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Smoking rates are known to be higher amongst those committed to prison than the general population. Those in prison suffer from high rates of comorbidities that are likely to increase their risk of cardiovascular disease (CVD), making it more difficult to manage. In 2016, a tobacco ban began to be implemented across prisons in England and Wales, UK. This study aims to measure the effect of the tobacco ban on predicted cardiovascular risk for those quitting smoking on admission to prison.

Using data from a prevalence study of CVD in prisons, the authors have assessed the effect of the tobacco ban on cardiovascular risk, using predicted age to CVD event, ten-year CVD risk and heart age, for those who previously smoked and gave up on admission to prison.

The results demonstrate measurable health gains across all age groups with the greatest gains found in those aged 50 years and older and who had been heavy smokers. Quitting smoking on admission to prison led to a reduced heart age of between two and seven years for all participants.

The data supports tobacco bans in prisons as a public health measure to reduce risk of CVD. Interventions are needed to encourage maintenance of smoking cessation on release from prison for the full health benefits to be realised.

Introduction: Artificial intelligence (AI) and digitisation offer substantial human potential and profit margins, making them promising retail solutions. Retail leaders have successfully integrated comprehensive uses into their daily operations, while competitors heavily invest in new projects. The Indian retail sector is undergoing a significant transformation, which can be attributed to factors such as growing income, demographic characteristics, and enhanced consumerism, as well as the rapid development of new technologies such as digitisation and AI, which is changing both consumers’ and retailers’ buying behaviour.

Purpose: This study aims to determine the influence of AI on elements that drive digitisation in the retailing sector, as well as the factors that lead to organised retailers adopting digitisation and its impact on their business.

Methodology: The study employs a standardised questionnaire distributed to organised stores via an online link, and the data are analysed with SmartPLS software 3.0.

Finding: The retail sector is driven by elements that promote digitalisation in food and groceries retailing, such as simplicity of operation, adoption of digital payment, quicker internet connection, retailer consumer interface, and the involvement of AI.

Research implication: AI has significant consequences for retailing, which serves as the interface between marketers and customers.

Theoretical implication: The study’s findings reflect the perspectives of retailers, store managers, and entrepreneurs on how digitalisation and AI are crucial for the creation and growth of long-term competitive advantages in retail.

The global financial crisis of 2008 emphasized the need for monetary policy authorities to have a more comprehensive view of the conditions prevailing in the economy before deciding their policy stance. The purpose of this paper is to outline the construction of a financial conditions index (FCI) and investigate the possible co-integrating relationship between the economic growth and FCI.

The study employs the PCA methodology, with appropriate augmentations to handle the unbalanced panel data-sets and constructs a FCI for India. It tests the growth-predicting power of FCI by applying the auto regressive distributed lags approach to co-integration and verifies if the FCI is co-integrated with real GDP growth. It also discusses construction of a financial development index (FDI) which tracks the financial markets through M3, market capitalization and credit amount to residents.

The constructed FCI has a quarterly frequency and is available starting 1998q2. The long-run coefficient of FCI while predicting the real GDP growth is significant at 10 percent. The results confirm that a more-broader index FCI outperforms a narrower index FDI in growth prediction.

By showing that FCI is a better growth predictor than FDI, the study establishes the importance of including the foreign exchange markets, bond markets and stock markets while summarizing the conditions in the economy. The authors hope that the FCI would be helpful to the monetary authorities in their policy decisions.

The paper adds to the few existing studies studies dealing with FCI for Indian economy and constructs a more comprehensive index which tracks multiple markets simultaneously. It also fills the gap in literature by evaluating the correlating relationship between FCI and economic growth.

The purpose of this paper is to empirically investigate the antecedents and impacts of a largely overlooked concept, employee development, within the challenging area of returns management.

The proposed relationships are validated through structural equation modeling analysis with survey data collected in India.

Combining the ability–motivation–opportunity model in human resource management and the theoretical tenets associated with dynamic capabilities, the authors confirmed that supply chain learning, returns management orientation and information support are important antecedents of returns management employee development. In turn, the findings suggest that, as a dynamic capability, returns management employee development positively impacts a firm’s returns management and market performance.

To successfully tackle the challenges related to handling returns, companies must focus their resources not only on new technologies and related processes, but also on employee training and development as well.

Although recruiting and retaining talent in supply chain management has long been recognized as a serious global challenge, no previous research has empirically studied employee development practices in the returns management context.

The complex network structure causes several disruptions in the supply chain that make risk management essential for supply chain management including halal supply chain (HSM). During risk management, several challenges are associated with the risk assessment phase, such as incomplete and uncertain information about the system. To cater this, the authors propose a risk assessment framework that addresses the issues of uncertainty using neutrosophic theory and demonstrated the applicability of the proposed framework through the case of halal supply chain management (HSCM).

The proposed framework is using the capabilities of the neutrosophic number which can handle uncertain, vague and incomplete information. Initially, the risk related to the HSC is identified through a literature review and expert’s input. Further, the probability and impact of each HSM-related risk are assessed using experts’ input through linguistic terms. These linguistic values are transformed into single-value trapezoidal neutrosophic numbers (SVTNNs). Finally, the severity of each HSM-related risk is determined through the multiplication of the probability and impact of each risk and prioritised the risks based on their severity.

A comprehensive risk assessment framework is developed that could be used under uncertainty. Initially, 16 risks are identified related to the HSM. Further, the identified risks are prioritised using the severity of the risks. The high-priority risk is “raw material status”, “raw material wholesomeness” and “origin of raw material” while “information integrity” and “people integrity” are low-priority risks.

HSM risk can be effectively assessed through the proposed framework. The proposed framework applied neutrosophic numbers to represent real-life situations, and it could be used for other supply chains as well.

The proposed method is effectively addressing the issue of linguistic subjectivity, inconsistent information and uncertainty in the expert’s opinion. A case study of the HSC is adopted to illustrate the efficiency and applicability of the proposed risk framework.

With the growing economic impact of e-commerce and the increasing trend of omnichannel mode, more considerate services can be provided to customers. This paper aims to explore the optimal practice of business strategies and enrich the research content of marine tourism omnichannel.

This paper studies the optimal practice of bundling pricing and service effort strategies between two tourism suppliers (TSs) and a travel agent (TA) who distributes complementary products in marine tourism omnichannel considering joint efforts of both sides. This study develops five models by Stackelberg and Nash game and introduces the revenue-sharing contract. All outcomes/results are analyzed and the corresponding numerical and sensitivity analyses are conducted to derive more managerial implications and business insights.

The main findings show that bundling price is directly proportional to inter-channel integration coefficient and service effort level coefficient, and inversely proportional to the price elasticity coefficient. TA tends to provide a higher level of service effort than TSs when TA plays a dominant role. Improving the service effort level unduly leads to a decline in profits. Moreover, TSs and TA can reach a win-win situation under the coordination mechanism and the marine tourism omnichannel can achieve the best performance.

A novel and useful approach towards joint equilibrium decisions of bundle pricing and service efforts in marine tourism omnichannel with complementary tourism products under different operational strategies is proposed.

This study presents a resource framework for reverse logistics operations based on widespread literature and industry experience. A common and comprehensive definition of resources that affect the implementation of reverse logistics is still patchy in the extant literature. Hence, a complete scheme, such as this, for the categorisation of resources that affect the implementation of reverse logistics is essential. This provides a useful information for business managers and extends reverse logistics research.

This study systematically identified, and validated with the help of reverse logistics experts, the resources that affect the implementation of reverse logistics.

The result is a classification scheme, termed as “reverse logistics resource matrix” (RLRM). This matrix offers a holistic overview of the resources that affect the implementation of reverse logistics for both scholars and practitioners.

To best of the researchers' knowledge, the RLRM is the first attempt to comprehensively structure the resources that affect the implementation of reverse logistics in an integrated framework.

The purpose of this study is to present a systematic literature review of academic peer-reviewed articles in English published between 2005 and 2021. The articles were reviewed based on the following features: research topic, conceptual and theoretical characterization, artificial intelligence (AI) methods and techniques.

This study examines the extent to which AI features within academic research in retail industry and aims to consolidate existing knowledge, analyse the development on this topic, clarify key trends and highlight gaps in the scientific literature concerning the role of AI in retail.

The findings of this study indicate an increase in AI literature within the field of retailing in the past five years. However, this research field is fairly fragmented in scope and limited in methodologies, and it has several gaps. On the basis of a structured topic allocation, a total of eight priority topics were identified and highlighted that (1) optimizing the retail value chain and (2) improving customer expectations with the help of AI are key topics in published research in this field.

This study is based on academic peer-reviewed articles published before July 2021; hence, scientific outputs published after the moment of writing have not been included.

This study contributes to the in-depth and systematic exploration of the extent to which retail scholars are aware of and working on AI. To the best of the author’s knowledge, this study is the first systematic literature review within retailing research dealing with AI technology.

The purpose of this paper is to focus on the pricing strategy for viral marketing in the context of the hotel industry and propose three typical price‐network size schedules to be tested. The paper further proposes controlled experiments in hotel industry context to examine the performance of those schedules.

A simulated hotel booking web site with basic reservation functions will be constructed. Participants should be randomly drawn and asked to encourage their friends from social networks to book with the participant to achieve a better price. Each participant will be given a special promotion code embedded with different pricing information. The number of customers attracted, total revenue and time to achieve a sell‐out was collected after the experiments.

These were the expected performance differences associated with different network pricing strategies in the viral marketing campaigns.

Since participants do not need to pay for the booking, it is difficult to control their intentions. Also, in the quantity discount scenario, individual preference may not be met.

First, this paper proposed the possible pricing strategy to promote the effectiveness of viral marketing in hotel industry. Second, by initiating business with customers directly through viral marketing, hotels can have more control over the inventory and product prices.

Through a better understanding in viral marketing from the perspective of network discount strategies, the marketing opportunities enabled by social networks can be extended. This paper suggests that employing a price‐network size schedule that offers deduction in price as network size increases may be an effective tool in attracting potential customers to make purchases in a timely manner. This paper further suggests experiments to test typical pricing schedules that could be utilized in practices. The proposed experiments can provide a deeper insight into the implementation of viral marketing for hotel managers.