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The purpose of this study is to examine the politics, political economy, and fallout of hawkish regulatory policy on prescription drugs in Nigeria. Hawkish regulatory policy on prescription drug in Nigeria, such as opioid analgesics, is a very complex and multifaceted one, which usually involves the interplay of many factors and parties.

Policy manuals, official government gazettes (legislations, regulations, Acts and decrees), academic literature and a direct ethnographic observation of events surrounding the regulation of prescription drugs were reviewed and engaged.

The results revealed that Nigerian and global political economy and politics interface to define the direction of the new restrictive opioid policy, with resultant friction between prohibition and consumption. The reviews showed that the overarching “get-tough” and “repressive” policy are not necessarily founded on empirical evidence of an increase in prescription drug sales or use, but more as a product of the interplay of both internal and external politics and the prevailing socioeconomic order.

Instead of borrowing extensively from or being influenced by repressive Western drug laws and perspectives, Nigerian policymakers on prescription opioids should take control of the process by drawing up a home-grown policy that is less intrusive and punitive in nature for better outcomes. A mental sea change is required to understand the intrigues of Western power in Nigeria’s politics and political economy to avoid the continuous symptomatic failure of drug policy.

The politics and economic influence of the United Nations, USA and Western powers, as well as the axiom of moral panic of prescription drugs scares within the Nigerian environment, are particularly significant in the making of the emerging hawkish policy on prescription drugs in Nigeria.

The COVID-19 pandemic has resulted in significant changes in consumer behaviour, which has had a cascading effect on consumer-centric logistics. As a result, this study conducts a focused literature review of pandemic-related consumer behaviour research to address two research questions: 1) what are the pandemic's direct effects on consumer consumption behaviour, with an emphasis on changes in their basic and psychological needs? and 2) what are the consequences of behavioural changes on consumer-centric logistics?

The scientific procedure and rationales for systematic literature review (SPAR-4-SLR) protocol and the theory, context, characteristics and methodology (TCCM) framework were adopted as a guideline to map, refine, evaluate and synthesise the literature. A total of 53 research articles were identified for further analysis.

Using Maslow's hierarchy of human needs as a theoretical guide, this review synthesises the COVID-19 pandemic's effects on consumer behaviour into four categories: abnormal buying behaviour, changes in consumer preferences, digitalisation of shopping behaviour and technology-related behaviour. Furthermore, four consumer-centric logistics propositions are proposed based on the four aspects of consumer behavioural changes.

This study outlines the significant behavioural changes in consumers in the face of the COVID-19 pandemic and how these changes impact consumer-centric logistics, with implications for managing consumers' involvement in logistics and pointing out future research directions.

Transport industry is one of the leading accident causing industries all around the world. Personal attributes like educational qualification, work experience, marital status, consumption of alcohol, hours of work of bus drivers are known to influence such accidents. In the present study, the mediating effects of Workplace spirituality (WS) among bus drivers between the personal attributes and job performance variables like self-reported accidents, punishments and appraisals are carried out.

A mediation analysis of WS between personal attributes and job performances of bus drivers is conducted in the present study. Since there is scarcity of studies measuring the effects of WS of bus drivers, new scale to measure WS was developed. The study was carried out in the government-owned road transport corporation in the state of Kerala, India. Responses were obtained from 617 male drivers.

Using exploratory factor analysis, four factors were identified, namely Meaningful work, Sense of Community, Mindfulness and Compassion. Confirmatory factory analysis provided good fit. The intercorrelations of personal attributes of drivers (independent variables) between WS factors (mediating variables) and job performance variables (dependent variables) were found out. Mediation analysis showed complete mediation of WS factors between marital status, alcohol consumption, hours of work and job performances like number of self-reported accidents, punishments and appraisals. The WS levels of drivers decrease significantly as working hours per goes beyond the legally allowed working hours.

Psychological attributes like Mindfulness, Sense of community, compassion etc. which are collectively known as WS influence the job performances of employees in other industries. Yet it is not studied in the transport industry. Hence, in the present study, the levels of WS are studied among bus drivers of government-owned road transport corporation in the State of Kerala, India.

The purpose of this study is to understand the complex interplay between illicit opioids trade and consumption practices and state policies that aim to reduce their misuse.

The study adopted an exploratory design. Data were gathered through in-depth interviews with 31 commercially oriented drug dealers in Uyo, Nigeria. The framework approach was used in data analyses, while “friction” provided the interpretive lens.

Accounts revealed public concerns over the misuse of tramadol and other opioids among young people and the associated health and social harms. These concerns provided support for enforcement-based approaches to prescription opioids control, including police raids on pharmacy stores. These measures did not curtail opioids supply and consumption. Instead, they constrained access to essential medicines for pain management, encouraged illegal markets and fuelled law enforcement corruption in the form of police complicity in illegal tramadol trade.

The findings reveal the frictions of drug control in Nigeria, wherein enforcement-based approaches gained traction through public concerns about opioids misuse but also faced resistance due to the persistence of non-medical use and illegal supply channels made possible by law enforcement complicity. These indicate a need to prioritize approaches that seek to reduce illegal supply and misuse of opioids while ensuring availability of these medications for health-care needs.

The study is unique in its focus on the creative tension that exists between state control measures and local opioids supply and consumption practices.

Vehicle allocation problems (VAPs), which are frequently confronted in many transportation activities, primarily including but not limited to full truckload freight transportation operations, induce a significant economic impact. Despite the increasing academic attention to the field, literature still fails to match the needs of and opportunities in the growing industrial practices. In particular, the literature can grow upon the ideas on sustainability, Industry 4.0 and collaboration, which shape future practices not only in logistics but also in many other industries. This review has the potential to enhance and accelerate the development of relevant literature that matches the challenges confronted in industrial problems. Furthermore, this review can help to explore the existing methods, algorithms and techniques employed to address this problem, reveal directions and generate inspiration for potential improvements.

This study provides a literature review on VAPs, focusing on quantitative models that incorporate any of the following emerging logistics trends: sustainability, Industry 4.0 and logistics collaboration.

In the literature, sustainability interactions have been limited to environmental externalities (mostly reducing operational-level emissions) and economic considerations; however, emissions generated throughout the supply chain, other environmental externalities such as waste and product deterioration, or the level of stakeholder engagement, etc., are to be monitored in order to achieve overall climate-neutral services to the society. Moreover, even though there are many types of collaboration (such as co-opetition and vertical collaboration) and Industry 4.0 opportunities (such as sharing information and comanaging distribution operations) that could improve vehicle allocation operations, these topics have not yet received sufficient attention from researchers.

The scientific contribution of this study is twofold: (1) This study analyses decision models of each reviewed article in terms of decision variable, constraint and assumption sets, objectives, modeling and solving approaches, the contribution of the article and the way that any of sustainability, Industry 4.0 and collaboration aspects are incorporated into the model. (2) The authors provide a discussion on the gaps in the related literature, particularly focusing on practical opportunities and serving climate-neutrality targets, carried out under four main streams: logistics collaboration possibilities, supply chain risks, smart solutions and various other potential practices. As a result, the review provides several gaps in the literature and/or potential research ideas that can improve the literature and may provide positive industrial impacts, particularly on how logistics collaboration may be further engaged, which supply chain risks are to be incorporated into decision models, and how smart solutions can be employed to cope with uncertainty and improve the effectiveness and efficiency of operations.

The development of digital technology has provided technical support to various industries. Specifically, Internet-based freight platforms can ensure the high-quality development of the logistics industry. Online freight platforms can use cargo transportation insurance to improve their service capabilities, promote their differentiated development, create products with platform characteristics and increase their core competitiveness.

This study uses a generalised linear model to fit the claim probability and claim intensity data and analyses freight insurance pricing based on the freight insurance claim data of a freight platform in China.

Considering traditional pricing risk factors, this study adds two risk factors to fit the claim probability data, that is, the purchase behaviour of freight insurance customers and road density. The two variables can significantly influence the claim probability, and the model fitting outcomes obtained with the logit connection function are excellent. In addition, this study examines the model results under various distribution types for the fitting of the claim intensity data. The fitting outcomes under a gamma distribution are superior to those under the other distribution types, as measured by the Akaike information criterion.

With actual data from an online freight platform in China, this study empirically proves that a generalised linear model is superior to traditional pricing methods for freight insurance. This study constructs a generalised linear pricing model considering the unique features of the freight industry and determines that the transportation distance, cargo weight and road density have a significant influence on the claim probability and claim intensity.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.