Search Results (25)

In the current economic context, organizations providing automotive repair services face significant challenges in ensuring service quality, operational efficiency, and long-term sustainability. This paper examines the importance of implementing process monitoring systems through the integration of European quality frameworks and Japanese operational principles such as Kaizen, Lean Manufacturing, and Poka-Yoke, to improve the quality of services and increase performance within automotive repair organizations. The research is grounded in Sustainable Development Goals (SDG 9—Industry, Innovation and Infrastructure, and SDG 12—Responsible Consumption and Production), demonstrating how structured quality practices contribute to reducing waste, optimizing processes, and delivering responsible services. The main objectives of the study are to identify the elements that influence the performance of service-specific processes, to improve the quality management practices related to these processes, to eliminate non-conformities, and to enhance profitability and competitive differentiation through service quality assurance. A mixed-methods research design was applied, including direct participatory observation, performance monitoring, and correlational statistical analysis over a six-month period in two Romanian automotive service centers. Key performance indicators (KPIs) such as technician efficiency, rework rate, and order throughput time were collected and analyzed before and after the implementation of selected tools. Findings demonstrate measurable improvements: rework rates decreased from 7.8% to 2.6%, technician efficiency improved from 89% to 105%, and average service completion time was reduced by 1.6 days. Correlation analysis confirmed strong relationships between visual management adoption and rework reduction (r = −0.75), as well as between Lean implementation and technician efficiency (r = +0.89). The study’s novelty lies in its integration of cross-cultural quality management practices into a replicable and sustainable operational model for post-sale service environments. The results validate that implementing monitoring systems, combined with Kaizen, Lean, and Poka-Yoke, supported by visual management and active employee engagement, can lead to superior service quality management, increased customer satisfaction, and long-term organizational success in the automotive repair industry. Full article

E-commerce platforms offering regional fresh produce often face a trade-off between logistics costs and product quality. Due to limited use of cold chain logistics, consumers frequently receive damaged goods, resulting in negative post-purchase experiences. This study examines how logistics service encounters, as reflected in consumer reviews, influence subsequent purchase behaviour, and how the alignment between review images and text moderates this relationship. We analyse sales and review data from 694 fruit products on Tmall between February and April 2024. Latent Dirichlet Allocation (LDA) is applied to extract logistics-related review content. At the same time, image–text similarity is assessed using the Chinese-CLIP model. Regression analysis reveals that positive logistics service encounters significantly enhance purchase intention. However, high image–text similarity weakens this positive effect, suggesting that overly repetitive content may reduce informational value for prospective buyers. These findings advance understanding of consumer behaviour in online fresh produce markets by highlighting the interactive effects of logistics experiences and user-generated content. The results offer practical implications for improving logistics services, enhancing content diversity in review systems, and increasing consumer trust in e-commerce environments. Full article

Background/Objectives: Military Service Members (SMs) require optimal nutrition to support health, readiness, and job performance. However, they often fall short of meeting nutrition guidelines. This study aimed to determine the impact and feasibility of implementing the U.S. Marine Corps (USMC) “Fueled to Fight^®” (F2F) nutrition program in non-appropriated fund (NAF) food venues. Objectives included evaluating changes in Military Nutrition Environment Assessment Tool (mNEAT) scores, feasibility of implementing and maintaining F2F strategies, and influence on customer purchasing patterns. Methods: Researchers conducted a pre-post interventional study from January to December 2024 at three NAF food venues across two USMC bases. F2F strategies, including identifying items using a stoplight color coding system (Green = healthy, Yellow = less healthy, Red = least healthy), menu revisions, food placement, promotion, and marketing, were implemented. Data included mNEAT assessments, sales reports, and stakeholder focus groups. Generalized Estimating Equations models were used to analyze sales data. Results: mNEAT scores increased across all venues post-intervention. Availability and sales of Green items increased, while sales of Red items decreased in some venues. Profit increased at all three food venues. Focus groups revealed feasibility and provided insights for future interventions. Conclusions: F2F interventions in NAF food venues are feasible and can positively impact the food environment and customer purchasing patterns without negatively affecting profit. This study highlights the importance of integrating nutrition programs into all military food venues, not just government-funded dining facilities, to support the nutritional fitness and readiness of SMs. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

This study aims to present an integrated approach to customer experience, which was developed considering the identification and application of essential factors from the product life cycle. The study was conducted in the automotive industry and may be transferable to other products with high complexity and medium–long in-service use. The main goal is to identify the determining factors and perform a regression analysis of the effect of attribute-level performance on overall customer satisfaction through the customer’s entire journey during the product development phase. This study is based on a generic example that is meant to capture trends influencing customer satisfaction in the launch of a new product vehicle, focusing on factors that influence each stage of the process, from planning–exploration, design and development, and manufacturing and validation to performance measurement and after-sales assistance. Based on multiple surveys that were used as the main instruments for measuring the level of customer satisfaction at defined touchpoints, the product life cycle was followed through several stages: prospecting survey, upstream survey, launch preparation survey, post-launch investigation, life cycle survey, and after-sales support. Three meta-factors were identified—design, price, and durability—for which the ordinal regression demonstrated that they are significant predictors of customer experience in general. The approach may be transferable to other sectors by identifying relevant attributes and adapting tools for measuring customer satisfaction, customer experience, and consumer concerns, which act as key vectors influencing the product life cycle and, by extension, business sustainability. Full article

Digital technologies represented by AR (Augmented Reality), VR (Virtual Reality), and digital twins, along with the expansion of metaverse platforms and digital marketing concepts, have attracted the attention of numerous sports fashion product consumers and brands, particularly in the category of sports shoes. Therefore, in the context of digital technologies, understanding the factors that affect consumer experience and the preferences in the online purchasing process of sports shoes is very important. This study employs Latent Dirichlet Allocation topic analysis to analyze 44,110 online user posts and comments from social platforms, extracting thematic elements of consumer experience needs for purchasing sports shoes online. The information obtained is further encoded and designed into a questionnaire, which is then utilized alongside the Kano model to analyze the overall preferences of consumer experience needs. The results indicate that webpage design and basic product information are considered as Must-be attributes for user experience needs; providing information on after-sales service policies and product comment, products’ special feature information, and online size testing are recognized as Performance attributes. Additionally, high-tech interaction methods, visual presentation, personalized customization, virtual try-on, apparel matching recommendations, and dressing scenario recommendations are identified as Attractive attributes. The study reveals that in the context of new digital technology development, the online shopping experience for sports shoes is enhanced across four dimensions: platform experience augmentation, product experience augmentation, user demand augmentation, and interactive experience augmentation. These four dimensions collectively constitute the holistic experience design for the online retail platform. Therefore, this research provides case references and theoretical insights for researchers and developers in the fields of brand marketing, experience design, and product service innovation. Full article

The attributes that people consider when selecting an online travel agency (OTA) change over time, affecting how consumer satisfaction and loyalty evolve. However, attribute weight shifts in subsequent OTA visits cannot be determined using a cross-sectional approach. Thus, this study investigates the linkage dynamics between satisfaction and loyalty intentions as OTA attribute weights shift over time. We also assess the moderating effect of sales promotions on this linkage across subsequent OTA visits. Using a two-time-lag survey methodology (T1 and T2), we obtain 329 usable data. Our findings demonstrate that the link between satisfaction and loyalty intentions strengthens as customers gain more experience with an OTA. While price and post-service quality weights increase sharply, the weights of security and ease of use decrease. Furthermore, our findings show that the moderating effect of sales promotions does not appear in the early stages of a consumer’s experience with an OTA. Our research is the first to offer a complete understanding of the role of sales promotions based on the satisfaction–loyalty mechanism, considering a broad range of OTA selection attributes. Full article

Japanese prefabricated housing manufacturers have gained international recognition for their innovative approaches to the whole design process, ranging from initial design to innovative cutting-edge technologies, state-of-the-art automated production lines, meticulous workmanship, and mass customisation. In this entry, three manufacturers (Daiwa House, Sekisui House, and Misawa Homes) were selected as case studies for close examination. By studying these leading companies, researchers and industry professionals can gain valuable insights into best practices, challenges, and innovations within the Japanese prefabricated housing sector. The research methods involved a desktop study of available information on websites, articles, and reports, as well as undertaking two study tours on residential sustainable design in Japan in 2022 and 2023. These three manufacturers were discussed and compared with respect to their development trajectories, design customisation, research capabilities and technological advancements, sustainable initiatives and procurement, as well as their after-sale services. They have demonstrated their adaptability and flexibility in response to natural disasters and the transformation of the needs in society. They are all keen on reducing the environmental impacts of their work towards zero carbon emissions and a sustainable future. Full article

Building on our “Critical Inventory”, we analyse the need for the regulation of PVC plastics in the EU and its member states. To this end, we checked the three phases of the life cycle of PVC plastics: production, use and end-of-life. In the production phase, we focus on the economic relationships between PVC and chlor-alkali electrolysis, in particular, the dependence on the chlorine market and PVC sales. For the use phase, the health and environmental risks posed by many PVC additives are particularly relevant. The European Chemicals Agency (ECHA) has submitted well-founded proposals for the regulation of individual or defined groups of substances (e.g., ortho-phthalates), which we support. Problems that put a ban on the agenda stem in particular from the end-of-life phase of PVC plastics (PVC compounds), especially in the construction sector. Due to their long service life, a stock of around 160 million tonnes of PVC products in the EU has built up, increasingly finding its way into the waste management sector. Currently, there are no waste management infrastructures or facilities capable of disposing of these amounts. Without a phasing-out the production of virgin PVC (“PVC ban”), these quantities will continue to increase. We, therefore, come to the conclusion that post-consumer PVC should be collected separately and canalized into a disposal infrastructure designed for chlorine recovery. The European PVC industry “would welcome to make this separate collection mandatory”. Including the associated costs, PVC will probably lose its status as a particularly economically favourable plastic. Full article

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts. Full article

In the current scenario of fast technological advancement, increasingly characterized by widespread adoption of Artificial Intelligence (AI)-driven tools, the significance of autonomous systems like chatbots has been highlighted. Such systems, which are proficient in addressing queries based on PDF files, hold the potential to revolutionize customer support and post-sales services in the automotive sector, resulting in time and resource optimization. Within this scenario, this work explores the adoption of Large Language Models (LLMs) to create AI-assisted tools for the automotive sector, assuming three distinct methods for comparative analysis. For them, broad assessment criteria are considered in order to encompass response accuracy, cost, and user experience. The achieved results demonstrate that the choice of the most adequate method in this context hinges on the selected criteria, with different practical implications. Therefore, this work provides insights into the effectiveness and applicability of chatbots in the automotive industry, particularly when interfacing with automotive manuals, facilitating the implementation of productive generative AI strategies that meet the demands of the sector. Full article

Although low-income homeownership programs serving vulnerable families at the lower end of the income distribution have been the focus of housing policy in many countries over the past 50 years, little is known about the post-origination experiences of immigrant families participating in these programs. Notably absent from the extant literature are studies examining the sustainability of homeownership among immigrant homebuyers and their susceptibility to falling behind on payments and experiencing mortgage defaults, evictions, or short sales. Utilizing data from 8263 families participating in Norway’s Starter Mortgage Program (Startlån) during the first three calendar years after mortgage origination, we examine the extent to which serious mortgage arrears varies by immigrant background. Two primary questions shape our research: (1) What is the incidence of serious mortgage arrears among Western, Eastern European, and non-Western immigrant homeowners relative to ethnic Norwegians participating in a public low-income homeownership program? and (2) What pre- and post-origination characteristics of applicants and households, mortgage terms at the time of origination, and experiences of household financial vulnerability or economic shocks predict heterogeneity in serious mortgage arrears by immigrant backgrounds? We found that 6.1% of ethnic Norwegian, 6.2% of Western, 4.9% of non-Western, and 3.2% of Eastern European immigrant homeowners participating in the Starter Mortgage Program were in serious mortgage arrears at least once during the first three calendar years after mortgage origination. Results from our negative binomial regression analyses suggest that program participants who were sole owners, with larger families, and higher debt were more likely to experience serious mortgage arrears; these effects were accentuated for ethnic Norwegians. Additionally, mortgage terms at the time of origination produced differential effects by immigrant background. Compared to Western and Eastern European immigrant homeowners, ethnic Norwegians were more likely to have experienced serious mortgage arrears if they purchased a single-family home, had larger LTV and DTI ratios, or if the Startlån share of their mortgages was higher. Non-Western immigrant mortgagors were more likely to make late mortgage payments if they had larger LTV ratios, interest-only mortgage servicing, or if they were more reliant on Startlån funds to finance their mortgages; however, this risk was reduced if they had fixed-rate mortgages. Financial vulnerability in terms of higher debt or fewer assets also increased the risk of serious mortgage arrears for ethnic Norwegians and non-Western immigrant homeowners, while increases in real wealth reduced that risk for all immigrant mortgagor groups. Full article

In recent years, e-commerce has assumed a more strategic and relevant role with regard to the good performance of the global retail industry. The facilities and conveniences provided by e-tail contribute to meeting the demands of a more connected consumer. However, it is necessary to consider the complex nature of the online market, which requires e-retailers to challenge themselves in order to create stronger connections with their customers to achieve greater customer satisfaction. In this context, the services offered in the after-sales phase, one of the most relevant stages of the consumer journey, can help reduce the negative impacts involved in the decision and experience of an online purchase. The post-sale in e-commerce is a research domain that has become increasingly prominent in recent years, and this maturation in the academic environment requires a deeper understanding of this scientific production. Therefore, to synthesize the main insights and suggest an agenda for future studies of this theme, a Systematic Literature Review (SLR) was conducted for this study using a hybrid approach, combining a structured review with a bibliometric review. One of the main contributions of the research is the seminal presentation of after-sales attributes in e-commerce, in a global and broad view, focusing on the retailer and consumer relationship, referred to herein as After-sales Customer Services (AFSCS). Unlike the other few existing reviews regarding the theme, this work is innovative because it investigates the e-commerce post-purchase, the attributes of this stage, and the relationship between retailer and consumer at this stage of the journey in a more direct, exclusive, and complete way. Full article

This study explored the dynamics of a residential property value premium for proximity to a light rail transit (LRT) station in the intermediate term (roughly two years) since the pandemic. We applied a longitudinal quasi-experimental design using repeat sales data from the Portland Metropolitan Area, Oregon. Our results indicate that the effect of the pandemic on prices of housing near LRT stations differs between single-family and multi-family markets. Since the pandemic outbreak, there has been no statically significant difference in the price appreciation between single-family (SF) housing within an LRT service area and otherwise similar SF homes; however, for multi-family (MF) homes, those within an LRT service area have experienced a 3.0% lower price appreciation rate than MFs outside such areas with similar characteristics. Our findings help better highlight the impact of the pandemic on the real estate market and can inform discussions about longer-term changes in post-COVID cities and their planning. Full article