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The significant role of universities in generating and diffusing new knowledge in the interest of society has positioned faculty as knowledge creators. The present study contributes to the domain of positive psychology by heeding the happiness call in academia. The research intends to develop and validate a scale for measuring happiness at work (HAW) for knowledge creators.

The study is systematically designed across a series of four independent studies: (1) Dimensionality and item analysis, (2) scale purification, (3) scale refinement and nomological validation and (4) generalizability. Additionally, common method bias (CMB) was checked utilizing the marker variable technique.

HAW has been established as a second-order reflective-reflective construct with six factors, namely work satisfaction, self-directedness, self-love, positive thinking, positive social relationships and work-family balance. The nomological validity and generalizability of the scale have also been established.

The study is an attempt to address an important topic of HAW among knowledge creators. By conceptualizing HAW as a combination of intraindividual and organizational factors, this study offers a comprehensive measure of HAW that was previously absent in the literature. The results of the study will assist management in making strategic decisions to ensure the HAW of knowledge creators.

Knowledge creators’ happiness is a major concern in academia and has received little attention till date. The primary contribution of this study is the conceptualization and development of a validated scale for measuring knowledge creators’ HAW. A valid and reliable scale for measuring HAW would enable researchers to gain fresh perspectives on the essence, attributes and quantification of this particularly noteworthy construct.

This study sought to populate a concise set of guidelines for use of the case study research methodology and evaluate the adequacy with which the methodology has been used previously within both the positivist and interpretivist domains of architectural research.

Firstly, the study set out to establish, from the literature, the nature of the case study methodology. Thereafter, a set of guidelines for adequate use of the methodology was populated. The study, then, proceeded to examine selected architectural research papers that had used the case study methodology in order to evaluate the adequacy with which they had used the methodology. The research papers were randomly drawn from two major architectural research journals publishing scholarly work within the positivist and interpretivist paradigms.

Within the interpretivist domain of architectural research, the study found that there might exist some inadequacy in the way in which the case study methodology has been used.

Despite the popularity of the case study methodology, there have only been limited scholarly attempts at developing a set of guidelines with which to evaluate the adequacy of its usage, particularly within the context of architectural research. This lack of a concise set of guidelines might lead to inappropriate usage of the methodology. This study sought to undertake a review of the usage of the methodology within architectural research with two objectives, namely, populating a concise set of guidelines for use of the methodology and evaluating the adequacy with which the methodology has been used previously.

The purpose of this study is to examine the significance of incorporating diverse lived experiences and identities through culturally relevant pedagogy (CRP) in promoting sustainability, cultural diversity and the integration of various perspectives and worldviews rooted in identity and lived experience in sustainability education at Community College of Allegheny County (CCAC).

A case study approach using Geneva Gay’s (2002) CRP framework was used to investigate the role of CRP in sustainability education at CCAC. Thematic analysis was used to analyze the data collected from eight interviews and 67 survey respondents, who participated in a larger exploratory study focusing on education-related sustainable development goals objectives in technical and vocational education and trainings.

The findings indicate that implementing CRP in sustainability education fosters a culturally diverse learning environment, leading to enhanced cultural competence, critical thinking, global citizenship and academic achievement. Best practices and potential benefits of integrating diverse lived experiences and identities using CRP are also identified.

The small sample size may limit the generalizability of the findings. To address this limitation, future studies could use larger and more diverse samples to confirm the findings of this study. Additionally, the study was conducted at a single institution, which may limit the transferability of the findings to other institutions. Future studies could replicate this study at different institutions to determine the generalizability of the findings. Another limitation is the reliance on self-reported data, which may be subject to social desirability bias. To address this limitation, future studies could use a mixed-methods approach that includes both qualitative and quantitative data sources.

This study highlights the need for community colleges to prioritize diverse faculty and staff recruitment, provide CRP and diversity training and establish partnerships with community organizations. By implementing these recommendations, institutions can enhance sustainability education, promote cultural competence and foster critical thinking among students, ultimately contributing to a more inclusive and equitable learning environment.

The integration of diverse lived experiences and identities using CRP in sustainability education can lead to a more culturally diverse and socially inclusive society. By fostering cultural competence, global citizenship and critical thinking in students, community colleges can empower them to address global challenges and contribute to achieving a sustainable and equitable future for all.

This paper contributes to the understanding of the role of CRP in promoting sustainability and cultural diversity in CCAC and education. It highlights the significance of including diverse perspectives and worldviews rooted in identity and lived experience in sustainability education, offering practical recommendations for integrating CRP in educational institutions.

Although humanoid robots are increasingly adopted in many business settings, the dynamic effects of anthropomorphism and the functional perceptions of service robots on consumers’ responses remain unclear. This paper aims to examine the impacts of robot anthropomorphism on consumers’ trust, receptivity and the downstream effect on satisfaction. Furthermore, it examines the mediating effects of perceived ease of use (PEOU) and perceived usefulness (PU) in the relationship between anthropomorphism and consumer responses.

After conducting two separate pilot studies to help design the research materials, this research involves three sequential studies. In studies 1A and 1B, the authors used two distinct humanoid robots (i.e. Connie and Pepper) to test the direct effects of anthropomorphism on trust and receptivity and the mediated effects via PEOU and PU. Study 2 conducted a 2 (robot appearance: machine-like vs. human-like) × 2 (task complexity: low vs. high) between-subjects experimental design to further explore the boundary effects of task complexity on trust and customer satisfaction.

This research theorizes and empirically examines the mediating effects of PEOU and PU in the relationship between anthropomorphism and consumers’ responses (i.e. trust and receptivity) to service robots. Results also demonstrate a moderating role of task complexity, whereby only when the task was complex did anthropomorphism affect consumer responses and customer satisfaction. The parallel mediations of PEOU and PU were also confirmed. However, when task complexity was low, the authors observed no differences between human- and machine-like robots.

First, this research used a scenario-based method by exposing participants to different pictures or videos of service robots and measuring individuals’ responses. Consumers may respond differently upon interacting with robots in actual service contexts. Second, future research could investigate the effects of other aspects of anthropomorphism, such as robots’ voice characteristics (gender, high/low pitch), verbal communication styles and emotional expression. Finally, future research could explore other service contexts to test the generalizability of the findings.

Findings of this study also provide useful insight for companies interested in adopting service robots. First, the authors unearthed several positive outcomes of using human-like versus machine-like robots in service settings. Despite concerns about the perceived creepiness and discomfort associated with human-like robots, managers should not worry about these service agents’ potential negative effects. Second, it shows that human-like robots’ competitive advantage over machine-like robots stands out when task complexity is high. Managers should therefore carefully consider relevant service characteristics and task requirements when deciding whether to adopt robots.

This study provides original and valuable contributions to the growing literature on service robots by addressing scholarly incongruencies regarding the impact of anthropomorphism and disentangling its positive influence on consumers’ perceptions and acceptance of service robots. This study also contributes to research on technology acceptance and service robot receptivity by empirically demonstrating the mediating role of PEOU and PU. Furthermore, this research enriches the body of knowledge on task-technology fit by providing evidence that task complexity is a crucial factor to consider in service robot design.

Industry 4.0 technologies have the potential to improve the quality management performance of industrial companies. The paper analyses the influence of Industry 4.0 technologies on quality management aspects, but also the barriers that slow down the deployment of each Industry 4.0 technology and limit each impact.

The impact of Industry 4.0 technologies on quality management aspects (QMAs) is a heterogeneous and multidimensional phenomenon dependent on the current context, a holistic multiple case study has been applied. Twenty-six case studies were carried out on eight Industry 4.0 technologies, with a minimum of two cases per technology. These cases were selected from the 168 projects presented in the four editions of the BIND 4.0 program, winner of the 14th edition of the European Enterprise Promotion Awards. The cases were selected based on a preliminary survey of 124 project managers. Subsequently, individual case and cross-case analyses for each technology were carried out. Finally, these results were confirmed by interviews with a minimum of two customers per Industry 4.0.

Results show that the adoption of Industry 4.0 technologies positively affects QMAs. Specifically, the influences received by “process control” and “customer satisfaction” from all the Industry 4.0 technologies studied are medium to high. In addition, barriers from the “economic and legal” and “workers” categories exert greater influence than the barriers pertaining to “organization”, “lack of training and information” and “technology”.

The main limitation is the generalizability of the findings of qualitative studies (ergo the case study). In this sense, statistical generalizability, characteristic of a random sample, is not intended in this paper. Therefore, the use of multiple case studies has been chosen to reinforce analytical generalizations with corroborated evidence (literal replication).

Managers interested in adopting Industry 4.0 technologies Ts should plan the implementation process to minimize the impact of these barriers and optimize the results for each stakeholder. In this sense, the barriers that concern the workers should be managed. It is the responsibility of managers to inform and explain how data will be handled, and how privacy concerns will be addressed.

It is essential to explain and convince workers about the need for a renewal of tasks. New types of jobs (i.e. the use of robots) will involve training for workers to enable their integration alongside the new technologies.

This paper addresses two under-researched areas that are essential when defining strategies in the industrial business context. Firstly, the paper analyses the influence of each I40 T on each QMA. Secondly, it analyses the barriers to adopt that slow down the rollout of each I40 T and limits each impact.

The purpose of this paper is to examine the purchase behavior of consumers towards green products by adapting and extending the theory of planned behavior with the inclusion of three pertinent environmental factors posited to reflect environmental consciousness in the form of environmental concern, environmental knowledge and environmental values.

The data was collected from 410 consumers at shopping malls with retail stores selling green and non-green products in a developing country using cluster sampling and analyzed using covariance-based structural equation modeling.

The findings of this study indicate that environmental factors reflecting environmental consciousness positively influence consumers’ attitude towards purchasing green products, wherein consumers’ environmental values have a stronger influence than their environmental concern and environmental knowledge. The findings also reveal that subjective norm, attitude and perceived behavioral control toward purchasing green products positively shape green purchase intention. The same positive effect is also witnessed between green purchase intention and behavior. However, perceived behavioral control towards purchasing green products had no significant influence on green purchase behavior.

This study suggests that green marketers should promote environmental consciousness among consumers to influence and shape their planned behavior towards green purchases. This could be done by prioritizing efforts and investments in inculcating environmental values, followed by enhancing environmental knowledge and finally inducing environmental concern among consumers. Green marketers can also leverage subjective norm and perceptions of behavioral control toward purchasing green products to reinforce green purchase intention, which, in turn, strengthens green purchase behavior. This green marketing strategy should also be useful to address the intention–behavior gap as seen through the null effect of perceived behavioral control on purchase behavior toward green products when this strategy is present.

This study contributes to theoretical generalizability by reaffirming the continued relevance of the theory of planned behavior in settings concerning the environment (e.g. green purchases), and theoretical extension by augmenting environmental concern, environmental knowledge and environmental values with the theory of planned behavior, resulting in an environmentally conscious theory of planned behavior. The latter is significant and noteworthy, as this study broadens the conceptualization and operationalization of environmental consciousness from a unidimensional to a multidimensional construct.

This study seeks to explore the impact of “fear of missing out” (FOMO) and “psychological enhancement” (PE) on addiction to social media applications, subsequently influencing users' life satisfaction and continuous usage intention.

This research involved the administration of two sets of questionnaires during distinct periods: December 15 to December 30, 2022 and August 26 to September 2, 2023. The participants were college students from three universities in China, and the data collection utilized the “Questionnaire Star” platform. Only responses deemed valid and consistent were included in the subsequent statistical analysis. A total of 1,108 valid samples were used for the final analysis. Analyses including reliability, validity, path analysis, structural equation modeling, mediation effects and moderation effects were conducted using SPSS and AMOS software.

The study revealed that both FOMO and PE exerted positive influences on users' addiction to social media applications. Furthermore, this addiction was found to have a negative effect on users' life satisfaction while simultaneously contributing positively to their intention to continue using these platforms. The mediating effect of social media application addiction and the moderating impact of self-regulation were also substantiated through the analysis.

Firstly, it is important to note that the research population of this study is limited to college students, which may limit its generalizability and representativeness. Although college students are a group known for their familiarity with and frequent use of smartphones and social media apps, the findings may not fully capture the behaviors of social media app users in other age groups. To enhance the understanding of social media app addiction across different age groups, future studies should consider expanding the research population and conducting multi-group difference analyses. Secondly, while focusing on specific users within a particular region can minimize unexplained variance in model estimation, it may also restrict the broader applicability of the study results. Therefore, future studies should consider testing the research model with diverse groups from different regions and cultural backgrounds. This approach will provide valuable insights into how social media app addiction may vary across various contexts, thereby enriching our understanding of this phenomenon.

Our findings reveal that in the “attention economy” environment shaped by addiction, social media app managers should leverage technology to swiftly and accurately target audiences, attract them to their platforms and cultivate long-term relationships. Encouraging users to develop new beneficial habits through app-specific functions and precise services will foster continuous usage and unlock revenue and marketing opportunities for app companies.

Despite the extensive scholarly discourse on social media application addiction, there is a lack of a well-defined framework delineating how addictive user behaviors can be leveraged in the marketing strategies of social media application platforms. The present study seeks to address these gaps, contributing to a better understanding of the formation mechanisms and knowledge systems related to social media application addiction. By investigating the causes and consequences of such addiction, this research offers valuable insights and recommendations for the innovative development of these apps, given their widespread popularity. Concurrently, the study establishes a theoretical basis for the concept that users can mitigate the negative effects of social media addiction by exercising their own self-regulation.

As the functionalities and features of social media apps converge, their individual uniqueness starts to diminish, intensifying the competition among social media companies. This escalating rivalry places higher demands on these companies. This study aims to aid social media app companies in comprehending and analyzing the diverse psychological needs of users. By enriching their platform features and services, leading users towards addiction and gaining an edge in the “Attention Economy” competition. Understanding and catering to users' needs will be instrumental in thriving within this dynamic and evolving attention economy landscape.

The purpose of this paper is to determine the relationship between trait and situational empathy, and the effect of educational activities on empathy of nursing students towards people living with dementia.

This embedded mixed-methods study compared trait and situational empathy examined situational empathy pre/post didactic and experiential activities with nursing students and used qualitative data from focus group discussions to corroborate the quantitative data.

There was no significant difference between trait and situational empathy. Post intervention scores (situational) demonstrated improvements on empathic concern, shared affect, empathic imagination, helping motivation and cognitive empathy. Focus group discussions supported quantitative findings and also included distress.

Limitations include lack of generalizability, single group threats and exclusion of stakeholder input. Single group threats include absence of a control group, familiarity with the CSES from pre-test to post-test and reactive measurements, as the students were observed by faculty while completing the Dementia Live activity. The perspective of stakeholders would strengthen the impact of the results on implementation.

Information gleaned from this study can help inform administrators in education and in practice. CliftonStrengths assessment and Dementia Live simulation activities can be used for administrators, faculty and students in schools of nursing as well as administrators and health-care workers.

Information from this study can impact those living with dementia as well as their caregivers.

Most studies involving health-care students and empathy do not delineate between trait and situational empathy. This study is unique in that it measured both and sought a relationship between the two. Determining one's personal attributes such as trait empathy, can help students capitalize on their strengths and ultimately enhance patient care.

This study develops and validates a theoretical model of real-time mobile connectivity, examining how employees' perceptions of their relationship with supervisors influence their emotional experiences. Through quasi-experiments, the authors investigate the behavioral patterns and emotional responses associated with real-time mobile connectivity in organizations, with a focus on messaging apps that indicate message read status. Specifically, they explore how supervisors' attentiveness or inattentiveness in mobile connectivity impacts emotional ambivalence (anxiety and pride) among subordinates. Additionally, they examine the downstream effects of this emotional ambivalence on employees' workplace thriving and job performance across various dimensions.

To address the paradox of real-time mobile connectivity, a quasi-experimental design involving 320 team members from 46 teams was implemented. Multi-level structural equation modeling was employed to analyze within-person variance and evaluate the proposed hypotheses.

The findings indicate that employees who do not receive timely indications from their supervisors are more likely to experience elevated levels of anxiety, while those who receive prompt indications experience a sense of pride. Moreover, the indirect effects of the real-time mobile connectivity paradox on employee performance, mediated by anxiety (negatively) and pride (positively), are fully explained through workplace thriving.

This study provides insights into the emotional ambivalence experienced in the workplace due to real-time mobile connectivity, highlighting its implications for organizational competitiveness. Integrating resource conservation theory and cognitive appraisal theory of emotion, the study explores the mediating role of workplace thriving and the impact on employee performance through pride and anxiety. Generalizability requires considering organizational settings and cultural contexts while acknowledging limitations such as a focus on messaging apps and specific samples. Future research should explore these dynamics in diverse contexts and identify additional factors influencing the relationship between real-time mobile connectivity and employee outcomes.

This study provides valuable insights for managers regarding the significance of message indications, as their attentiveness can elicit emotional reactions from employees that subsequently impact workplace thriving and job performance.

This study pioneers the exploration of the paradox of real-time mobile connectivity in the workplace, uncovering the discrete emotions experienced by employees. Furthermore, it elucidates the subsequent opposing effects on workplace thriving and job performance, contributing to the existing literature and knowledge in this area.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.