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Recently, a shift regarding the negative consequences of organizational citizenship behaviors (OCBs) has put them forward as employees’ negative aspects carrying dangerous consequences for organizations. Considering this issue’s seriousness, the purpose of this paper is to examine the process through which compulsory citizenship behavior fosters citizenship fatigue.

A total of 370 nurses working in Pakistani public-sector hospitals were this study’s final data set sample using SmartPLS4.

Partial least squares structural equation modeling (PLS-SEM) results revealed that when employees are compelled to engage in extra-role actions, they frequently experience work-life conflict, which results in citizenship fatigue. The higher the employee’s age and the lower the education level, the lower his/her citizenship fatigue. On the other hand, findings revealed that workaholic personality aspects tend to reduce the strength of the relationship between work-life conflict and citizenship fatigue.

To the best of the authors’ knowledge, this paper is the first attempt to examine the recently emerged concept of citizenship fatigue among health-care professionals through conservation of resources theory. Besides, this research will highlight how the demand for voluntary actions in routine or forced citizenship behavior can become the reason for work–family conflict and ultimately create citizenship fatigue. Additionally, this paper presents the novel concept of workaholic personality and how it can play a positive role in the linkage between work–family conflict and citizenship fatigue.

This paper aims to introduce a growth comprehensive pattern to explain the phenomenon of individual foreign investment, first at the global level and then at the regional level. The patterns are developed based on a number of main theories with grounded theory (GT) as the foundation, distributed on the two pull and push forces of international business theory and migration theory; simultaneously, it is classified on the three levels (attribute–consequence–value [ACV]) of means-end theory.

An embedded method is applied to generate two complementary datasets from two approaches: in-depth interviews and secondary data analysis.

In this structure, the investor plays a central role as the decision-maker based on the entrepreneur's motives for internationalization (economics-driven and psychology-driven factors) and the householders' motives for emigration (aspiration and access capabilities). The external forces considered are a push from the home country (structures) and pull from the host country (immigrant investment programs [IIPs]), in which the factor of (dis)trust/misconception as a moderator has an additional impact on this mobility. Demographic factors such as gender, region, generation/age, level of education, religion and occupation generally describe the characteristics of each specific target group.

This paper is to develop a conceptual framework.

The results of this study, in addition to fulfilling its own objectives, will also serve as the foundation for further research in several scientific fields such as economics, sociology and politics.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-12-2022-0786

Although there is a growing body of work on immigrants' information behavior, little is known about the pre-arrival information experiences of immigrants who consult formal information sources such as immigration agents. Drawn from a larger study on the information behavior of immigrants, this paper mainly reports the semi-structured interview findings on the pre-arrival information experiences of Bangladeshi immigrants who used formal information sources with discussion on how that affected their post-arrival settlement into Canada.

The study used a mixed method approach with semi-structured interviews (n = 60) and surveys (n = 205) with participants who arrived in Canada between the years of 1971 and 2017. Data were collected from May 2017 to February 2018.

Although the overall scope of the original study is much larger, this paper features findings on the pre-arrival information experiences derived mainly from an analysis of interview data. This study provides insights into the pre-arrival information experiences of Bangladeshi immigrants consulting formal information sources such as immigration firms, individual immigration consultants and more formal government agencies. The author introduces a new concept of “information crafting” by exploring the negative consequences of selective information sharing by immigration consultants/agents in newcomers' settlements in Canada, primarily positive information about life in Canada, sometimes with exaggeration and falsification. The interview participants shared story after the story of the settlement challenges they faced after arriving in Canada and how the expectations they built through the information received from immigration consultants and government agencies did not match after arrival. This study emphasizes the importance of providing comprehensive information about life in Canada to potential newcomers so that they can make informed decisions even before they apply.

The findings of this study have theoretical and practical implications for policy and research. This study provides insights into the complicated culturally situated pre-arrival information experiences of Bangladeshi immigrants. Moreover, the study findings encourage researchers in various disciplines, including psychology, migration studies and geography, to delve more deeply into newcomers' information experiences using an informational lens to examine the information newcomers receive from diverse sources and their effects on their post-arrival settlement in a new country. The study challenges the general assumptions that formal information sources are always reputable, useful, and comprehensive, and it provides some future directions for research that seeks to understand the culturally situated information behavior of diverse immigrant groups.

The purpose of this study is to generate knowledge about assessment methods in blended business education, which have become increasingly important to establish sustainable assessment practices that support knowledge acquisition for undergraduate students in business administration at a Colombian university.

For the analysis, a two groups comparison was performed using a nonequivalent control group design with a sample of 420 students. As this study wants to find insights to improve the knowledge on assessment topics in marketing research (MR) education, it was focused on the students from the business administration program. This study also uses individual scores from the state test as prior cognitive scores and the high school classification provided by the National Ministry of Education in Colombia (2012).

It was found that the variables that best predict performance on the MR course examinations were the mathematics skills and reading comprehension scores on the state test. The study also showed a better performance of female students on both assessment methods. There were no significant differences between the assessment methods or among the high school levels.

One of the limitations of this study is the limited number of items on the tests. Additionally, the authors recommend conducting an analysis of the differences between the testing items to provide a detailed explanation of students’ performance when comparing computer-based testing and paper-and-pencil testing.

Further design of teaching material and assessments online and offline, based on local and regional marketing problems, is suggested. As the current text and readings are more oriented to the English-speaking contexts, most of the problems presented are oriented to multinational companies and brands.

Insights into the skills required for future jobs provide valuable guidance (World Economic Forum, 2020). Essential skills for emerging roles, like data scientists, can find robust support within the MR course. To further enrich in-class and online exercises with Excel and SPSS, Colombian educators can leverage data sets obtained from sources like the national statistics office and international market intelligence databases available through the university’s library, including Passport and Statista. Engaging with authentic data sets provides students with a more profound understanding of practical applications in MR.

This approach facilitates the identification of key variables, such as assessment and cognitive abilities in math and reading, which predict students’ knowledge acquisition in MR. It not only offers insights into the relevant factors influencing learning in MR but also provides valuable feedback. Additionally, it suggests potential avenues for future research in this field.

Relying on the principles of the social exchange theory, the current study is aimed at investigating the impact of team-member exchange relationships (TMX) among school management team (SMT) members on school outcomes (organizational citizenship behavior [OCB], job satisfaction and innovation) via the mediating role of leader-member exchange (LMX) relationships between principals and SMTs.

Data were collected from multiple sources in 86 elementary and junior high schools to avoid one-source bias: 86 principals, 357 SMT members and 683 schoolteachers who were not members of the management teams.

The results revealed a positive relationship between TMX and teachers' job satisfaction and OCB, but no significant link between TMX and innovation. LMX partially mediated the relationship between TMX and OCB and between TMX and teachers' job satisfaction. Full mediation was found in TMX-innovation relationship.

The findings carry a message for school principals and policymakers regarding the importance of developing and maintaining high-quality horizontal and vertical exchange relationships among the SMT members for their positive influence on school outcomes.

To the best of the authors' knowledge, this is the first study to examine the link between TMX and LMX as a team phenomenon, and specifically in the educational setting. The finding that there is a positive link between the two constructs may imply that SMTs contribute to school success not only directly by exhibiting high-quality TMX but also indirectly through the high-quality LMX.

This study aims at understanding how higher education institutions (HEIs) can contribute to sustainable development, by designing their programmes for bringing about a transformative impact on communities and students, and also to examine what alternative pedagogical approaches could be used for that. In the past decades, HEIs have increasingly created social innovation (SI) programmes, as a way to achieve United Nations Sustainable Development Goals. These community-oriented and field-based programmes are difficult to ally with conventional classroom education. This study explores how these programmes could integrate the participatory approach and what would be the benefits. It also investigates the effectiveness of the experiential learning approach for teaching sustainability.

A case study method is used to document SI projects initiated by an HEI programme in rural India.

It was found that the participatory approach contributes to empowering communities and also benefits the students in terms of academic, professional and personal growth. Empirical findings show that experiential learning is an efficient method to teach sustainability. Ultimately, both pedagogical approaches are found to be mutually beneficial.

This study fills a gap in the literature, by providing empirical evidence on how HEI can implement innovative educational strategies such as participatory approach and experiential learning in their programmes towards teaching sustainability. A conceptual model for HEI interested in developing similar programmes is also proposed. To the best of the authors’ knowledge, this study is one of the first studies focusing on the context of Indian HEI.

The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens.

This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data.

Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness.

Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.

The global landscape of higher education has witnessed a steady increase in the mobility of international students, as more individuals seek diverse academic experiences and cross-cultural learning opportunities. This paper conducts a systematic literature review to investigate trends, research directions and key themes in the literature. By utilising the push–pull factor model the aim is to provide a comprehensive understanding of the factors influencing international students' choices to pursue higher education and future career opportunities abroad.

A systematic literature review approach was applied. The selection was made using PRISMA framework-based inclusion and exclusion criteria. The review includes 43 publications from 2010 to 2022.

The review results revealed five themes of scholarly conversations labelled as (1) betterment of life, (2) policy of the host country, (3) role of institutions, (4) return to home country and (5) social, economic, environmental, individual and cultural factors. The findings emphasise on the significance of factors such as the quality of education, visa requirements, academic reputation, tuition fees, availability of scholarships, job opportunities, social, economic, environmental, individual and cultural factors. The paper also identifies language barriers, visa policies and social integration difficulties as major barriers to international students' stay in the host country after graduation.

This research enhances the current body of literature by conducting a comprehensive analysis of the empirical evidence available in literature that investigates the mobility of international students. The outcomes of this study will make a valuable contribution towards developing a more profound comprehension of the primary factors that influence international students' decision to pursue their education abroad.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

Recent studies have observed rise in consumer’s ethical concerns about the online retailers while making a purchase decision. The impetus for businesses to use corporate social responsibility (CSR) is evident, but the effects of CSR motives on corresponding processes underlying cause-related marketing (CRM) patronage intention have not been thoroughly examined. This study, anchored on attribution theory, established a research model that better explains the influence of CSR motives on patronage intentions toward CRM-oriented online retailers. Additionally, this study aims to examine the moderating role of spirituality (SPT) on CSR motives and CRM patronage intention (CPI).

Primary data has been collected from 722 respondents and analyzed by using deep neural-network architecture by using the innovative PLS-SEM-ANN method to predict/rank the factors impacting CPI.

The results revealed the normalized importance of the predictors of CPI and found that value-driven motive was the strongest predictor, followed by strategic motive, SPT, age and stakeholder-driven motive. In contrast, egoistic motive, education and income were found insignificant.

The pandemic has transformed the way consumers shop and fortified the online economy, thereby resulting in a paradigm shift toward usage of e-commerce platforms. The results offer valuable insights to online retailers and practitioners for predicting patronage intentions by CSR motives and, thus, effectively engage CRM consumers by designing promotions in a way that would deeply resonate with them. This study assessed and predicted the factors influencing the CPI s, thereby guiding the online retailers to design CSR strategies and manage crucial CRM decisions.