types of police force. Implications of the results are offered in relation to relevant literature.

The past five years have seen a growth in studies of police body-worn cameras (BWCs). A large share of the research focused on individual officer attitudes toward these new law enforcement tools. The scholarship, however, focused almost exclusively on their positive and negative perceptions of body cameras or correlations between those attitudes and general officer characteristics. This study examined whether the influence of negative or “concerning” policing attitudes toward body cameras is mediated by other variables, such as officer outlooks toward law enforcement, officers' perceptions of citizen cooperation or their opinions of the public.

An online survey was distributed to a convenience sample of police offices from two Northeastern police agencies.

Findings indicate that the relationship between experience and concerning perceptions of body cameras is mediated by distrust in citizens and perceived civilian cooperation. Further, an office's outlooks regarding aggressive law enforcement tactics do not have a direct effect on concerning perceptions of body cameras, nor do they serve as a mediator between years of experience and concerning perceptions of body cameras.

Findings uncover the nuance and complexity of studying and understanding police officer outlooks and perceptions of BWCs. Future experimental designs should include general outlook measures.

This experimental study aims to examine the effectiveness of cause-related marketing messages that incorporate both text and visuals, as compared to messages comprised solely of text, on the attitudes and behavioral intentions of restaurant customers, and to see if the impact varies across four categories of social causes (health, animal welfare, human services and the environment).

This experimental study uses a 2 (type of message) × 4 (cause category) between-subjects design.

Restaurant messages that combine text and visuals are more effective than restaurant messages with only text in engendering positive attitudinal and behavioral responses. This paper also found interaction effects between advertisement type and cause category on individuals’ responses (i.e. attitudes and behavioral intentions).

The messaging strategies suggested by this research will allow the restaurant industry to capitalize on the value of cause-related marketing initiatives.

This research contributes to the hospitality literature by expanding the realm of research on effective cause-related marketing initiatives.

The purpose of this study was to identify the relationship between customer values, satisfaction and loyalty in the context of Airbnb.

The survey questionnaires were developed based upon review of previous studies. The samples were collected from US- based Airbnb users. The data were analyzed using structural equation modeling.

The result of this study indicates that first, Airbnb users’ hedonic value has a positive impact on satisfaction and loyalty, while utilitarian value influences only on satisfaction. Second, this study also shows that product involvement plays as a moderating role in the paths between hedonic value and customer satisfaction.

Study findings may help researchers understand the roles of hedonic and utilitarian values and their impacts on satisfaction and loyalty in the context of Airbnb. This study also contributes to Airbnb marketers in fulfilling users’ expectations about the company.

This research contributes to the hospitality literature by expanding the realm of research on Airbnb, which is a unique company applying the sharing economy concept in the lodging industry.

This study aims to examine the brand tourism effect observed in luxury hotels. The study assumed that when loyal customers of luxury hotels perceive two different types of non-loyal customers, loyal customers’ perceptions might influence their behavioral intention. In addition, two emotions (i.e. anger and pride) might mediate the relationship between perceptions and behavioral intention.

This study used a scenario-based experimental design. Data from 1,013 responses were analyzed using partial least squares-structural equation modeling.

The study reveals that when loyal customers view brand tourists, infringement has a positive influence on switching intention, and likability positively effects brand loyalty. This study indicated that loyal customers perceive brand immigrants negatively, whereas brand tourists are comparably positively perceived by loyal customers. Only pride mediates these two relationships, respectively.

The study confirmed the brand tourism effect in luxury hotels by indicating a clearer relation between perception, emotion and behavioral intention. The theoretical implications could suggest insightful guidelines for future studies regarding loyalty in luxury hotels.

Urban sociological research posits a strong correlation between social isolation and the growth in illicit activities of street culture, namely the drug trade and violent gang activities. However, in this article we offer an explanation for why, even in the absence of extreme poverty and social isolation from mainstream institutions, youths in Cambridge, Massachusetts feel vulnerable to illicit street cultural activities. We also offer an explanation for why these youths perceive the effects of social dislocation to be similar to that experienced by youths from larger central cities. As we will elaborate below, some students in Cambridge are affected by illicit street cultural activities because: (1) social dislocation is a relative phenomenon and not merely an absolute phenomenon as described by William J. Wilson; (2) there is a social dislocation spill‐over effect from larger central cities that intensifies or amplifies the experiences of youths in the relatively poorer neighborhoods of Cambridge; (3) and some youths, from stable working‐class or wealthier neighborhoods in Cambridge, view involvement in the illicit activities of street culture as a reputable means of gaining peer respect through status group affiliation.

In the tourism industry, unmanned aerial vehicles (UAVs) can perform monitoring and patrol missions to protect assets and tourists at attractions such as coastal areas, canyons, national parks, etc. Such use of UAVs can improve safety and security of tourism attraction and facilitate tourism industry. However, there is a key issue regarding economic investment and efficient operation for actual system implementation. The purpose of this paper is to provide a guideline for supporting economic investment and the efficient operation of UAV system in the tourism industry.

Ideas and methodologies have been proposed to overcome the fundamental limitations of commercial UAVs. A mathematical optimization model is developed to determine the optimal number of UAVs to be purchased, and its operation schedules simultaneously.

Using proposed concept and methodology, UAVs can support long duration of missions. Economic system design as well as the operation schedule is derived with the hypothesis data in Kkot-Ji beach in Korea. The proposed methodology and approach is expected to have huge potential at many tourism attractions to achieve the safety and security of tourists.

The result of this study can facilitate practical use of UAVs in the tourism industry. Furthermore, it is applicable in many industries that need UAV systems such as national defence, agriculture, disaster management, etc.

The proposed study suggests a solution to handle fundamental weakness of UAVs and support long duration of missions. In addition, this study incorporates economic system design issue and operation issue simultaneously.

This study investigates the pricing and inventory control decisions of a company that owns a coffeehouse chain providing drip coffee and a coffee manufacturing factory producing ready-to-drink (RTD) coffee. To determine a way to maximize profit, optimal pricing and inventory control strategies are studied for both the RTD coffee as well as the coffeehouse chain network.

It is assumed that the company sells only drip coffee through its coffeehouse chain and would like to launch RTD coffee via other channels such as convenience stores, supermarkets and so on to maximize its total profit. A mathematical model–based optimization is adopted to address the decision-making for the given problem situation, where the demand for both drip and RTD coffee are dependent on the values of decision variables. To solve the proposed mathematical model, particle swarm optimization (PSO) is applied due to nonlinearity of the developed model.

It is confirmed that a company can earn more profit by launching RTD coffee, even though the profit from drip coffee would reduce. In addition, the scenario analysis shows that by launching RTD coffee under various conditions, the total profits would also improve.

The value of this study is the proposed basic framework for the industry. In addition to the modeling framework and cost structure, realistic cost figures and technical details can be considered when applying the model to a practical setting.