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This research highlights the ongoing concern about organizational productivity and the lack of focus on designing an optimal model. The authors aim to create a comprehensive model for managing organizational productivity, considering its impact on profitability, customer satisfaction, and employee morale. They use qualitative research methods, including Systematic Literature Review and Interpretive Structural Modeling (ISM).

In this research using the qualitative research method of Systematic Literature Review, 57 variables affecting productivity were identified. These variables were placed in 16 layers by using the ISM method, which were classified analytically in four sections: INPUTS, OUTPUTS, OUTCOMES and IMPACTS. By determining the relationship between the sections, the research model was designed.

The potential model for organizational productivity management provides a comprehensive framework addressing critical factors like technology adoption, employee empowerment, organizational culture, and more. It identifies Linkage, Dependent, and independent variables. The lower layers consist of INPUTS such as Technological Tools, Organizational Values, and more. In the highest layer, impactful variables like Enhanced competitiveness, Improved decision-making, and Improved organizational culture are labeled as IMPACTS. Middle layer variables are categorized as OUTPUTS and OUTCOMES.

In this study, the concept of productivity management was redefined for the first time, and a multi-layered model for productivity management was creatively explicated using the structural equation modeling method.

This study addresses a critical gap in understanding consumer behavior toward indigenous chicken, investigating the interaction between cognitive factors and purchase decisions. The current research offers a valuable contribution to the field of sustainable food marketing by shedding light on these dynamics.

The research employed a quantitative survey method to gather data from consumers of indigenous chicken in Malaysia and analyzed it using structural equation modeling.

Health and price consciousness, along with effort expectancy, significantly influence consumer attitudes toward indigenous chicken. However, environmental consciousness and availability do not directly impact attitude. Similarly, attitude mediates the relationship between health consciousness, price consciousness, effort expectancy and purchase behavior, while attitude does not mediate the relationship between environmental consciousness, availability and purchase behavior of indigenous chicken.

This study is one of the pioneering works to apply the cognitive affect behavior (CAB) model to examine the factors influencing consumer attitudes and purchase behavior toward indigenous chicken. It investigates how constructs such as health consciousness, environmental consciousness, price consciousness, effort expectancy and availability affect these attitudes and behaviors, offering novel insights into the purchase intentions of younger and educated individuals.

In the contemporary global market, supplier selection represents a crucial process for enhancing firms’ competitiveness. This is a multi-criteria decision-making problem that involves consideration of multiple criteria. Therefore this requires reliable methods to select the best suppliers. The purpose of this paper is to examine and propose appropriate method for selecting suppliers.

ANFIS and fuzzy analytic hierarchy process-fuzzy goal programming (FAHP-FGP) are new methods for evaluating and selecting the best suppliers. These methods are used in this study for evaluating suppliers of dairy industries and the results obtained from methods are compared by performance measures such as Mean Squared Error, Root Mean Squared Error, Normalized Root Men Squared Error, Mean Absolute Error, Normalized Root Men Squared Error, Minimum Absolute Error and R2.

The results indicate that the ANFIS method provides better performance compared to the FAHP-FGP method in terms of the selected suppliers scoring higher in all the performance measures.

The proposed method could help companies select the best supplier, by avoiding the influence of personal judgment.

This study uses the well-structured method of the fuzzy Delphi in order to determine the supplier evaluation criteria as well as the most recent ANFIS and FAHP-FGP methods for supplier selection. In addition, unlike most other studies, it performs the selection process among all available suppliers.

Despite the concept of regenerative tourism (RT) is still under research, there are recognized attempts to conceptualize RT from different thoughts, paradigms, worldviews and frameworks, even though the integral or alternative paradigms lack a detailed description. Therefore, the goal of this chapter is to overview the current debates on the background of RT, its definitions and its relationship with sustainability and tourism besides exploring the RT paradigms, principles and objectives in extant literature.

In this introductory chapter, we collaborate on how digital transformation (DT) supports a value-driven educational approach, emphasizing the need for regular assessments of stakeholder needs, enhancing students' abilities to solve complex problems, applying learned knowledge effectively, nurturing creativity, and boosting employment prospects through skill development. Strategic considerations for implementing DT include creating a shared vision through collaborative strategy development, establishing clear objectives, designing a detailed action plan for DT initiatives, encouraging active participation from all educational community members, and maintaining the DT strategy through continuous evaluation and adaptation. By interweaving DT with these strategic educational priorities, higher education institutions can not only improve the learning experience but also equip students to succeed in a rapidly evolving future.

The evolution of Artificial Intelligence (AI) in higher education marks a paradigm shift, driving significant changes in pedagogical approaches and learning methodologies. With the rise of generative AI and artificial general intelligence (AGI), institutions have witnessed a transformative era where traditional content creation and delivery are being redefined. Start-ups like OpenAI and Anthropic have been at the forefront, offering tools like ChatGPT and Claude-3, which reshape natural language processing and forecast a future where AI integrations are seamless and pervasive. This chapter provides a critical overview of the current AI-driven applications enhancing personalized learning, content generation, and remote learning. Tools such as Mainstay, CourseGenie, and AIDES demonstrate AI's capacity to improve student engagement and success rates, while Degreed and Gnowbe showcase the broadening horizons of AI in skills building and microlearning experiences. Furthermore, platforms like Elicit and Research Rabbit exemplify the transformation in research and academic writing, albeit not without raising ethical concerns. In conclusion, AI's permanence in the educational landscape is unquestionable, calling for strategic frameworks that empower educators and students to harness its benefits effectively. The imminent expansion of the AI tool ecosystem necessitates preparedness for substantial shifts in educational practices, where ethical considerations and value-based strategies become paramount. Higher education institutions must align with this technological momentum, ensuring AI's potential is maximized in an ethical, inclusive, and impactful manner.

The dawn of Artificial Intelligence (AI) in higher education (HE) is not just on the horizon; it's here, promising a transformative leap forward. This shift is not simply about adopting new technologies; it's about redefining educational paradigms to meet specific challenges – from enhancing support and critical thinking to improving outcomes and fostering teamwork. This chapter outlines a comprehensive strategy to integrate AI into HE, spotlighting personalized learning, content generation, and remote learning, among others, as key domains ripe for AI's influence. An effective AI strategy will foster excellence and enable HE institutions to unlock the potential of technology for students and faculty alike. At its core, the proposed AI development strategy targets five critical areas: training, career growth, skill enhancement, learning, and team building. These areas ensure that all HE community members are well-equipped to navigate the AI-enhanced landscape of future jobs and challenges. However, realizing the full benefits of AI transcends the deployment of tools and systems; it requires strategic planning, investment in people, and policy changes. HE must cultivate champions to spearhead this transformation, emphasizing that success is not just measured in output but in the cultivation of socially responsible citizens. To harness AI's full capacity, we must transcend outdated stereotypes and metrics, fostering an educational environment that prepares students for the future. The ultimate goal is not just to integrate AI into HE but to use it as a catalyst for growth, innovation, and a better future for all.

This chapter explores the transformative impact of artificial intelligence (AI) on higher education, particularly in the context of accelerating technological and societal changes. As higher education institutions face the need to offer more flexible, adapted and relevant academic programmes, AI presents significant opportunities and challenges. In the first part of this chapter, the authors elaborated on characteristic the evolution of AI including characterizing the emerging AI landscape. One of our contributions in this concluding chapter is to conceptualize the next areas of deployment of AI in higher education considering the novel, innovative services that will disrupt the entire market in the next few years. The strategic proposition for deployment of AI in higher education highlighted six pillars, namely large language models, research.AI, content creation.AI, personalised learning.AI, skill building assistants.AI and education out of the Box.AI. The authors presented opportunities to harness AI to enhance teaching, learning and research under each pillar, along with a detailed list of potential application areas and services. Universities are exploring innovative ways to use AI-driven solutions to improve research, teaching and learning experiences, and the authors also developed indicative scenarios for the use of AI in higher education based on the six pillars. One of the bold contributions in this chapter is the structured framework for understanding the evolution and use of AI in higher education, utilizing a matrix to map the intersection of market penetration and product development. Finally, the authors discuss future directions and strategies for Higher Education 2030 in light of advances in AI technology.

This chapter delves into the pivotal role of digital transformation (DT) strategies in fostering educational innovation, particularly through the lens of transformative learning (TL). By outlining a five-stage TL model, we explore how DT strategies can not only support but also significantly enhance educational reforms. Actions and multipliers, constituting the core elements of this model, interact dynamically to advance TL within academic institutions. Actions, such as strategic initiatives and the development of learning environments, account for the tangible steps toward transformation. Meanwhile, multipliers amplify these efforts, emphasizing the importance of strategy, commitment and the sustainable impact of educational transformations. We also highlight the emerging influence of artificial intelligence (AI) in reshaping learning contexts, demonstrating its capacity to personalize learning experiences and foster problem-solving skills. Additionally, we envision the future trajectory of higher education (HE) toward 2035, emphasizing the integration of AI and DT in creating a responsive and adaptive educational ecosystem. This chapter argues that DT is not just a tool but also a catalyst for active and transformative learning, proposing a holistic approach to integrating technology in education that addresses current challenges and anticipates future needs.

Based on social exchange theory and social identification theory, I investigated how employee organizational identification affects the effectiveness of commitment-based human resource (HR) practices. I focused on employee attitudes (job satisfaction) and behaviors (proactive knowledge seeking) as HR practices’ outcomes.

Using a structural equation modeling analytical approach, I tested the hypotheses with data from a web-based cross-sectional survey of 208 specialists and engineers of manufacturing subsidiaries in Poland.

Results showed that the positive relationship between commitment-based-HR practices and job satisfaction is weakened for employees strongly identified with the organization. Simultaneously, the connection between seeking knowledge and job satisfaction is stronger and more important for people who identify moderately to strongly.

The study limitations regard mainly its cross-sectional design and single cultural and industrial context.

From the managerial perspective, the study suggests that to enhance proactive employee behavior, companies need to increase employee organizational identification and ensure that employees have a positive perception of the implemented HR practices.

The study contributes to the ongoing discussion on whether individual contingencies affect the effectiveness of commitment-based HR practices in the form of individual attitudinal and behavioral outcomes. The findings revealed that the contingent effect of organizational identification depends on the type of individual outcomes, suggesting that the strength of organizational identification affects how employees decide to reciprocate the organization’s attention and investment.