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Using longitudinal data, this study aims to provide a greater understanding as to how parenting factors, including the employment of various disciplinary techniques, during a young person's early adolescence may contribute to excessive Internet use (EIU) in later adolescence.

Employing “Problem Behaviour” theory (PBT) as a guiding framework, this study uses data from the Growing Up in Ireland ’98 Cohort to investigate the effect of proximal and distal parental influences, measured when children were 13 years old, on symptoms of EIU in young adults at 17 or 18 years. Multiple regression models control for other child and family factors, and separate models for males and females examine sex differentials.

Estimation did not find a statistically significant association between internet-specific mediation practices in early adolescence and EIU in later adolescence. However, regularly playing games or sports together is a protective factor. Parent-adolescent conflict and spending time home alone are estimated as risk factors. How parents deal with misbehaviour is a strong predictor of EIU, with the direction of association dependent upon the type and frequency of discipline employed.

The findings are of practical significance in informing parents of modifiable aspects of their behaviour that can lead to EIU.

The study applies a longitudinal modelling framework and considers the effect on EIU of various parental disciplinary techniques, representing a novel contribution.

The purpose of this paper is to investigate the effect of consumer sustainability attitudes and quick service restaurants (QSRs) practices along with the willingness of consumers to pay a premium for sustainability efforts.

A random sample of QSR customers in Germany resulted in 428 completed surveys. First, common factor analysis was conducted to assess the summated scales related to the sustainable behavior of customers, the importance attached by them to the different dimensions of sustainability and the extent to which customers perceive that QSR implement such practices. Second, the effect of these summated scales on the willingness to pay a premium (WTPP) for sustainability practices were assessed by means of a logistic regression.

The findings indicated that WTPP for sustainability efforts is primarily driven by internal beliefs and behaviors of consumers themselves rather than actions by QSR firms. Furthermore, when comparing five major QSRs, QSR brands did not appear to create a strong point of differentiation in their sustainability practices in the minds of frequent QSR consumers in the context of this study.

Implications of these results suggest that a growing number of consumers place high importance on sustainability and engage in personal sustainability practices that impact behaviors such as QSR selection and a WTPP for QSR brands and products that are perceived as implementing sustainable practices.

This paper addresses a gap by assessing drivers of willingness of QSR customers to pay a premium for sustainable practices and if QSR brands sustainability practices differ in the minds of consumers.

The purpose of this paper is to investigate specific marketing mix activities and influencing factors in hotels coping with falling room demand derived from drug cartel-related risk and insecurity.

A case study research was carried out using semistructured interviews with key informants (hotel managers) in two neighboring destinations at the US–Mexico border, an area where criminal organizations' drug trafficking-related violence has impacted the hospitality industry.

The research identifies factors that are internal (market segment diversification, type of ownership, magnitude of investments) and external (tourism promotion organizations, media coverage, tourist flow volume) to the firms as they affect their marketing mix implementation.

The research developed a framework to better understand the use of marketing mix practices and influencing factors in criminal insecurity contexts, which could be further studied in other risk and conflict scenarios.

The pricing and communication tactics are employed more intensively, while product-service and distribution channel actions are used to a lesser extent. Greater emphasis should be placed on product-service, distribution and market segment diversification.

Considering the positive impacts that tourism and hospitality businesses have on local communities, it is recommended that the hotel sector works together with government and industry associations to improve the safety and security at tourism destinations.

The research extends the extant knowledge in hospitality crisis management by investigating the full marketing mix tactics in hotels at destinations stricken by cartel-related organized crime, an understudied context in the literature.

This paper aims to address three research questions (1) Who is the main stakeholder that shapes Airbnb experience, (2) Does Airbnb offers an authentic travel experience? and (3) What should be the future research trends in Airbnb?

This paper uses the systematic literature review (SLR) with a well-defined protocol, research strategy and methods to answer the research questions.

The review revealed that while Airbnb plays a significant role as the platform provider, the stakeholders influencing the experiences are multifaceted. Hosts, guests, local communities and even regulatory bodies all contribute to shaping the overall Airbnb Experience ecosystem. Hosts, in particular, have a crucial role in curating and delivering unique experiences, which significantly impacts the quality and authenticity of the offerings. On the question of whether Airbnb offers an authentic travel experience, the review uncovered mixed findings. For examples, some studies emphasized the potential for Airbnb to provide authentic and local experiences, allowing travelers to engage with the community and cultural aspects of a destination. However, other studies raised concerns about the commodification and standardization of experiences, leading to a potential loss of authenticity.

This paper is different from previous SLR where previous research systematically reviewed; motivations to use and choose Airbnb, institutionalization of Airbnb, stakeholders of Airbnb. This paper addresses authentic experience as a factor that influences activity participation.

This paper aims to analyze the ethical responses of the fashion industry to the first wave of the COVID-19 pandemic when the entire world was shocked by the rapid spread of the virus. The authors describe lessons from emergency ethics of care in the fashion industry during the initial months of COVID-19, which can assist fashion managers in improving ethical decisions in future operations.

Rapid qualitative research methods were employed by conducting real-time, in-depth interviews with key informants from multinational fashion companies operating in Spain, a severely affected region. A content analysis of news articles published during the first months of 2020 was conducted.

Five critical disruptions in the fashion industry were identified: (1) changes in public needs, (2) transportation and distribution backlogs, (3) defective and counterfeit supplies, (4) stakeholder relationships at stake and (5) managers' coping challenges. Additionally, five business survival responses with a strong ethics of care component were identified, implemented by some fashion companies to mitigate the damage: (1) adapting production for public well-being, (2) enhancing the flexibility of logistic networks, (3) emphasizing quality and innovation, (4) reinventing stakeholder collaborations and (5) practicing responsible leadership.

Despite the well-documented controversies surrounding unethical practices within the fashion industry, even during COVID-19, our findings inform managers of the potential and capability of fashion companies to operate more responsibly. The lessons learned can guide fashion companies' operations in a post-pandemic society. Furthermore, they can address other grand challenges, such as natural disasters, geopolitical conflicts and climate change.

This study aims to explore the risk and protective factors of abuse on older adults by family caregivers, with a special focus on the protective role of caregiver resilience in elder abuse.

This cross-sectional survey was conducted on a purposive sample of 600 family caregivers of community-dwelling older adults in Hong Kong (mean age = 71.04 and female = 67.2%). Caregivers reported in a guided interview about elder abuse behaviours, caregiver burden, care recipients’ agitated behaviours, caregiver resilience, self-efficacy, social support and basic demographic characteristics. Hierarchical linear regression analyses were conducted to examine the predictors of different forms of elder abuse.

Caregiver resilience was predictive of lower levels of verbal abuse, physical abuse, injury and financial exploitation but not potentially harmful behaviour (PHB). Social support was independent with all forms of elder abuse, while self-efficacy predicted greater physical abuse after the adjustment of confounding variables. Caregiver burden and agitated behaviours by care recipients remained as significant risk factors in the final models when protective factors were considered.

This study extends current knowledge on the protecting role of resilience in elder abuse in family caregiving. Mixed findings revealed on social support and self-efficacy also highlight the complexity of the prediction of caregiver abuse. Further research should address this area.

The findings of this study warrant the inclusion of caregiver resilience as a key component in developing interventions to prevent elder abuse. Addressing caregiver burden and agitated behaviours have the potential in preventing elder abuse.

The findings raise awareness of the importance of supporting caregivers in the community to prevent elder abuse.

Research concerning the protective factors of elder abuse is in a preliminary stage. To the best of the authors’ knowledge, this study is among the first which successfully demonstrates the protective role of resilience in caregiver abuse on older adults. The findings shed invaluable light on the design of effective interventions.

Exploring the influencing factors on urban rail transit (URT) ridership is vital for travel demand estimation and urban resources planning. Among various existing ridership modeling methods, direct demand model with ordinary least square (OLS) multiple regression as a representative has considerable advantages over the traditional four-step model. Nevertheless, OLS multiple regression neglects spatial instability and spatial heterogeneity from the magnitude of the coefficients across the urban area. This paper aims to focus on modeling and analyzing the factors influencing metro ridership at the station level.

This paper constructs two novel direct demand models based on geographically weighted regression (GWR) for modeling influencing factors on metro ridership from a local perspective. One is GWR with globally implemented LASSO for feature selection, and the other one is geographically weighted LASSO (GWL) model, which is GWR with locally implemented LASSO for feature selection.

The results of real-world case study of Shenzhen Metro show that the two local models presented perform better than the traditional global model (OLS) in terms of estimation error of ridership and goodness-of-fit. Additionally, the GWL model results in a better fit than GWR with global LASSO model, indicating that the locally implemented LASSO is more effective for the accurate estimation of Shenzhen metro ridership than global LASSO does. Moreover, the information provided by both two local models regarding the spatial varied elasticities demonstrates the strong spatial interpretability of models and potentials in transport planning.

The main contributions are threefold: the approach is based on spatial models considering spatial autocorrelation of variables, which outperform the traditional global regression model – OLS – in terms of model fitting and spatial explanatory power. GWR with global feature selection using LASSO and GWL is compared through a real-world case study on Shenzhen Metro, that is, the difference between global feature selection and local feature selection is discussed. Network structures as a type of factors are quantified with the measurements in the field of complex network.

This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers.

The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes.

The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates.

Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance.

This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

The purpose of this study is to examine the moderating role of the characteristics of the chief executive officer (CEO) on the association between CEO power and corporate social responsibility (CSR) performance.

This paper conducts multiple regression analyses to empirically test the proposed hypotheses based on a sample of US-based publicly held companies. The sample period extends from 2000 to 2018. Firm-level CSR ratings are obtained from the Kinder, Lydenberg and Domini (KLD) database (currently known as MSCI ESG STATS). Financial data and CEO data are retrieved from Compustat and ExecuComp databases, respectively. Additional test and robustness analysis are performed.

This paper shows that firms with more powerful CEOs are less likely to engage in CSR activities. The negative association between CEO power and CSR is found to be exacerbated by CEOs who are younger, more competent and overconfident; however, this negative association is mitigated by CEOs who are female. This paper also finds that gender plays a more important role among CEO characteristics. Collectively, the findings highlight the potential opportunities to better understand the role of various CEO characteristics that jointly affect CSR.

First, this is the first study providing a comprehensive empirical analysis of how various CEO characteristics jointly affect CSR. Prior studies that focus on standalone CEO characteristics offer an incomplete picture of the relation between a single CEO characteristic and a firm's CSR performance. The current study thus extends the research field by examining the association between seemingly unrelated CEO characteristics and CSR performance. The results also highlight that gender is the critical factor moderating the relationship between CEO power and CSR performance when it is compared with CEO age, ability and overconfidence. Second, the authors add to the literature on employee selection by showing that female CEOs mitigate the negative effect of managerial power on CSR performance. Although the currently available empirical research in management control systems focuses on ex-post analyses of moral hazard mitigation for incumbent employees, both the economics and management literature acknowledge ex ante evidence suggesting that employee selection is even more important. Our findings may provide insight into the selection of CEOs.