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The purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.

A model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.

This modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.

The research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.

The study implies that transport operations would actually need to be re-organized so as to deal with lowering CO₂ footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).

The growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.

Despite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

Markov chains and queuing theory are widely used analysis, optimization and decision‐making tools in many areas of science and engineering. Real life systems could be modelled and analysed for their steady‐state and time‐dependent behaviour. Performance measures such as blocking probability of a system can be calculated by computing the probability distributions. A major hurdle in the applicability of these tools to complex large problems is the curse of dimensionality problem because models for even trivial real life systems comprise millions of states and hence require large computational resources. This paper describes the various computational dimensions in Markov chains modelling and briefly reports on the author's experiences and developed techniques to combat the curse of dimensionality problem.

The paper formulates the Markovian modelling problem mathematically and shows, using case studies, that it poses both storage and computational time challenges when applied to the analysis of large complex systems.

The paper demonstrates using intelligent storage techniques, and concurrent and parallel computing methods that it is possible to solve very large systems on a single or multiple computers.

The paper has developed an interesting case study to motivate the reader and have computed and visualised data for steady‐state analysis of the system performance for a set of seven scenarios. The developed methods reviewed in this paper allow efficient solution of very large Markov chains. Contemporary methods for the solution of Markov chains cannot solve Markov models of the sizes considered in this paper using similar computing machines.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

The present critical analysis has been performed to explore the steady stagnation point flow of Jeffery fluid toward a stretching surface, in the presence of convective boundary conditions. It is assumed that the fluid strikes the wall obliquely. The governing non-linear partial differential equations for the flow field are converted to ordinary differential equations by using suitable similarity transformations. Optimal homotopy analysis method (OHAM) is operated to deal the resulting ordinary differential equations. OHAM is found to be extremely effective analytical technique to obtain convergent series solutions of highly non-linear differential equations. Graphically, non-dimensional velocities and temperature profile are expressed. Numerical values of skin friction coefficients and heat flux are computed. The comparison of results from this paper with the previous existing literature authorizes the precise accuracy of the OHAM for the limited case. The paper aims to discuss these issues.

The governing non-linear partial differential equations for the flow field are converted to ordinary differential equations by using suitable similarity transformations. OHAM is operated to deal the resulting ordinary differential equations.

OHAM is found to be extremely effective analytical technique to obtain convergent series solutions of highly non-linear differential equations. Graphically, non-dimensional velocities and temperature profile are expressed. Numerical values of skin friction coefficients and heat flux are computed.

The comparison of results from this paper with the previous existing literature authorizes the precise accuracy of the OHAM for the limited case.

The growth and promotion of green tourism destinations can have many potential benefits from artificial intelligence (AI). The literature on AI and applications of AI in promoting green destinations is very less. The major areas of research in this direction are related with nature-based tourism or sustainable tourism. There is a great potential to research in this area as AI can play an important role in promoting green destinations. Simultaneously, AI can play the role of enabler to achieve environmental targets by promoting various green destinations. The major finding of this chapter is that the research in this area is majorly revolving around tourist destinations and sustainable development. Another area of research where AI is used is eco-tourism and sustainable tourism. With the help of various decision support systems, sustainable tourism can be promoted. Social media platforms and digitalization of tourism is a great enabler of using AI in the field of tourism.

The purpose of this technical viewpoint is to provide a commentary of how we went about using logistics prototyping as a method to engage citizens, science fiction (SF) writers and small- to medium- sized enterprises (SME’s). Six urban logistic prototypes built on the themes of future cities, community resilience and urban supply chain management (SCM) are summarized, together with details of the data collection procedure and the methodological challenges encountered. Our investigation aimed to explore the potential of logistics prototyping to develop “user-driven” and “SME” approaches to future city design and urban supply chain decision-making.

This Boston field experiment was a case study investigation conducted between May and August 2013. Qualitative data was collected using a “mixed-method” approach combining together focus groups (MIT faculty), scenarios, prototyping workshops, interviews and document analysis. These story-creators could use the prototype method as a way of testing their hypotheses, theories and constrained speculations with regard to specified future city and urban supply chain scenarios.

This viewpoint suggests that the prototyping method allows for unique individual perspectives on future city planning and urban supply chain design. This work also attempts to demonstrate that prototyping can create sufficiently cogent environments for future city and urban SCM theories to be both detected and analysed therein. Although this is an experimental field of the SCM theory building, more conventional theories could also be “tested” in the same manner.

By embedding logistics prototyping within a mixed method approach, we might be criticized as constraining its capability to map out the future – that its potential to be flexible and imaginative are held back by the equal weighting given to the more conventional component. In basing our case study within one city then this might be seen as limiting the complexity of the empirical context – however, the situation within different cities is inherently complex. Case studies also attract criticism on the grounds of not being representative; in this situation, they might be criticized as imperfect indicators of what transpires in other situations. However, this technical viewpoint suggests that in spite of its limitations, prototyping facilitates an imaginative and creative approach to theory generation and concept building.

The methodology allows everyday citizens and SME’s to develop user-driven foresight and planning scenarios with city strategists’ and urban logistic designers. It facilitates much broader stakeholder involvement in city and urban supply chain policymaking, than current “quantitative” approaches.

Logistics fiction prototyping provides a democratic approach to future city planning and urban supply chain design. It involves collectively imagining socio-technical futures and second-order sociological effects through the writing of SF narratives or building “design fictions”.

Decision-making in future cities and urban SCM is often a notable challenge, balancing the varying needs and claims of multiple stakeholders, while negotiating an acceptable trade-off between their competing claims. Engagement with stakeholders and active encouragement of stakeholder participation in the supply chain aspects of future cities is increasingly a feature of twenty-first century social decision-making. This viewpoint suggests that the prototyping method allows for unique individual perspectives on future city planning and urban supply chain design. This work also attempts to demonstrate that prototyping can create sufficiently cogent environments for future city and the urban SCM theories to be both detected and analysed therein. Although this is an experimental field of SCM theory building, more conventional theories could also be “tested” in the same manner.

This research explores sustainable competitive advantage (SCA) in heritage tourism, focusing on authenticity, cultural preservation, and visitor experience. It examines the role of data in transforming visitor experiences through personalization, marketing insights, and predictive analytics. This chapter also examines the impact of technology, such as mobile applications, augmented reality (AR), and virtual reality (VR), on heritage tourism experiences. It emphasizes the importance of balancing modernization and cultural preservation, emphasizing ethical considerations and safeguarding heritage site authenticity. This chapter also highlights the significance of sustainability in heritage tourism, including eco-conscious practices, community engagement, and resource allocation. It discusses feedback mechanisms, documentation of cultural and historical assets, and heritage preservation as fundamental elements in SCA. The research encourages collaborative innovation within the heritage tourism domain, encouraging academics and industry professionals to explore emerging research topics. This chapter advocates for a future where heritage tourism offers unparalleled and sustainable experiences while safeguarding and celebrating the enduring treasures of the past.

This study seeks to empirically examine the influence of corporate governance on corporate performance in Ghana.

The study employs data from 30 listed firms spanning from 2008 to 2018 and applies the generalized method of moments technique. The authors use economic value added, shareholder value added (SVA) and economic margin (EM) as measures of corporate performance.

The findings reveal that the presence of both inside directors and outside (nonexecutive) directors significantly improves corporate performance, lending credence to both the stewardship theory and the agency theory. The inclusion of women on the corporate boards and frequent meetings of the board reduce the economic profits of firms. The authors find that CEO duality impedes corporate performance, supporting the presumption of the agency theory. The study further reveals that audit committee size and ownership concentration positively drive the performance of quoted firms in Ghana.

Prior studies on corporate governance and firm performance nexus have chiefly adopted traditional accounting-based performance measures such as return on assets and return on equity to evaluate firm performance. However, these indicators are critiqued for being historic and fail to consider firms' cost of equity. In light of the shortcomings of the accounting-based proxies, this study takes a unique direction by using value-based metrics, which are considered superior measures of performance. Besides, to the best of the authors' knowledge, this study provides a first attempt to investigate the link between corporate governance and firm performance using SVA and EM as performance indicators.

The purpose of this study is to look at how Shariah-compliant status and Shariah regulation affect the demand for initial public offerings (IPOs) in Pakistan. The Shariah-compliant status, which is seen as a method that offers a credible signal to investors, may explain the anomaly in IPO demand.

This research used multivariate and quantile regression models to assess data from 85 IPOs issued on the Pakistan Stock Exchange between 2000 and 2019.

Shariah-compliant status has a considerable negative association with IPO demand. Nevertheless, there is a considerable positive association among Shariah regulation and IPO demand. Furthermore, the interaction among regulatory quality and Shariah-compliant status has a considerable strong influence on IPO demand. As a consequence, the findings show that Shariah-compliant firms might possibly attract the attention of investors. Investors were found to concur on the amicability of rigorous rules and permissible Shariah-compliance aspects.

Future studies could analyse the financial ratio benchmark (cash and debt) to determine the Shariah-compliant status and Shariah regulation to better understand the problem of IPO demand in the context of Pakistan.

The outcomes of this research are useful for issuers and underwriters in comprehending the characteristics that influence high and early IPO success. Such knowledge may assist issuers and underwriters in responsibly planning and managing the IPO process.

The results may be useful to investors looking for critical information in prospectuses to make the best choice when subscribing to IPOs in Pakistan.

This is one of the first studies to provide empirical data on the links among Shariah-compliant status, Shariah regulation and IPO demand in Pakistan. Furthermore, this research demonstrates the interaction impact of regulatory quality and Shariah-compliant status on IPO demand.

The aim of our study is to explore the nexus between the gender composition of board and firm financial performance. We use the data of 114 listed banks from 10 Asian emerging economies. Data were extracted from the DataStream for the year 2012–2021. We apply fixed effect model to analyze the data. In addition, we use generalized method of moments (GMM) to verify our main findings. We find that both proxies of board gender composition which are the proportion of female board members and the percentage of female executives on the board have a significant impact on banks' financial performance. Findings suggest that female representation on board provides more insights of monitoring and optimal advisory capabilities and, therefore, gender-diversified board enhances firm performance. Females are more active in business matters and take more interests to fulfill their responsibilities. The results of our study provide useful signals for corporate and regulatory policymakers. Board gender disparities between enterprises should be better understood by all stakeholders to have the optimal combination of board members that ultimately lead to better performance of the firm.