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This paper aims to explore work interfering with family (WIF) and family interfering with work (FIW) as the mediators linking psychological resilience to happiness.

Data gathered from tour guides in Türkiye were used to test the aforesaid associations. In this paper, partial least squares structural equation modeling was performed to gauge the direct links and mediating impacts of WIF and FIW simultaneously in the link between psychological resilience and happiness.

Psychological resilience exerts a dual influence on WIF and FIW. Simply put, psychologically resilient tour guides balance their work demands with those of the family or vice versa and exhibit low levels of WIF and FIW. As hypothesized, WIF and FIW are the two mediating mechanisms relating psychological resilience to happiness.

It is important to create a family-supportive atmosphere where tour guides can avail themselves of family-friendly practices (i.e. family leave, flexible work schedules, breaks to be spent with family members after long tours and convenient working hours). Psychologically resilient tour guides, with the abovementioned practices, would experience alleviated conflicts between work and family roles and would therefore have heightened happiness.

Although the extant literature has presented plenty of empirical studies about the predictors and outcomes of WIF and FIW, evidence about the links of personal resources as third variables to WIF and FIW simultaneously is still scanty. In addition, there is no evidence linking personal resources such as psychological resilience to nonwork outcomes such as happiness through the mediating roles of WIF and FIW. This paper fills in these voids by gauging the links given above using data collected from tour guides, a population that has been subjected to limited inquiry in the WIF and FIW literature.

This study employed technological frames of reference (TFR) theory to explore officer attitudes toward body-worn cameras (BWCs) in the Chicago Police Department (CPD), identifying frames that may undermine compliance.

Semi-structured interviews were conducted with 11 officers, focusing on their perceptions of the BWCs’ purpose, adoption catalysts, effectiveness metrics and benefits and drawbacks.

Officers viewed BWCs primarily as tools for oversight and cited external influences and the department’s desire to be perceived as progressive as key catalysts for BWC adoption. There was widespread uncertainty regarding the criteria CPD uses to gauge BWC effectiveness. The protective feature of the cameras was cited as the primary benefit of the technology, while privacy intrusion and discretion were identified as key drawbacks. Noteworthy nuances were observed across these perceptual domains.

The study focuses on a single police agency, limiting its generalizability. Nevertheless, it holds value for departments experiencing BWC policy compliance issues and those preparing to implement the cameras.

Insights into officers’ technological frames help identify perspectives that threaten desired use of BWCs and highlight necessary training and policy interventions that align officers' BWC readings with departmental goals to enhance policy compliance.

This study is among the few that employ TFR theory to examine officer perceptions of BWCs in a large urban police agency.

Research serves to elucidate and tackle real-world issues (e.g. capitalizing opportunities and solving problems). Critical to research is the concept of validity, which gauges the extent to which research is adequate and appropriate in representing what it intends to measure and test. In this vein, this article aims to present a typology of validity to aid researchers in this endeavor.

Employing a synthesis approach informed by the 3Es of expertise, experience, and exposure, this article maintains a sharp focus on delineating the concept of validity and presenting its typology.

This article emphasizes the importance of validity and explains how and when different types of validity can be established. First and foremost, content validity and face validity are prerequisites assessed before data collection, whereas convergent validity and discriminant validity come into play during the evaluation of the measurement model post-data collection, while nomological validity and predictive validity are crucial in the evaluation of the structural model following the evaluation of the measurement model. Additionally, content, face, convergent and discriminant validity contribute to construct validity as they pertain to concept(s), while nomological and predictive validity contribute to criterion validity as they relate to relationship(s). Last but not least, content and face validity are established by humans, thereby contributing to the assessment of substantive significance, whereas convergent, discriminant, nomological and predictive validity are established by statistics, thereby contributing to the assessment of statistical significance.

This article contributes to a deeper understanding of validity’s multifaceted nature in research, providing a practical guide for its application across various research stages.

This research paper aims to evaluate the sustainability knowledge and background of students, staff and faculty regarding current university sustainability practices and individual behaviors at Central Michigan University (CMU); to compare sustainability background and knowledge based on academic discipline of enrollment or employment; and to assess sustainability awareness and interest of the campus community to guide future sustainability initiatives and resources at CMU.

An electronic cross-sectional survey was used to collect anonymous responses through Qualtrics, and then results were analyzed through SPSS. Analyses were performed based on the academic structures at CMU.

This research has found that students in STEM fields are more inclined to have pro-sustainability attitudes, knowledge and behaviors, compared to those studying the arts and business. Additionally, results indicate that there is a significant difference in knowledge between the students, and the staff and faculty respondents regarding sustainability knowledge and application, with the staff and faculty consistently demonstrating more pro-sustainability knowledge and behavior.

While research has previously been conducted on sustainability attitudes and behaviors, this research is unique because it ties sustainability knowledge to academic discipline. Additionally, it serves to gauge which sustainability programs and topics members of the campus community are most interested in, and which areas they are most willing to support.

The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.

Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.

Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.

To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

This study delves into the impact of an ethical mindset on the efficacy of ethical awareness within the tax profession and aims to ascertain whether the presence of an ethical mindset can account for the discrepancies in the literature and enhance the effectiveness of ethical awareness initiatives.

The research used a tax experiment involving both treatment and control groups. Both groups were presented with a tax-related scenario, with the treatment group subjected to a specific ethical awareness intervention. To gauge the participants’ ethical mindsets, they were divided into strong self-interest and mild self-interest mindset groups based on their Machiavellian scores. The analysis was conducted utilizing ANOVA to scrutinize the results.

The key findings shed light on the fact that while ethical awareness endeavors can enhance the likelihood of individuals making ethical choices in tax decisions, their effectiveness varies significantly depending on the individual’s ethical mindset. Furthermore, results show that gender affected the relationship between ethical mindset and ethical awareness effectiveness, and males with mild self-interest score reacted more to the ethical awareness intervention. Results support that individuals’ ethical mindset, measured as strong self-interest and mild self-interest, is pivotal in determining the effectiveness of ethical awareness efforts.

This study is unique because it evaluates the effect of ethical mindsets to provide a novel way to improve tax ethical awareness initiatives.

New business density (NBD) is the ratio of the number of newly registered liability corporations to the working-age population per year. NBD is critical to assessing a country's business environment. The present work endeavors to discover and gauge the contribution of 28 potential socio-economic enablers of NBD for 2006–2021 across developed and developing economies separately and to make a comparative assessment between those two regions.

Using World Bank data, the study first performs exploratory data analysis (EDA). Then, it deploys a deep learning (DL)-based regression framework by utilizing a deep neural network (DNN) to perform predictive modeling of NBD for developed and developing nations. Subsequently, we use two explainable artificial intelligence (XAI) techniques, Shapley values and a partial dependence plot, to unveil the influence patterns of chosen enablers. Finally, the results from the DL method are validated with the explainable boosting machine (EBM) method.

This research analyzes the role of 28 potential socio-economic enablers of NBD in developed and developing countries. This research finds that the NBD in developed countries is predominantly governed by the contribution of manufacturing and service sectors to GDP. In contrast, the propensity for research and development and ease of doing business control the NBD of developing nations. The research findings also indicate four common enablers – business disclosure, ease of doing business, employment in industry and startup procedures for developed and developing countries.

NBD is directly linked to any nation's economic affairs. Therefore, assessing the NBD enablers is of paramount significance for channelizing capital for new business formation. It will guide investment firms and entrepreneurs in discovering the factors that significantly impact the NBD dynamics across different regions of the globe. Entrepreneurs fraught with inevitable market uncertainties while developing a new idea into a successful new business can momentously benefit from the awareness of crucial NBD enablers, which can serve as a basis for business risk assessment.

DL-based regression framework simultaneously caters to successful predictive modeling and model explanation for practical insights about NBD at the global level. It overcomes the limitations in the present literature that assume the NBD is country- and industry-specific, and factors of the NBD cannot be generalized globally. With DL-based regression and XAI methods, we prove our research hypothesis that NBD can be effectively assessed and compared with the help of global macro-level indicators. This research justifies the robustness of the findings by using the socio-economic data from the renowned data repository of the World Bank and by implementing the DL modeling with validation through the EBM method.

Torque is one of the main loads acting on the aircraft wing, the horizontal tail and the vertical tail. In flight load measurement, due to the significant influence of the bending moment and the shear force on the strain gauge, the accuracy of torque measurement is usually low. Therefore, aircraft torque measurement is difficult. Based on the characteristics of a certain type of horizontal tail, a measurement method for the torque with high accuracy was proposed in this paper.

A new simplified torque measurement method for the all-moving horizontal tail was proposed based on the spiral driver. The feasibility of the method and key points of the tests were analyzed and studied through a virtual load calibration test.

Based on the results of the real load calibration test, the torque load equation with high accuracy was established, and the torque measurement was achieved in load flight tests.

However, the proposed method is based on the structure of the spiral driver. If there is generally no spiral driver at the aircraft wings and vertical tails, then the appropriate torque measurement method needs to be derived according to the specific object.

The research in this paper provides a new idea for the torque measurement of aircraft structures, which can be used for the torque measurement of subsequent aircraft types.

This study aims to investigate whether various types of risks faced by the publicly listed commercial banks of India and Bangladesh are driven by market power and provides comparative insights from both economies.

By using the adjusted Lerner index to gauge bank market power and applying the generalised methods of moments (GMM) regression approach, the research delved into the relationship between bank market power and three distinct facets of risk across a sample of 26 publicly listed commercial banks in India and 22 listed banks in Bangladesh spanning from 2011 to 2022.

The results indicate that for Bangladesh, both “competition fragility” and “competition stability” viewpoints coexist simultaneously across all risk types, supporting a nonlinear relationship between market power and risk. However, in the Indian context, a nonlinear association exists only in the case of credit risk, while the relationship with insolvency risk is linear, substantiating the “competition fragility view”. Apart from market power and bank-specific variables, GDP growth rate has emerged as a prominent driver across all risk categories in both countries.

The filtration of banks is a limitation that might have influenced the outcomes. This study recommends that the Reserve Bank of India encourages further bank consolidation. Along the same line, Bangladesh Bank should closely oversee the growing competitive landscape. Furthermore, the regulators must monitor the elevated levels of non-performing loans to reduce credit risk so as to bolster the stability of their respective banking sectors.

This comparative study is the first attempt to analyse the market power and risk relationship and includes a novel bank-specific variable, i.e. technology, apart from other established variables.

Online retail platform corporate sustainable development (ORPCSD) has garnered significant interest and appeal among consumers. However, no scale has been developed to measure consumer perceptions of ORPCSD. Therefore, this study aimed to delineate the conceptual framework and dimensions underlying these perceptions and construct a reliable and valid measurement tool.

This study employed established qualitative and quantitative methods in two studies. In the first study, the dimensions and measurement items of consumer perceptions of ORPCSD were proposed using the grounded method. In the second study, the measurement scale was refined and validated using exploratory factor analysis, confirmatory factor analysis, and nomological validity examination.

The results indicated that consumer perception of ORPCSD consisted of three dimensions: economic, social, and environmental sustainability. The measurement scale for these dimensions comprised 25 items, demonstrating excellent psychometric properties.

This study contributes original insights by enhancing the current understanding of consumer perceptions of ORPCSD. Additionally, it provides researchers and managers with psychometric metrics to gauge these perceptions and offers actionable strategies for sustainable marketing initiatives.