Search results

This study investigates user preferences for motivational features aligned with self-determination theory (SDT), emphasizing autonomy, relatedness, and competency. The study seeks to identify the most appealing and effective motivational features in AI-driven mobile apps for fostering autonomy, promoting relatedness, and enhancing feelings of competency in the context of reading comprehension.

Employing a mixed-methods research design based on the self-determination theory, the study used triangulation of methods to enhance the research findings' quality, completeness, validity, and understanding.

Both qualitative and quantitative findings showed similarities in the emphasis on personalization, progress tracking, social interaction, gamification, differences in specific feature preferences, and nuanced recommendations, highlighting the importance of a comprehensive and user-centric development approach.

The study design and sample size might constrain the statistical power and generalizability of the results. The study used a convenience sample of students from one school.

The synthesis of findings suggests the need for a comprehensive development strategy that incorporates both the commonalities and nuanced recommendations from qualitative and quantitative data. Combining the strengths of both findings, a holistic user-centric approach that integrates personalization, progress tracking, social interaction, and gamification is crucial for effective app development. Recognizing that individual preferences may vary, developers should consider tailoring features to accommodate a range of user needs, as indicated by qualitative and quantitative insights.

This comprehensive approach contributes to advancing knowledge in AI-driven mobile applications and their impact on student motivation and reading comprehension in the Philippine context. Also, it provides valuable insights for designers and developers, contributing to creating engaging and user-friendly platforms that enhance reading comprehension skills.

Our study aims to understand what is known about happiness at work (HAW) in terms of publication, citations, dimensions and characteristics, as well as how knowledge about HAW is generated regarding theoretical frameworks, context and methods. Additionally, it explores future directions for HAW research.

This paper conducts a systematic literature review of 56 empirical articles published between 2000 and 2022 to comprehensively explore HAW. It examines publication trends, citation patterns, dimensions, characteristics, theoretical frameworks, contextual factors and research methodologies employed in HAW studies.

Our findings suggest that while HAW research has gained momentum, there is still a need for exploration, particularly in developing countries. Various theoretical frameworks such as the job demand-resources model, social exchange theory and broaden-and-build theory are identified, with suggestions for the adoption of less popular theories like the positive emotion, engagement, relationships, meaning and accomplishment (PERMA) model and flow theory for future investigations. The review contributes to workplace happiness literature by offering a comprehensive analysis spanning two decades and provides valuable insights for guiding future research toward exploring factors influencing employee well-being.

Our article offers a structured analysis of HAW literature, emphasizing the necessity for more extensive research, especially in developing nations. It provides valuable insights into the theories and dimensions associated with HAW, guiding future research and assisting organizations in formulating strategies to enhance employee happiness and overall well-being.

Despite the growing interest in blockchain technology (BCT) applications in the agri-food industry, evidence of their economic and strategic implications remains scarce. This study aims to contribute to filling this gap by jointly investigating how BCT adoption affects transactional relationships, and how it contributes to the firm’s strategic resources.

An explanatory case study is conducted based on a theoretical framework grounded on transaction cost economics and the resource-based-dynamic capabilities view. Six BCT implementations by agri-food firms are studied. Data were collected through semi-structured interviews and analysed using thematic analysis.

Findings reveal that BCT benefits depend on how companies integrate technology across their supply chains. In fact, the results suggest that overall transaction efficiency within the supply chain is enhanced only for those firms prioritising stakeholder engagement during technology implementation and leveraging existing trust relationships with economic agents. Moreover, the results suggest that BCT is not yet perceived as a strategic resource, but rather that it has the potential to enhance firms’ operational-adaptive, absorptive and innovative capabilities. When all supply chain actors clearly understand blockchain’s functionality and value, the development of these capabilities becomes more pronounced.

The study identifies two BCT adoption configurations. One primarily focuses on enhancing supply chain efficiency and transparency (dynamic BCT), while the other uses BCT mainly for marketing purposes (static BCT). These configurations lead to varied possibilities for leveraging BCT’s potential advantages. Furthermore, they show how a mismatch between a strategic approach and its chosen configuration could work against any positive impact and lead to disillusionment with the BCT. Thus, managers should assess carefully the impact of such different configuration choices on performance.

To the best of the authors’ knowledge, this is the first study to attempt to analyse the economic implications of adopting BCT in the food sector from both a firm and supply chain perspective. Additionally, it shows how interpreting these impacts is contingent on the diverse modalities for embedding BCT into existing supply chains.

The study aims to promote the use of qualitative methods in service research by investigating how these methods are reported in service journals, how the level of reporting has evolved and whether methodological reporting influences the downloads or citations received by qualitative articles.

Methodological reporting practices were identified through content analysis of 318 qualitative articles published in three major service research journals and comparison with prior methodological literature. Regression analysis was used to test how the level of methodological reporting influences article downloads and citations.

The study identifies 29 reporting practices related to 9 key methodological reporting areas. The overall level of methodological reporting in published qualitative articles has increased over time. While differences in the level of reporting between service journals persist, they are narrowing. The level of methodological reporting did not influence downloads or citations of qualitative articles.

Service scholars using qualitative methods should pay attention to methodological reporting as it can improve the chances of being published. Factors such as theoretical contributions are likely to have a greater influence on article impact than methodological reporting.

No prior study has explored methodological reporting practices across different qualitative methodologies or how reporting influences article impact. For authors, reviewers and editors, the study provides an inventory of reporting practices relevant for evaluating qualitative articles, which should lower barriers for qualitative methods in service research by providing practical guidelines on what to focus on when reporting and assessing qualitative research.

This study aims to investigate how university students experience a skill transformation process aligned with the sustainable development goals (SDGs). This transformation occurs through their participation in a service-learning programme alongside an international volunteering project. The theoretical framework for understanding this skill transformation process is based on the “rite of passage”.

Qualitative methodology is adopted by conducting 23 online surveys with volunteers (virtual and onsite) and five with coordinators across the rite of passage phases. Volunteering was carried out in five Mayan indigenous communities in Mexico as part of an international cooperation project with the goals of supporting community-based tourism development and strengthening volunteers’ skills in accordance with the SDGs.

Results show that international volunteering programmes for university students significantly enhance their interpersonal and professional skills, demonstrating strong potential for implementing the SDGs. These programmes provide learning and education opportunities for both volunteers and local communities. Volunteers gain a broader perspective on gender equality and cultural barriers. Additionally, volunteering supports sustainable tourism, economic worth and collaboration among institutions. Both volunteers’ personal characteristics (educational level and sociocultural context), as well as their sociocultural context, influenced the perception of the skill transformation process and learning about the SDGs. Finally, a new educational university programme in volunteering aligned with SDGs is proposed.

This research examines the practical ramifications of incorporating volunteer programmes into university courses. Universities must include these initiatives in their educational systems as a means of enhancing student learning.

A new educational university programme in volunteering aligned with SDGs is proposed. This study suggests a shift in university mindset, as well as increased funding for training and adherence to the SDGs.

This study pioneers the rite of passage framework in an international volunteer tourism project facilitated by universities, emphasizing volunteering as a valuable tool for SDG implementation, considering the interrelationships between objectives.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

Due to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects.

This study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process.

This study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming.

APMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste.

This paper aims to explore the relationship between board composition and a firm’s commitment to combatting climate change. Specifically, this study investigates how various characteristics of the board, namely its size and presence of independent directors, and of the directors themselves, including gender diversity, age, educational background and national homogeneity, affect the corporate-level climate change orientation. From a theoretical standpoint, the authors take a cross-fertilizing perspective, bridging upper echelons theory with agency, resource dependence and critical mass theories.

The study uses ordered probit regression models on a hand-collected multi-country and multi-industry sample of 35 listed firms included in the Global Climate Change Liquid Equity Index (GALPLACC) provided by ECPI. This index is particularly relevant as it focuses on firms that have demonstrated a commitment to climate change, providing a robust dataset for the analysis.

The findings underscore the importance of disentangling various characteristics of corporate boards and directors. Specifically, the orientation toward climate change is negatively influenced by both board size and having a higher number of independent directors, while it is positively affected by reaching a critical mass of women on the board. Conversely, factors such as average age, educational background and the level of national homogeneity do not show significant effects.

This paper has an exploratory nature and contributes to the ongoing debate on the crucial, yet controversial role played by board-level and directors’ sociodemographic characteristics in shaping a firm’s environmental stance. Moreover, this study offers potential recommendations for policymakers regarding board composition to enhance firms’ climate change orientation.

The present research conducts a thorough review of published literature relevant to earnings management (EM) practices in family firms (FFs), utilizing the Scopus database, intending to identify potential directions for future research.

Through a systematic review, this study focuses on identifying and summarizing trends in publications over the years, the journal outlets, geographical contexts, research methodologies, the temporal evolution of theories and the specific constructs under investigation.

Earlier empirical studies suggest that corporate governance enhances integrity and transparency in FFs, thereby reducing EM practices. Contrarily, compliance with International Financial Reporting Standards (IFRS) seems to offer managers more opportunities for convenient EM rather than restricting such practices. Notably, corporate social responsibility (CSR) practices do not appear to mitigate EM practices consistently. The literature, however, reveals inclusive results and areas requiring deeper exploration for more definitive results. For instance, certain corporate governance mechanisms, such as family-specific social and cultural business characteristics, subjective measures of family businesses, behavioral approaches to family owners' decision-making and directors' personal, psychological and social factors, remain largely untested. Additionally, there is a notable research gap concerning the relationship between IFRS, capital structure and EM.

This study’s contributions lie in its comprehensive literature review, identification of research trends and gaps, and its potential to guide future research endeavors in the domain of EM practices in FFs.