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This article examines challenges and strategies related to police relationships and engagement with rural communities in England and Wales, Australia and France. It aims to bridge a gap in knowledge around how police balance public demands with organisational and contextual constraints, exploring the role of communication technology in overcoming geographical and cultural barriers in rural policing.

The research draws upon 121 semi-structured interviews conducted across three distinct jurisdictions. In the United Kingdom and Australia, interviews were conducted via Microsoft Teams, while face-to-face interviews were conducted in France. Participants were recruited through the purposive sampling of police working in rural areas. The data were thematically analysed using NVivo Software.

Rural communities have low expectations of policing services, a consequence of geography, organisational structures and limited resource allocation. Building relationships can be challenging owing to isolation and terrain, the need for officers to have local and cultural knowledge, and difficulties in recruiting officers in rural posts. Technology-mediated communication has played a part in the solutions (e.g. social media). However, this may not always be suitable owing to limited connectivity, citizen and police preferences for communication and engagement, and the institutional and cultural nuances surrounding the application of technology.

This article provides empirical insights into the attitudes and experiences of rural police officers, highlighting the distinctive policing context and engagement needs of rural communities. The research underscores the necessity for contextually aware engagement. It suggests that while technology-mediated communication offers some solutions to spatial challenges, its effectiveness may be limited by access, generational preferences and the adaptability of police institutions and cultures.

This study aims to apply the service robot acceptance model (sRAM) to examine how attitude toward human–robot interaction (HRI) and engagement influence consumer acceptance of service robots in a frontline setting.

Data was collected from 255 visitors who interacted with a robotic tour guide at a city museum. The data was analyzed using smart PLS 4.0.

The findings show the positive effects of subjective norms, appearance, perceived trust and positive emotion on both attitude toward HRI and engagement. In addition, social capability impacted attitude toward HRI, whereas perceived usefulness affected engagement.

To deliver engaging museum experiences that bring about positive word-of-mouth and intention to visit, managers need to incorporate the sRAM dimensions in the design and deployment of service robots.

This research uses field data to empirically validate the sRAM in the context of service robot acceptance. It introduces engagement as a novel mediating variable, enriching current understanding of human-like qualities in HRIs.

With the development of digitalization and interconnection, there is a growing need for enterprise customers to ensure the compatibility of the third-party components they are using in the manufacturing process, thus raising the integration requirements for the Industrial Internet platform and its third-party developers. Therefore, our study investigates the optimal integration decision of the Industrial Internet platform while considering its access price, the integration cost, and the net utility derived by enterprise customers from the third-party components.

We model a two-sided Industrial Internet platform that connects customers on the demand side to the developers on the supply side. We then explore the integration decision of the Industrial Internet platform and its important factors by solving the optimal profit function.

First, despite the high integration cost of third-party developers, the platform still chooses to integrate when enterprise customers derive high utility from the third-party components. Second, due to the compatibility effect, charging the enterprise customers a higher price may reduce the platform profits when these customers derive low utility from the third-party components. Third, the platform profits will increase along with the integration cost of third-party developers when it is low in the case where enterprise customers derive low utility from third-party components.

Our findings offer insightful takeaways for the Industrial Internet platform when making integration decisions.

This study aims to analyze how ready a firm is to transform into Industry 4.0 using the Readiness Index (INDI 4.0) assessment. It also investigates the differences (before and after) of the program “Making Indonesia 4.0” in 2018 in socioeconomic and demographic aspects.

The INDI 4.0 assessment involved a self-evaluation by 622 companies across 13 industry sectors, subsequently verified by the Ministry of Industry. This study incorporates discussions with industry experts to enhance the interpretation of the analytical findings.

This study explores the interrelation among the components of INDI 4.0 across different levels, assessing the readiness of each sector for Industry 4.0. The findings reveal the diverse impact of implementing Industry 4.0 in Indonesia on socioeconomic and demographic aspects. Furthermore, the study proposes several policy recommendations for the Indonesian government’s consideration.

This study’s scope is confined to the industrial context of Indonesia, as the assessment components are tailored to the specific characteristics and culture of the country’s industry. Subsequent research endeavors can leverage this study as a foundational reference, adapting the components to align with the particular interests of other nations.

Businesses, especially those in Indonesia, can employ these findings to evaluate their position in the context of Industry 4.0 transformation compared to their industry. Simultaneously, the Indonesian government can use these results as a starting point to evaluate and potentially enhance their policies related to Industry 4.0. We recommend five policy proposals for the Indonesian government: diversifying measurement models, shifting terminology, emphasizing soft skills, promoting continuous learning and implementing Center of Digital Industry Indonesia 4.0 (PIDI 4.0) initiatives.

This study offers a broad impact of Industry 4.0 implementation in socioeconomic and demographic aspects in Indonesia, such as income, job-shifting, age, educational background and gender.

To the best of our knowledge, no prior research has explored the repercussions of industrial implementation on socioeconomic and demographic facets.

This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.

This paper aims to investigate the impact of gender diversity in the composition of engagement auditors on the disclosure of key audit matters (KAMs) in a dual-signature environment.

The authors used the unique institutional setup of Taiwan, where the law requires that audit reports be signed by two audit partners. The authors examined the effect of gender diversity composition among engagement auditors on KAM disclosure, considering behavioral differences between female and male auditors.

The empirical results indicate that gender diversity composition in the dual-signature environment is associated with the number of disclosed KAM items (KAMIT) and the length of the explanations for each KAMIT. Furthermore, the authors found that gender diversity composition, particularly when led by female audit partners, has a more pronounced impact on the explanation of each KAMIT rather than on the disclosure of KAMIT. The authors also noted that the moderating effect of audit firm specialization does not influence the gender diversity composition of audit partners in disclosing KAMs.

This study’s empirical findings demonstrate that the interaction between different gender compositions in a dual-signature environment influences KAM disclosure.

This paper aims to investigate associations between firm resources and reliance on entrepreneurial marketing (EM) channels among agrofood ventures. It accounts for agropreneur gender and racial/ethnic status in the context of marketing channel portfolio composition. The authors examine the established assumption that resource limitations drive EM and whether socially disadvantaged status of agropreneurs is associated with marketing strategy beyond standard resourcing measures.

Using 2015 Local Foods Marketing Practices Survey data, the authors apply linear regression to investigate differences in the use of EM channels, accounting for resources, social status and other factors.

Limited-resource ventures rely more on consumer-oriented channels that require EM practices. Socially disadvantaged entrepreneurs favor these channels, even when accounting for resources. Notably, ventures headed by men of color rely more on the most customer-centric local foods marketing channel.

Future research should investigate how social and human capital influences the use of EM.

Entrepreneurial support policy and practice for agropreneurs should be cautious about the “double-burden” folk theorem of intersectional disadvantage and review how to best direct resources on EM to groups most likely to benefit.

This paper uses a unique, restricted, nation-wide, federal data set to examine relationships between resource endowments, social status and the composition of agrofood enterprises’ marketing channel portfolios. To the best of the authors’ knowledge, it is the first to include racial- and ethnic-minority status of agropreneurs and to account for intersectionality with gender.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.

This study explores the mediating effects of relationship marketing orientation (RMO) and service quality orientation (SQO) on market orientation, selling orientation, and policyholder retention in non-life insurance services. Additionally, it offers important recommendations for non-life insurers in Taiwan for policy development and improving policyholder retention.

Data were collected from a sample of policyholders belonging to the top five non-life insurance companies in Taiwan. The data were then analyzed with structural equation modeling.

RMO and SQO mediate the effects of the salesperson’s market orientation on policyholder retention. Thus, RMO and SQO are key factors influencing policyholder retention. Consequently, high levels of market orientation should be maintained to increase RMO and SQO, strengthening the retention rate of non-life insurance policyholders.

The main limitation of this study is its cross-sectional nature. In the future, researchers should collect data from other countries and service industries (e.g. banks, securities, and other financial institutions), expand to different insurance contexts (e.g. life insurance), and conduct longitudinal studies or experimental research.

The results of this study can act as a guide for providers of non-life insurance services. Based on the research results, we recommend decision-makers pay increased attention to increasing policyholder retention rates by strengthening their firm’s RMO and SQO.

Few studies have investigated the relationships among market orientation, selling orientation, RMO, SQO, and policyholder retention in non-life insurance services within Asian contexts in general and specifically in Taiwan. Thus, this study’s theoretical contributions, managerial implications (especially for decision-makers), and the proposed future research directions represent timely and valuable additions to the literature.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.