Search results

Many firms are now pursuing large-scale change initiatives to accelerate their digital transformation (DT) and sustainable development. However, the success rate of DT projects is still low, and the extent to which DT enables firms to improve environmental sustainability (ES) remains unclear. Recently, researchers have argued that DT is more about people transformation rather than technology. Based on the contingent resource-based view, this study investigates how DT influences ES and examines the moderating role of training provision (TP).

Survey data gathered from manufacturers in China were analysed to test the proposed theoretical framework.

The results indicate that DT has a positive impact on ES, and that this effect is positively moderated by TP.

The empirical findings provide insights for managers to understand the success of digital sustainability transformation requires necessary digital knowledge and skills derived from TP.

This study provides an initial examination of digital sustainability, which is a new stream of literature for the digital age, and further extends existing knowledge by demonstrating the importance of people (i.e. TP) in strengthening the effectiveness of DT on ES.

Corporate governance has become a core topic in management research and business practice. Recent debates like – environmental responsibility, sustainability, ethics, corporate control, generation, protection and distribution of wealth, the role of the board and senior executives in setting standards for performance management, and stakeholder relationship management – have strong links to organisational trust. However, management literature has been relatively silent on how various corporate governance configurations and perspectives potentially shape trust relations within the organisation, especially in Africa. Thus, this chapter reviews corporate governance through the lens of the institutional logics perspective evident in western capitalism and develops a framework connecting various governance configurations to organisational trust. Doing so provides new directions for those seeking to develop further research in corporate governance, institutional logics and organisational trust.

Resilience while learning is the capacity to initiate, persist and direct effort toward learning when experiencing unpleasant affective states. The underlying mechanisms of resilience are emotional buffering and self-regulation when experiencing unpleasant affective states. The authors identified four factors that support resilience while learning: positive emotional engagement, creative problem-solving, learning identity and social support. The authors developed and tested scales and found evidence to support the four-factor model of resilience. The authors offer a person-centered approach to resilience in learning by conducting a latent profile analysis that tested the likelihood of resilience based on profiles of differences in scores on these factors under two affective conditions: (unpleasant) learning during frustration versus (pleasant) learning during progress. A quarter of individuals activated the four resilience factors in pleasant and unpleasant affective states, while 75% of participants saw decrements in these factors when faced with frustration. The results support a four-factor, person-centered approach to resilience while learning.

The authors develop and test a four-factor model of resilience and test the model in a group of 330 management undergraduate and graduate students. Each participant identified two learning episodes in their responses, one while frustrated and one while making progress, and ranked the level of intensity on the four resilience factors. Analysis on an additional 88 subjects provided additional support for the validation and reliability of scales.

Results revealed 2 latent profiles groups, with 25% of the sample associated with resilience (low difference on resilience factors between the two learning episodes) and 75% who remain susceptible to unpleasant emotions (high difference between the two learning episodes).

The study supports a person-centered approach to resilience while learning (in contrast to a variable centered approach).

The study provides a means to classify individuals using a person-centered, rather than a variable-centered approach. An understanding of how individuals buffer and self-regulate while experiencing unpleasant affect while learning can help educators, consultants and managers develop better interventions for learning.

This study addresses the growing concern over student success associated with increased dropout rates among undergraduate business students, and the failure of many management developments and executive training efforts. This study suggests that looking at specific variables may not provide insight into the complex relationship between learning outcomes and factors that support resilience in learning.

There is growing interest in understanding resilience factors from a person-centered perspective using analytical methods such as latent profile analysis. This is the first study to look at how individuals can be grouped into similar profiles based on four resilience factors.

This case invites students to assess the impact that Brexit, the withdrawal of the United Kingdom from the European Union, might have on a New York–based hedge fund's portfolio and, specifically, its UK assets. The case is designed to prompt students to make market assumptions and investment hypotheses based on a combination of numerical data and qualitative information. It requires no numerical computations; instead, it asks the student to interpret both markets' short-term reactions to the Brexit vote and strategy shifts from UK and European business leaders in order to evaluate longer-term implications for the economies of the United Kingdom, Europe, and the world.

With 43.2 million coronavirus cases and 525,000 deaths in 2022, India ranked second worldwide, after the United States (84.6 million cases and 1 million deaths), according to the latest available June 2022 COVID-19 impact data.

Amid people’s growing mistrust in the government, India’s news media enhanced the nation’s distinguished designation as the world’s largest and most populous democracy. India’s news media inform, educate, empower, and entertain a surging population of 1.4 billion people, which is roughly one-sixth of the world’s people.

Drawing upon the media agendamelding theoretical framework, we conducted a case study research into interplay between two prominent democratic institutions, the media and the government, to analyze the role of the COVID-19 pandemic in redefining India’s networked society.

India’s COVID-19 pandemic aggravated internecine tensions between media and government relating to four key freedom issues: (1) world’s largest COVID-19 lockdown affecting 1.3 billion Indians from March 25, 2020 to August 2020 with extensions and five-phased re-openings, to restrict the spread of COVID-19; (2) Internet shutdowns; (3) media censorship during the 1975–1977 “Emergency”; and (4) unabated murders of journalists in India.

Although the COVID-19 pandemic caused deleterious problems debilitating the tensions between the media and the government, India’s journalists thrived by speaking truth to power. This study delineates key aspects of India’s media agendamelding that explicates how the people of India form their media agendas. India’s news audiences meld media messages from newspapers, television, and social media to form a picture of the issues, insights, and ideas that define their lives and times in the 21st century digital age.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

This chapter presents the outcome of a study that examined the phenomena ‘Is Tourism Regulation Catalyst for Abuja Federal Capital Territory (FCT) Nigeria, Hotels Business Performance Innovation?’ Previous studies on this subject area have been largely done around tourism and its impact on hotel business performance and the relationship between tourism regulation and hotel business performance from mostly western perspectives. Hence, this study aims to investigate the direct effect of tourism regulations on hotel business performance in Nigeria. Quantitative survey questionnaires were used to collect data from 176 participants comprising general managers and departmental heads in 22 key Abuja FCT Nigerian hotels. Balanced Scorecard (BSC) developed by Norton and Kaplan was adopted as the study’s theoretical framework. Data collected were analysed using the simple linear regression technique and Statistical Package for the Social Sciences (SPSS) statistical analysis software tool.

The result of the analysis shows that tourism regulation has a significant and positive correlation with Abuja hotel business performances based on the BSC four dimensions of financial, customer, processes and learning and growth. The unique city characteristics of Abuja FCT were also identified as an issue for consideration in future tourism regulation innovation by the regulatory authorities. This study contributes to business performance measurement literature from the Abuja FCT hotels, Nigerian perspective, and sets an agenda for the Nigerian tourism regulators, the Nigeria Tourism Development Corporation (NTDC) charged with diversifying the Nigerian economy revenue through tourism performance innovation. Also, a policy study into city characteristics classification as a way of innovating tourism regulations and hotels business performance is suggested.