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This paper aims to identify the key drivers of occupancy rates in peer-to-peer accommodation.

The applied methodology fits the specific characteristics of this market segment: the peculiar distribution of the occupancy rate (a ratio characterised by a large share of zeros) requires the adoption of a mixed discrete-continuous model; the insidious issue of price endogeneity is dealt with a control function approach; the econometric specification takes into account the monopolistic competition, the relevant market regime in the hospitality industry. The model is tested on Airbnb listings in the Balearic Islands (Spain).

The occupancy rate of peer-to-peer properties in the Balearic Islands strongly depends on their geographical location and online reputation. There is a qualitative difference between two groups: listings with positive occupancy rates, which demand tends to be inelastic, and listings with zero occupancy. The authors found that the price is a not a statistically significant determinant of the latter group membership.

This paper applies a zero-inflated beta model, never used in previous analyses of occupancy rates, to provide a benchmark for future studies. This procedure allows the estimation of unbiased marginal effects. It, thus, offers important technical and managerial implications, as a wrong understanding of how occupancy depends on price would deliver ineffective managerial decisions. This paper highlights the importance of methodological choices, as coefficients are highly sensitive to misspecifications of the model.

This study systematically reviewed pricing research published in leading marketing and hospitality and tourism (H&T) journals between 2010 and 2019. It attempts to concretize the understanding of the evolving patterns of pricing research in both fields and suggests an agenda for future research in H&T.

This study performed keyword co-occurrence analyses and co-citation analyses on the bibliographic data of 575 articles from marketing and H&T journals. Content analysis was applied to investigate the emerged topics in H&T.

The marketing discipline showed a persistent focus on research themes including price promotion, reference price, price fairness and pricing strategy. The H&T domain experienced a significant content enrichment of preexistent research topics. H&T scholars showed a growing interest in studying pricing for sharing economy accommodations and the interplay between pricing and electronic word-of-mouth. While marketing research applied theories from multiple disciplines as the theoretical foundations, H&T studies adopted the hedonic pricing model as an overarching theory.

Future pricing research in H&T may incorporate theories from other disciplines such as psychology, sociology and anthropology to broaden the scope of the study. Besides, innovative study designs and complex data analysis techniques should be encouraged. Topicwise, H&T scholars can dive deeper into price promotion and distribution channel price management.

This is the first study consolidating the pricing literature in H&T and marketing. It also suggests the potential research directions for researchers.

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

Social capital is used as a theoretical framework to reveal the importance of relationships between officers and their supervisors for performing community policing. It is expected that officers with higher levels of social capital will accomplish more community policing than their peers who have lesser amounts of trust, cooperation, group cohesion, and social support in their work relationships. Using data from the Project on Policing Neighborhoods, two measures of community policing were developed. Results from negative binomial and zero‐inflated negative binomial regression models did not support the premise that police social capital is related to officer performance of community policing. Instead, officer performance varied significantly according to the department in which the officer worked, whether officers were assigned to be community policing specialists, and their levels of tenure. Implications of these findings are discussed in terms of organizational factors that promote or hinder the implementation of community policing.

This study investigates how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' purchasing and gift-giving behaviors and how broadcaster popularity moderates social interaction-oriented content's effect on the two different behaviors in live-streaming commerce.

A research model was proposed and empirically tested using a panel data set collected from 537 live streams via Douyin (the Chinese version of TikTok), one of the most popular live broadcast platforms in China. A fixed-effects negative binomial regression model was used to examine the proposed research model.

This study's results show that social interaction-oriented content in broadcasters' live speech has an inverted U-shaped relationship with broadcast viewers' purchasing behavior and shares a positive linear relationship with viewers' gift-giving behavior. Furthermore, broadcaster popularity significantly moderates the effect of social interaction-oriented content on viewers' purchasing and gift-giving behaviors.

This research enriches the literature on live-streaming commerce by investigating how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' product-purchasing and gift-giving behaviors from the perspective of broadcast viewers' attention. Moreover, this study provides some practical guidelines for developing live speech content in the live-streaming commerce context.

Bayesian A/B inference (BABI) is a method that combines subjective prior information with data from A/B experiments to provide inference for lift – the difference in a measure of response in control and treatment, expressed as its ratio to the measure of response in control. The procedure is embedded in stable code that can be executed in a few seconds for an experiment, regardless of sample size, and caters to the objectives and technical background of the owners of experiments. BABI provides more powerful tests of the hypothesis of the impact of treatment on lift, and sharper conclusions about the value of lift, than do legacy conventional methods. In application to 21 large online experiments, the credible interval is 60% to 65% shorter than the conventional confidence interval in the median case, and by close to 100% in a significant proportion of cases; in rare cases, BABI credible intervals are longer than conventional confidence intervals and then by no more than about 10%.

This study aims to investigate patterns of social media posting occurring during the initial post-disaster recovery period. In addition, the study investigates the antecedents of user engagement with Twitter posts or “tweets.”

This study uses Hurricane Harvey as the research focus because of its $125bn in damage and the recovery effort associated with it. Analyzing nearly 38,000 tweets related to 2017’s Hurricane Harvey and specifically focuses on rescuers who became to be known as the “Cajun Navy.”

The popularity of the tweet significantly influences social media engagement; disaster relief organizations first should seek out alliances and partnerships with those who already are well-known or influential in social media. In addition, by regularly monitoring and participating in social media, such as encouraging retweets and mentions even when not responding to a disaster, agencies will gain social media followers and influence when these are most needed.

By analyzing patterns of social media posts during and after Hurricane Harvey, this study attempts to quantify social media’s effectiveness during this disaster, in particular with a focus on what characteristics of Harvey-related Twitter posts most influenced user engagement.

Findings underscore the need for agencies to strengthen their social media presence and use it in all three phases of disaster conceptualization. Beyond managing communication with other agencies and the community, which alone is critical for an agency’s impact, social media offers the world as a potential audience for agencies that can deftly leverage it.

Social media has revolutionized not only communication but also inter-connectivity as people have normalized its use. Its ubiquity and efficiency have seen its incorporation into the critical area of emergency management, both during and after disasters. The main objective of this paper is to investigate and evaluate the usefulness of social media in disaster recovery efforts, focusing on its advantages in relation to a recent disaster event.

The purpose of this paper is to examine how installed base information could help servitizing original equipment manufacturers (OEMs) forecast and support their industrial service sales, and thus increase OEMs’ understanding regarding the dynamics of their customers lifetime values (CLVs).

This work constitutes a constructive research aiming to arrive at a practically relevant, yet scientific model. It involves a case study that employs statistical methods to analyze real-life quantitative data about sales and the global installed base.

The study introduces a forecasting model for industrial service sales, which considers the characteristics of the installed base and predicts the number of active customers and their yearly volume. The forecasting model performs well compared to other approaches (Croston’s method) suitable for similar data. However, reliable results require comprehensive, up-to-date information about the installed base.

The study contributes to the servitization literature by introducing a new method for utilizing installed base information and, thus, a novel approach for improving business profitability.

OEMs can use the forecasting model to predict the demand for – and measure the performance of – their industrial services. To-the-point predictions can help OEMs organize field services and service production effectively and identify potential customers, thus managing their CLV accordingly. At the same time, the findings imply new requirements for managing the installed base information among the OEMs, to understand and realize the industrial service business potential. However, the results have their limitations concerning the design and use of the statistical model in comparison with alternative approaches.

The study presents a unique method for employing installed base information to manage the CLV and supplement the servitization literature.

Do consumers rate reviews describing other consumers' sensory experience of a product (touch, smell, sight, hear and taste) as helpful or do they rate reviews describing more practical properties (product performance and characteristics/features) as more helpful? What is the effect of review helpfulness on purchase intention? Furthermore, why do consumers perceive sensory and non-sensory reviews differently? This study answers these questions.

The authors analyze 447,792 Amazon reviews and perform a topic modeling analysis to extract the main topics that consumers express in their reviews. Then, the topics were used as regressors to predict the number of consumers who found the review helpful. Finally, a lab experiment was conducted to replicate the results in a more controlled environment to test the serial mediation effect.

Contrary to the overwhelming evidence supporting the positive effects of sensory elicitation in marketing, this study shows that sensory reviews are less likely to be helpful than non-sensory reviews. Moreover, a key reason why sensory reviews are less effective is that they decrease the objective perception of the review, a less objective review then decreases the level of helpfulness, which decreases purchase intention.

This study contributes to the interactive marketing field by investigating customer behavior and interactivity in online shopping sites and to the sensory marketing literature by identifying a boundary condition, the authors’ data suggest that sensory elicitations might not be processed positively by consumers when they are not directly experienced, but instead communicated by another consumer. Moreover, this study indicates how companies can encourage consumers to share more effective and helpful reviews.

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant literature has examined the role of solar photovoltaic (PV) adoption and user costs, with an emerging corpus of literature investigating the role of the determinants of PV uptake, particularly in relation to the built environment and the spatial variation of PV dependency and dissimilarity. Despite this burgeoning literature, there remains limited insights from the UK perspective on housing market characteristics driving PV adoption and in relation spatial differences and heterogeneity that may exist.

Applying micro-based data at the Super Output Area-level geography, this study develops a series of ordinary least squares, spatial econometric models and a logistic regression analysis to examine built environment, housing tenure and deprivation attributes on PV adoption at the regional level in Northern Ireland, UK.

The findings emerging from the research reveal the presence of some spatial clustering and PV diffusion, in line with several existing studies. The findings demonstrate that an urban-rural dichotomy exists seemingly driven by social interaction and peer effects which has a profound impact on the likelihood of PV adoption. Further, the results exhibit tenure composition and “economic status” to be significant and important determinants of PV diffusion and uptake.

Housing market characteristics such as tenure composition across local market structures remain under-researched in relation to renewable energy uptake and adoption. This study examines the role of housing market attributes relative to socio-economic standing for adopting renewable energy.