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BackgroundCannabis exposures reported to the California Poison Control System increased following the initiation of recreational cannabis sales on 1 January 2018 (i.e., "commercialization"). We evaluated whether local cannabis control policies adopted by 2021 were associated with shifts in harmful cannabis exposures.MethodsUsing cannabis control policies collected for all 539 California cities and counties in 2020-2021, we applied a differences-in-differences design with negative binomial regression to test the association of policies with harmful cannabis exposures reported to California Poison Control System (2011-2020), before and after commercialization. We considered three policy categories: bans on storefront recreational retail cannabis businesses, overall restrictiveness, and specific recommended provisions (restricting product types or potency, packaging and labeling restrictions, and server training requirements).ResultsLocalities that ultimately banned storefront recreational retail cannabis businesses had fewer harmful cannabis exposures for children aged 13 years (rate ratio = 0.97; 95% confidence interval = 0.85, 1.11). Of 167 localities ultimately permitting recreational cannabis sales, overall restrictiveness was not associated with harmful cannabis exposures among children aged 13 years, a 1-standard deviation increase in ultimate restrictiveness was associated with fewer harmful cannabis exposures (rate ratio = 0.93; 95% confidence interval = 0.86, 1.01). For recommended provisions, estimates were generally too imprecise to detect associations with harmful cannabis exposures.ConclusionBans on storefront retail and other restrictive approaches to regulating recreational cannabis may be associated with fewer harmful cannabis exposures for some age groups following statewide commercialization.

Although the considerable body of research on control variables offers best practice recommendations, there are still shortcomings in their application in published research papers. Consequently, a significant proportion of quantitative studies lack rigor in how they apply control variables. Additionally, existing literature on control variables is primarily technical, with noticeable lack of accessible guidance that could benefit both seasoned and novice researchers. In fact, there is a need for more elaboration in non-technical terms to enhance our understanding of effective control variable usage. Therefore, the objectives of this editorial are twofold. First, we elaborate on five key issues related to control variables, including a clear, easy-to-understand explanation of what a control variable is, its advantages and complications, and the issues associated with under- or over-controlling variables. Second, we provide guidelines that are easy to follow for any researcher, whether experienced or novice. In doing so, we clarify several ambiguities and offer straightforward explanations of issues related to control variables. We hope that these guidelines will assist researchers—whether lecturers or students—by providing clear directions on the appropriate use of control variables.

COVID-19 pandemic has forced the implementation of a new normal for all businesses leading to an increase in stress levels for many including educators and students. Ethics in education was questioned by professional bodies due to online exams. The objective of this study is to determine the antecedents of student online learning effectiveness and its impact on educational ethics. The study compares Malaysia and India since some Indian educational institutions had been implementing online teaching prior to the pandemic. Data was collected through a survey method. 571 questionnaires from students across Malaysia and India were analyzed using SPSS and PLS-SEM. It was found that physical, psychological, intellectual, and emotional attributes were significant in predicting learning effectiveness which in turn had an impact on educational ethics. Intellectual attributes were the most important predictor of learning effectiveness. The emotional and psychological attributes had a small impact on learning effectiveness. Furthermore, physical attributes had trivial effects on the learning effectiveness. A T test comparing mean differences between the two countries revealed that there were no significant differences in learning effectiveness between students of both countries while educational ethics was more evident among Indian students as compared to Malaysian students. The key implication is the need for educational institutions and educators to develop pedagogies to stimulate students’ intellectual abilities during online teaching to preserve educational ethics. HEIs must support the students psychologically, emotionally and encourage themselves to keep physically active during the phase of online classes. Institutions must take ample steps to ensure that educational ethics is not affected as a result of the new normal failing which could prove detrimental in the long run for students and institutions. This will help to curb unethical practices during online examinations and mitigate academic dishonesty. Our findings regarding various attributes of online learning effectiveness add profoundly to literature and help to understand antecedents contributing to successful learning effectiveness which leads to positive educational ethics in both the countries.

This study investigates the relationship between Employee-Centred Corporate Social Responsibility (ECCSR) and organisational resilience, focusing on empirically validating the mediating effects through rigorous model comparison approach to enhance the robustness of the findings. Non-probability approach, specifically purposive sampling was employed to reach the targeted respondents. Respondents were selected based on two criteria: being full-time employees and having been employed during and after the global pandemic. Data were gathered through a self-administered questionnaire and analysed using variance-based structural equation modeling (SEM). The results indicate a significant positive effect of ECCSR on organisational resilience. Additionally, the mediation effects of workplace well-being and social well-being are confirmed through a model comparison approach, which quantifies the level of uncertainty introduced by these mediating effects. The study is novel in three aspects: First, it categorises employee wellbeing into two dimensions, namely, workplace well-being and social well-being; second, it conceptualises the impetus of ECCSR in terms of the Matthew Effect, highlighting the self-reinforcing nature of benefit accumulation; and third, it employs a quality assessment to quantify the presence of mediators. Organisations are encouraged to consider and leverage the positive impacts associated with ECCSR, such as increased employee engagement and retention, as well as improved corporate reputation.

Microfinance Institutions (MFIs) often grapple with allocating adequate resources toward Corporate Social Responsibility (CSR), despite their crucial societal impact. This research investigates CSR assessment and CSR sustainability in the context of strategic CSR-related activities of MFIs, aiming to optimize resource allocation and enhance their CSR initiatives. Data was collected from 875 employees and clients of large MFIs in the Philippines and analyzed using structural equation modeling in AMOS 24. The findings revealed that CSR assessment and CSR sustainability were significantly linked to strategic CSR-related activities, and CSR assessment had a significant link with CSR sustainability. These findings emphasize the importance of strategic orientation in CSR related activities and support the logical precedence of Carroll's CSR Pyramid and Battal's CSR Train Theories, which advocate for a balanced approach encompassing economic, legal, ethical, environmental, and philanthropic responsibilities. This study contributes to the literature by exploring strategic and sustainable CSR in MFIs in developing countries, where the topic has not been broadly studied. The results can serve as a reliable basis for ensuring transparency, assessment effectiveness, and the creation of an integrative CSR framework for MFIs.

The research investigates the effect of restaurant authenticity on perceived service quality, customer satisfaction, and electronic word of mouth (eWOM) in the context of ethnic restaurants. Additionally, it examines the mediating role of service quality and customer satisfaction, as well as their parallel mediating effect on the relationship between restaurant authenticity and diners’ eWOM intention. Data was collected from 279 diners who had experienced services at ethnic restaurants in Vietnam. The Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was employed to investigate the relationships among the variables in the research model. The findings reveal that restaurant authenticity directly influences perceived service quality, customer satisfaction, and customers’ eWOM intention. Customer satisfaction fully mediates the relationship between service quality and eWOM, while it partially mediates the effect of restaurant authenticity on customer eWOM. However, the indirect impact of restaurant authenticity on customer eWOM through service quality was not significant. Ethnic restaurant managers should focus on enhancing the authenticity of their restaurants to generate positive outcomes such as increased customer satisfaction and eWOM intention. This study contributes to the existing literature on consumer behavior in the hospitality sector by conceptualizing the mediating role of service quality and customer satisfaction in the relationship between restaurant authenticity and eWOM intention. The research also extends the Stimuli Organism-Response model by considering restaurant authenticity as a stimulus that directly and indirectly influences service quality (stimulus), customer satisfaction (organism), and customer eWOM intention (response).

BackgroundCannabis outlets may affect health and health disparities. Local governments can regulate outlets, but little is known about the effectiveness of local policies in limiting outlet densities and discouraging disproportionate placement of outlets in vulnerable neighborhoods.MethodsFor 241 localities in California, we measured seven policies pertaining to density or location of recreational cannabis outlets. We geocoded outlets using web-scraped data from the online finder Weedmaps between 2018 and 2020. We applied Bayesian spatiotemporal models to evaluate associations of local cannabis policies with Census block group-level outlet counts, accounting for confounders and spatial autocorrelation. We assessed whether associations differed by block group median income or racial-ethnic composition.ResultsSeventy-six percent of localities banned recreational cannabis outlets. Bans were associated with fewer outlets, particularly in block groups with higher median income, fewer Hispanic residents, and more White and Asian residents. Outlets were disproportionately located in block groups with lower median income [posterior RR (95% credible interval): 0.76 (0.70, 0.82) per $10,000], more Hispanic residents [1.05 (1.02, 1.09) per 5%], and fewer Black residents [0.91 (0.83, 0.98) per 5%]. For the six policies in jurisdictions permitting outlets, two policies were associated with fewer outlets and two with more; two policy associations were uninformative. For these policies, we observed no consistent heterogeneity in associations by median income or racial-ethnic composition.ConclusionsSome local cannabis policies in California are associated with lower cannabis outlet densities, but are unlikely to deter disproportionate placement of outlets in racial-ethnic minority and low-income neighborhoods.

This study aims to examine how customer communication, trustworthiness, transparency, and innovation influence the transformation of digital banking. It also investigates the impact of digital banking transformation on customers’ financial behavior. Data were collected using a well-structured questionnaire and distributed to digital bank customers of ten banks in Indonesia. A total of 187 digital bank customers participated in this study. For data analysis, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) using WarpPLS 6.0. The results show that customer communication, trust, transparency, and innovation positively affect digital banking transformation. It also indicates that the digital banking transformation positively impacts customer financial behavior. This study highlights previously unexplored aspects, particularly the role of digital banking transformation as a stimulant that encourages consumers to actively manage, plan, and invest their funds, which can significantly influence their financial decision-making behavior. The findings of this study highlight the importance of providing customers with a comprehensive understanding of the presence of digital banks. The advent of digital banks is expected to alter financial behaviors in the consumer decision-making process. Additionally, the government may safeguard and reassure individuals about the use of digital banking. By accelerating the pace of digital transformation and enacting appropriate regulations, Indonesia is poised to fulfill its mission of becoming a developed nation.

The adoption of multi-channel strategies by retailers, encompassing both online and offline modalities, has fundamentally transformed consumer shopping behaviors, resulting in a significant increase in mobile shopping. Nonetheless, the long-term success of mobile shopping apps is heavily dependent on consumers’ continuous use. Regrettably, the existing studies on this area remain vague and require further exploration. This study aims to close this gap by examining Malaysian consumers’ continuance intention and continuance use of mobile shopping through the lens of ISSM in the context of mobile shopping apps. We utilised a quantitative approach and successfully collected 369 responses from Klang Valley, using purposive sampling techniques. The predictive hypotheses developed were validated using Partial Least Square – Structural Equation Modelling (PLS-SEM) and were further supplemented with Necessary Condition Analysis (NCA) to determine must-have factors based on necessary logic. The PLS-SEM results confirmed the association between continuance intention and continuance use. The findings revealed that service quality and system quality are associated with satisfaction, but not information quality. Additionally, service quality, system quality, and information quality, together with satisfaction, trust and incentive have a positive significant influence on Malaysians’ mobile shopping app continuance intention. However, the NCA results indicated that system quality is the sole must-have factor that contributes to satisfaction. Meanwhile, service quality, system quality, information quality, satisfaction, trust, and incentive are the must-have predictors for mobile shopping app continuance intention. The study sheds light on previously unexplored aspects namely, continuance intention and continuance use, within the framework of the ISSM in the context of mobile shopping. By doing so, it offers more refined and actionable insights to improve consumer experience and foster sustained engagement with mobile shopping apps. Simultaneously, it contributes to the advancement of knowledge regarding the foundational constructs and their interconnectedness, thereby enhancing theoretical development in this domain.

This study investigates the role of transactional leadership as a pivotal antecedent to employee knowledge hiding, specifically focusing on tacit and explicit forms. Drawing on social learning theory, the study proposes and examines the indirect effect of transactional leadership on both tacit and explicit knowledge hiding through the mediation of perceived competition. Additionally, the study introduces leader knowledge hiding as a potential boundary condition to these relationships. Data were collected from university employees in Pakistan using a time-lagged design. The study employed structural equation modeling, utilizing AMOS 23, to rigorously test the proposed hypotheses. The results reveal that transactional leadership significantly encourages tacit and explicit knowledge hiding, with perceived competition as the underlying mechanism. However, the hypothesized moderating effect of leader knowledge hiding is not supported. This study is a pioneering effort to understand the nuanced relationship between transactional leadership, perceived competition, and the distinct forms of knowledge hiding—tacit and explicit. Incorporating leader knowledge hiding as a potential boundary condition adds a unique dimension to the existing literature, contributing novel insights. Our findings hold implications for knowledge management and transactional leadership literature. Practitioners can glean valuable insights from the study's results to inform strategies to mitigate knowledge hiding behaviors within organizational settings.

This study examines the influence of utilitarian and hedonic experiences, customer engagement, and customer value anticipation on customer retention in the authorized automotive after-sales service sector, using customer satisfaction and customer delight as mediators. We collected 316 samples and employed Partial Least Squares Structural Equation Modeling for data analysis. The findings confirm the direct impacts of utilitarian and hedonic experiences, customer engagement, and customer value anticipation on both customer satisfaction and customer delight. Additionally, customer satisfaction and customer delight directly influence customer retention and mediate the relationship between the hypothesized predictors and customer retention. This research enriches the customer retention literature by integrating Expectancy Disconfirmation Theory with flow theory, customer engagement theory, and customer value theory. It also provides insights into the predictors of customer retention and the mediating roles of customer satisfaction and customer delight in the authorized automotive after-sales service sector. The results suggest that service providers innovate their offerings and focus on enhancing customer satisfaction and delight to meet and exceed customer expectations.

This study aims to investigate social media attributes—including sharing of content, accessibility, and credibility—that can impact a tourist's intention to revisit green resorts. It also examines the mediating role of the green image between social media attributes and a tourist’s intention to revisit green resorts. The social information processing theory was utilized as the theoretical framework to explain the hypothesized relationships. Data was collected through an online survey targeted at tourists who visit the Maldives' green resorts and have used social media to search for information about these resorts. A total of 140 usable responses were collected. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the data. The results confirm that sharing of content and the accessibility of social media positively impact the green image, which in turn drives tourists’ intention to revisit. However, the credibility of social media neither directly influences the green image nor indirectly impacts tourists' intention to revisit. This study applies the social information processing theory to offer a fresh perspective on social media and sustainable marketing concepts. Additionally, it contributes to the field of sustainable marketing by investigating the concept of a green image in the context of developing economies, where this topic has not been extensively studied.

Introduction. Despite the transition of economic systems to a digital economy, today industry is a driver of the development of the economy of any country, and accordingly, consideration of the development peculiarities of this sphere in post-Soviet countries is of scientific interest. Hence, the purpose of the study is to analyze the industry development dynamics in the post-Soviet countries in the period of 1990–2021. Materials and methods. In the course of the study, methods such as analysis and synthesis, tabular and graphical, relative and average values were used. Results. At the start of the market transformation, post-Soviet countries had high industrial potential, while the share of the GVA industry in the GDP of each subject was above 20%. As we move away from 1990, more post-Soviet countries are showing a tendency to reduce the GVA ratio, which is due to the combination of factors, the main of which is the impact of the transformational transition and subsequent crises. The best results in hydrocarbon production are shown by countries with natural resource deposits: Russia, Kazakhstan, Azerbaijan and Turkmenistan, the rest of the post-Soviet countries have near-zero production indicators, which reduces their ability to invest “petrodollars” in other sectors of the economy. Indirectly, the import structure shows problems in the manufacturing industry, in which in 2021 the import of machinery and equipment prevails in almost all post-Soviet countries. Conclusions. Summarizing the results obtained during the study, we can draw a general conclusion based on the results of the analysis: having some accumulated (formed) capital in industry, the post-Soviet countries were unable to convert it into sustainable development of their economic systems and at the moment are largely forced to rely on imports of machinery, equipment and vehicles. The findings will be useful to researchers in the field of post-Soviet countries’ integration and disintegration

Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as leaving the data incomplete and failing to accommodate incomplete covariates with interactions, non-linear terms, and random slopes. We propose a Bayesian latent variable imputation approach to impute missing data due to MNAR (and other missingness mechanisms) and estimate the model of substantive interest simultaneously. In addition, even when the incomplete covariates involves interactions, non-linear terms, and random slopes, the proposed method can handle missingness appropriately. Computer simulation results suggested that the proposed Bayesian latent variable selection model (BLVSM) was quite effective when the outcome and/or covariates were MNAR. Except when the sample size was small, estimates from the proposed BLVSM tracked closely with those from the complete data analysis. With a small sample size, when the outcome was less predictable from the covariates, the missingness proportions of the covariates and the outcome were larger, and the missingness selection processes of the covariates and the outcome were more MNAR and MAR, the performance of BLVSM was less satisfactory. When the sample size was large, BLVSM always performed well. In contrast, the method with an MAR assumption provided biased estimates and undercoverage confidence intervals when the missingness was MNAR. The robustness and the implementation of BLVSM in real data were also illustrated. The proposed method is available in the Blimp software application, and the paper includes a data analysis example illustrating its use.

Introduction. Social living conditions and related problems are one of the key indicators of the level and pace of the socio-economic development of modern society. The Russians’ standard of living is constantly on the agenda of the academic community and the Government of the Russian Federation. Accordingly, the study of the state and prospects for the development of key indicators characterizing the well-being of the population is one of the main tasks of economy. Theoretical analysis. The purpose of this study is statistical analysis and identification of the socio-economic indicators influence degree on the provision of housing for the country’s population. To achieve this goal, we used such statistical tools as tabular and graphical, coefficient, grouping method and correlation-regression analysis. Empirical analysis. The methodological approach to the statistical analysis of the population’s provision with housing corresponds to three stages of statistical research and includes formation of a system of indicators, collection and processing of actual material in spatial and time sections, as well as formation of conclusions regarding the patterns of development of the phenomenon under consideration. Results. As a result of the study, the following patterns were established: despite the population’s low standard of living and periodic shock impacts on Russian economy, the level of housing provision for the population for the period 1990–2021 is continuously increasing; the highest level of housing provision is observed in the most densely populated regions of the country, which are part of the Central Federal District and the Volga Federal District; the largest influence on the variation of the population’s provision of housing is exerted by general economic indicators, namely the level of employment and the positive dynamics of the industrial production index. The results of the study can be used to make decisions regarding the improvement of the population’s standard of living in constituent entities of the Russian Federation.

Integrative data analysis (IDA) involves obtaining multiple datasets, scaling the data to a common metric, and jointly analyzing the data. The first step in IDA is to scale the multisample item-level data to a common metric, which is often done with multiple group item response models (MGM). With invariance constraints tested and imposed, the estimated latent variable scores from the MGM serve as an observed variable in subsequent analyses. This approach was used with empirical multiple group data and different latent variable estimates were obtained for individuals with the same response pattern from different studies. A Monte Carlo simulation study was then conducted to compare the accuracy of latent variable estimates from the MGM, a single-group item response model, and an MGM where group differences are ignored. Results suggest that these alternative approaches led to consistent and equally accurate latent variable estimates. Implications for IDA are discussed.

If a financial derivative can be traded consecutively and its terminal payoffs can be adjusted as the sum of a bounded process and a stationary process, then we can use the moving average of the historical payoffs to forecast and the corresponding errors form a generalised mean reversion process. Thus we can price the financial derivatives by its moving average. One can even possibly get statistical arbitrage from certain derivative pricing. We particularly discuss the example of European call options. We show that there is a possibility to get statistical arbitrage from Black–Scholes's option price.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg.

BackgroundA small number of studies have used a natural experiment approach to examine the health impacts of increased economic resources stemming from Native American-owned casinos. We build on this work by examining whether casinos are associated with obesity-related health in utero.MethodsWe examined whether casino openings or expansion (as proxy for increased economic resources) are associated with a decreased likelihood of infants being born large-for-gestational-age (LGA), an important risk factor for childhood overweight/obesity. We used repeated cross-sectional data from California birth records (1987-2011) to assess the prevalence of LGA births among Native Americans (n = 21 011). Using zip code fixed-effect regression models, we compared how prevalence of LGA births changed in association with casino openings or expansions, while controlling for secular trends through the inclusion of a comparison group of Native American newborns in zip codes that were eligible to open or expand casinos, but did not do so. In sensitivity analyses, we evaluated whether there was any change in small-for-gestational-age births (SGA).ResultsAverage prevalence of LGA births over the period was 11%. Every one slot machine per capita increase was associated with a 0.13 percentage point decrease (95% confidence interval: -0.25, -0.01) in the prevalence of LGA births but was not associated with SGA prevalence.ConclusionsCasino expansion in California is associated with a lower prevalence of LGA births. Interpreted in combination with previous work showing that California casino expansions were associated with a lower body mass index (BMI) among schoolchildren, these results suggest that casinos are associated with improvement in a surrogate marker of excess adiposity. Further studies are needed to elucidate the mechanisms by which casinos might be associated with obesity-related health outcomes among Native Americans.