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This paper proposes that the trade-off between medical benefits and privacy concerns among mHealth users extends to their disclosure intentions, manifested as individuals simultaneously holding intentions to tend to disclose in the near future and to reduce disclosure in the distant future. Consequently, this paper aims to explore the privacy decision-making process of mHealth users from the perspective of a dual trade-off.

This paper constructs the model using the privacy calculus theory and the antecedent-privacy concern-outcome framework. It employs the construal level theory to evaluate the impact of privacy calculus on two types of disclosure intentions. The study empirically tests the model using a data sample of 386 mHealth users.

The results indicate that perceived benefits positively affect both near-future and distant-future disclosure intentions. In contrast, perceived risks just negatively affect distant-future disclosure intention. Additionally, perceived benefits, near-future and distant-future disclosure intentions positively affect disclosure behavior. The findings also reveal that privacy management perception positively affects perceived benefits. Personalized services and privacy invasion experience positively affect perceived benefits and risks, while trust negatively affects perceived risks.

This paper considers the trade-off in the privacy calculus phase as the first trade-off. On this basis, this trade-off will extend to the disclosure intention. The individuals’ two times of trade-offs between privacy concerns and medical benefits constitute the dual trade-off perspective. This paper first uses this perspective to explore the privacy decision-making process of mHealth users. This paper employs the construal level theory to effectively evaluate the impact of privacy calculus on both disclosure intentions in mHealth, extending the theory’s applicability. Moreover, we introduce antecedents of privacy calculus from the perspectives of platform, society, and individuals, enhancing the study’s realism. The research findings provide a basis for mHealth platforms to better cater to users’ privacy needs.

In recent years, democracies across Europe have been challenged in ways this paper has not witnessed on such a scale for generations. It is therefore unsurprising that innovations within democracy are emerging, particularly in terms of participatory and deliberative practices. Focusing specifically on local democracy and decision-making, this paper aims to examine ways of democratising future studies as a means of enhancing citizen participation in democracy.

The paper draws upon the growing body of work under the umbrella term of “participatory futures” as a way of contextualising and critiquing the “real-world” application and empirical testing of methods within this field by local municipalities.

It identifies the importance of supporting the development of futures literacy in citizens, public administration officials and political and strategic leaders. The paper demonstrates how broadening the application of existing foresight techniques through their confluence with participatory action research (PAR) principles can create spaces that reignite people’s social imagination. This in turn enables citizens and those working in local municipalities to engage in dialogue about the future.

The paper examines the results of a PAR study, in which innovative participatory future methods were tested as tools for enhancing citizen involvement in local decision-making. Through this, it outlines how foresight practices can be democratised, supporting local democracy to thrive, and identifies future research and practice directions within the field.

This study aims to explore the intricate relationship between uncertainty indicators and volatility of commodity futures, with a specific focus on agriculture and energy sectors.

The authors analyse the volatility of Indian agriculture and energy futures using the GARCH-MIDAS model, taking into account different types of uncertainty factors. The evaluation of out-sample predictive capability involves the application of out-sample R-squared test and computation of various loss functions.

The research outcomes underscore the significant impact of diverse uncertainty factors such as domestic economic policy uncertainty (EPU), global EPU (GEPU), US EPU and geopolitical risk (GPR) on long-run volatility of Indian energy and agriculture (agri) futures. Additionally, the study demonstrates that GPR exhibits superior predictive capability for crude oil futures volatility, while domestic EPU stands out as an effective predictor for agri futures, particularly castor seed and guar gum.

The study offers practical implications for market participants and policymakers to adopt a comprehensive perspective, incorporating diverse uncertainty factors, for informed decision-making and effective risk management in commodity markets.

The research makes an inaugural attempt to examine the impact of domestic and global uncertainty indicators on modelling and predicting volatility in energy and agri futures. The distinctive feature of considering an emerging market also adds a novel dimension to the research landscape.

The purpose of this study investigates the short-term market reaction of three commodity futures indices for four recent events of high geopolitical risk: the Ukraine–Russia war, the Taiwan Strait crisis and the Hamas terrorist attack on Israel.

The author examines three commodity futures indices at and around the beginning of four recent events of high geopolitical risk using an event study methodology.

The results show a positive abnormal return for the commodity futures indices for three of the four recent events considered in the analysis. The exception in terms of abnormal returns observed is the visit of US Speaker of the House Nancy Pelosi to Taiwan on August 2, 2022, which resulted in statistically significant negative abnormal returns in the commodity futures around the visit. The other three geopolitical events, by causing an increase of uncertainty level and supply-side constraints, led to a rise in the price of most commodity futures. This allowed commodity-exporting countries to achieve positive and statistically significant abnormal returns. Policy implications of our findings are discussed.

The effect of high geopolitical risk events on commodity futures indices has been relatively little examined in the financial theory. This study intends to fill this gap in the literature.

This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.

The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.

The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.

These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.

The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.

This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.

We highlight how factors like volatility, futures volume, and open interest vary in their impact.

The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.

We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

The purpose of the article “Nurturing Human Intelligence in the Age of AI: Rethinking Education for the Future” is to explore the profound impact of Artificial Intelligence (AI) on education and to emphasize the need for a fundamental shift in current education systems. The article aims to provide practitioners with actionable insights on how to navigate the rapidly evolving landscape of AI in education while preparing young people for their crucial role as the workforce of tomorrow. It seeks to highlight the potential of AI to revolutionize education while also acknowledging the importance of preserving the unique human touch in the learning process.

This article explores the disruptive impact of Artificial Intelligence (AI) on education and emphasizes the need for a fundamental shift in current education systems to prepare young people for an AI-driven future. It highlights the potential of AI to revolutionize education through personalized learning experiences, enhanced teacher professional development and automation of administrative tasks while acknowledging the importance of approaching AI implementation with caution and preserving the unique human touch in education. The article argues for a shift in focus from rote learning to fostering critical thinking, creativity and problem-solving skills, emphasizing the development of Learning Mastery and Knowledge Mastery. It underscores the vital role of educators in leveraging AI technologies and preparing young people for the future, along with the need for responsive educational policies and curriculum frameworks that integrate AI literacy and ethical considerations. The article concludes by calling for reimagining the schooling system, prioritizing high-level thinking and nurturing the unique capabilities of human intelligence. The future of education lies in harnessing the power of AI while celebrating and cultivating distinctively human qualities. Educational practitioners play a crucial role in shaping this future by bridging the gap between research and practice, ensuring a positive and prosperous future for society in an AI-driven world.

(1) AI can revolutionize education through personalized learning, enhanced teacher development and task automation. (2) Balance is needed between AI and human touch in education. Current education systems fail to cultivate critical thinking and creativity. (3) Learning Mastery and Knowledge Mastery should be emphasized to foster independent thinking and problem-solving. (4) Educators play a vital role in integrating AI into the learning process. (5). AI can redefine success in education and cultivate future-proof skills. (6). Responsive and adaptable educational policies are necessary. (7) The future of education lies in harnessing AI while nurturing human intelligence.

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(1) Educators should actively engage with AI technologies and explore ways to integrate them into the learning process to enhance personalized learning experiences. (2) Professional development programs should be designed to equip teachers with the necessary skills to effectively utilize AI tools and leverage them to improve instructional practices. (3) Curriculum frameworks need to be revised to integrate AI literacy, digital citizenship and ethical considerations into the educational journey of young learners. (4) Educational institutions should invest in AI-powered assessment tools that provide a holistic understanding of a student’s abilities, capturing their strengths and areas for improvement beyond test scores. (5) Educators should focus on teaching metacognitive strategies, encouraging self-reflection and self-assessment and providing opportunities for students to develop problem-solving and critical-thinking skills. (6) Active learning strategies, such as project-based learning, problem-based learning and inquiry-based learning, should be employed to foster deep learning and knowledge mastery. (7) Educational policies should encourage innovation and collaboration between educational institutions, government bodies and industry stakeholders to ensure responsiveness to the rapidly evolving landscape of AI in education. (8) Educators should strive to create a learning environment that nurtures and celebrates the unique capabilities of human intelligence while harnessing the power of AI to enhance the learning experience.

(1) Workforce preparedness for an AI-driven future. (2) Potential exacerbation of societal inequalities. (3) Fostering human–AI collaboration skills. (4) Addressing ethical concerns regarding data privacy and security. (5) Emphasizing lifelong learning to adapt to changing demands. (6) Redefining success through a holistic view of student abilities. (7) Shaping societal values that balance human intelligence and AI capabilities. The education system must address these implications to ensure equitable access to AI-enhanced learning, maintain public trust and prepare individuals for a society where human–AI collaboration is essential, while promoting a balanced and harmonious coexistence between human intelligence and AI.

The article “Nurturing Human Intelligence in the Age of AI: Rethinking Education for the Future” offers a fresh perspective on the impact of AI on education. While the topic of AI in education is not novel, the article’s emphasis on nurturing human intelligence alongside AI integration sets it apart. The author’s call for a fundamental shift in education systems to prioritise critical thinking, creativity and problem-solving skills is a unique approach. The article’s exploration of Learning Mastery and Knowledge Mastery as key concepts in preparing students for an AI-driven future adds originality to the discussion. Overall, the article presents a thought-provoking and original viewpoint on the future of education in the age of AI.

Prior studies have consistently treated participative leadership as a given leadership style. Conversely, this study aims to prove that participative leadership can be predicted by leaders and teams collectively, depending on leaders' work characteristics (i.e. workload).

A two-source survey was designed to collect data from a sample of 89 leader-team dyads in a trading company in a southeastern Chinese city. Polynomial regression and response surface analysis were used to test the hypotheses.

The leader showed more participative leadership when leader-team future orientation was congruent rather than incongruent; in the congruent situation, there was an inverted U-shaped relationship between leader-team future orientation congruence and participative leadership; in the incongruent situation, when the team's future orientation gradually exceeded the leader's, participative leadership first increased and then decreased; and leader workload positively moderated the relationship between leader-team future orientation congruence and participative leadership.

These findings theoretically respond to the call for investigating the influence of leader-team future orientation congruence on leaders’ behaviors, and in practice enlighten managers on how to encourage supervisors to involve employees in decision-making processes.

Compressing project timelines represents a prevalent temporal tactic aimed at accelerating the innovation process. However, empirical evidence on the impact of such time constraints on innovation remains inconclusive. This study aims to investigate the relationship between a prevalent organizational time mechanism—Performance Appraisal Interval (PAI)—and employee exploratory innovation behavior. Additionally, we explore the boundary conditions that may influence this relationship: the moderating effects of future work self salience and supervisory developmental feedback.

Using online survey data collected in two waves from 426 employees working in hi-tech companies in China, we tested all the hypotheses.

(1) PAI demonstrates an inverted U-shaped influence on employees exploratory innovation behavior; (2) Employees’ future work self salience serves as a moderator that enhances the positive nature of this inverted U-shaped relationship; (3) Supervisory developmental feedback amplifies the moderating role of future work self salience, and the synergistic effect of PAI, future work self salience, and supervisory developmental feedback significantly enhances exploratory innovation behavior.

By providing insights that are attuned to the temporal aspects of performance appraisal, this study aids organizations in making more informed, strategic decisions that enhance both the effectiveness of performance assessments and the cultivation of an environment that encourages exploratory innovation. Additionally, it is recommended that organizational leaders incorporate future-oriented interventions and developmental feedback into their management practices to further promote employees' engagement in exploratory innovation.

Drawing on the interactive theory of performance, this study introduces a novel perspective on how an organizational temporal mechanism influences exploratory innovation and advances our understanding of the non-linear link between time constraints and employees' innovative behaviors.

The purpose of this study is to explore how past and future temporal focus of CEOs in the hospitality industry influence their intention to invest in metaverse technology and the underlying mechanism under boundary conditions of perceived competitive pressure.

This multi-informant study collected data over three waves from a sample of 235 CEOs and their subordinates in India’s hospitality industry. A PLS-SEM was applied to the study data. Further, the study also used phenomenological interviews to capture CEOs’ perspectives on the study’s conceptual model.

Findings suggest that the past temporal focus of CEOs decreases technology orientation, and future temporal focus increases the technology orientation of firms, consequently impacting the intention to invest in the metaverse. CEOs’ perceived competitive pressure moderates the mediating relationship, such that the negative impact of past temporal focus on technology orientation is decreased and that of future temporal focus on the CEO is increased.

By exploring the role of a CEO’s past and future temporal focus on influencing technology orientation and, hence, adoption of new technology, the study extends upper-echelon theory to the field of metaverse adoption in the hospitality industry and responds to scholars’ calls to explore the industry’s technology adoption from the lens of the upper echelon.

The study has significant implications for the success of the adoption of metaverse technology in the hospitality industry. Findings imply that the board members should encourage CEOs to have future temporal focus.

The study provides novel insights into the adoption of metaverse technology by the hospitality industry, where CEO attributes such as their temporal focus influence intention to invest in metaverse.

The purpose of this study is to analyze the price discovery and market efficiency of energy futures traded in India. The study also examines the volatility spillover effect between the cash and futures markets of energy commodities.

The study uses crude oil and natural gas spot and futures series traded at Multi Commodity Exchange (MCX), India. To evaluate the objectives, the paper employs the cointegration test, causality check, dynamic ordinary least squares (DOLS) method and Baba, Engle, Kraft and Kroner (BEKK) GARCH Model.

The study supports the long-run association between the selected markets. Unlike natural gas, in the case of crude oil bidirectional, flow of information is observed. The study rejects the unbiasedness and efficient market hypothesis of the energy futures market in India. Further, the study confirms that the selected energy commodities indicate bidirectional shock transmission between their respective cash and futures markets.

The study will assist the commodity market participants in designing their trading strategy. The volatility signal will be used by investors and portfolio managers for risk management and portfolio adjustment. Regulators will be able to anticipate future spillover and can design policies to strengthen the market.

The paper evaluates the three aspects of the energy futures market, namely price discovery, market efficiency and volatility slipover. To the best of the authors’ knowledge, studies on efficacy and shock transmission in the context of the energy futures market in India are rare. Further, the study also contributes by investigating the price discovery process of the energy futures market.