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The aim of this study is to gain important insights on integration oriented servitization identifying essential dimensions of effective structures, coordination approaches and management controls adopted by manufacturing firms that integrate forward towards distribution, sales and services.

The study adopts a theory-guided qualitative abductive methodology to conduct a comparative case-study of two manufacturing firms in the same industry integrating forward to enhance servitization but with significantly different performance outcomes. The findings are uncovered from a broad spectrum of primary and secondary data spanning two decades.

The consistently high-performing firm puts equal emphasis on production and downstream distribution, sales and services and motivate individuals to engage in entrepreneurial efforts to develop combined product-services offerings that are valued by customers. The underperforming firm prioritizes operating efficiency driven by engineering prowess and managed through planning, standardization, authority and central controls.

The study is based on two representative firms operating in a specific industry context, which has ramifications for the generalizability of results and calls for replication studies to substantiate and extend findings.

Forward integration from manufacturing into distribution, sales and services represents a specific servitization strategy that needs structure and particular coordination approaches to be effective in complex dynamic product-markets. The characteristics of the outperforming case company provide useful insights on effective integrated servitization efforts.

Forward integration is a commonly adopted strategy among manufacturing firms that constitute the backbone of modern economies and effective governance of these integration oriented servitization efforts has important implications for societal value creation.

This study builds on rationales from management science including economic theory, corporate strategy and different micro-foundational lenses and thereby hone recent calls for broader theoretical foundations to enlighten studies of the servitization puzzle.

The study aims to devise a comprehensive evaluation model (CEM) for evaluating spatial equity in the layout of elderly service facilities (ESFs) to address the inequity in the layout of ESFs within city center communities characterized by limited land resources and a dense elderly population.

The CEM incorporates a suite of analytical tools, including accessibility assessment, Lorenz curve and Gini coefficient evaluations and spatial autocorrelation analysis. Utilizing this model, the study scrutinized the distributional equity of three distinct categories of ESFs in the city center of Xi’an and proposed targeted optimization strategies.

The findings reveal that (1) there are disparities in ESFs’ accessibility among different categories and communities, manifesting a distinct center (high) and periphery (low) distribution pattern; (2) there exists inequality in ESFs distribution, with nearly 50% of older adults accessing only 18% of elderly services, and these inequalities are more pronounced in urban areas with lower accessibility, and (3) approximately 14.7% of communities experience a supply-demand disequilibrium, with demand surpassing supply as a predominant issue in the ongoing development of ESFs.

The CEM formulated in this study offers policymakers, urban planners and service providers a scientific foundation and guidance for decision-making or policy amendment by promptly assessing and pinpointing areas of spatial inequity in ESFs and identifying deficiencies in their development.

In the era of Industrial Revolution (IR) 4.0, the integration of digital technologies, automation and data-driven insights has generated a broad wave of transformation across all industries, including the insurance sector. The study focuses on determining how the adoption of these technologies (InsurTech) is changing the life insurance industry, ultimately enhancing the level of customer satisfaction.

The data analysis has been performed with 304 useable responses from the policyholders of life insurance in the north-west region of India. The methodology adopted for this study is partial least squares (PLS) structural equation modeling (SEM). To investigate the predictive relevance of customer satisfaction, the PLS predict technique has been used. Also, importance performance map analysis (IPMA) has been applied to assess the important and performing dimensions of customer satisfaction.

The outcomes show that the adoption of InsurTech has a positive impact on customer satisfaction. Customer service management and policy management are among the strongest predictors of customer satisfaction, and the predictive relevance is reported to be moderate. IPMA results have suggested that improvements in online distribution of insurance services and customer service management lead to higher customer satisfaction.

The conceptual model can be tested with the moderating effect of different demographic factors (age, gender etc.), and future research can be done to analyze the mediating role of customer satisfaction between InsurTech adoption and customer loyalty.

The study offers valuable contributions to the marketing literature, shedding light on the influence of InsurTech adoption on customer satisfaction within the Indian life insurance sector. The research offers a practical approach that could help marketing professionals and policymakers comprehend the utilization of online insurance services, and this understanding can help industry experts to develop customer-oriented products and services.

This research is the first of its kind to test the association between InsurTech adoption and customer satisfaction in the life insurance sector in the Indian context. Research also provides novel insights for policymakers to enhance the satisfaction of customers towards using online insurance services in the near future in developing countries like India.

Assess the realistic impacts of implementing an Radio Frequency Identification (RFID)/Internet of Things (IoT) uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms. The authors use a hybrid simulation framework to address the following objectives and challenges: a) reduce and control operating rooms’ level of inventory; b) stabilize scrubs’ demand and c) improve infection control and prevention of cross-contamination (through scrubs over manipulation and hoarding).

The authors adopt a Design Science approach. This methodological approach is used to design, develop, create and evaluate information technology “artifacts” (e.g. constructs, models, methods and instantiations) intended to solve organizational problems and make research contributions (Peffers et al., 2007). More specifically, the authors follow the Design Science Research Methodology process model which includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication.

To assess the realistic impacts of implementing an RFID-IoT uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms, the authors adopted a design science approach and initiated the research by documenting the business case and reviewed the existing literature to build a comparative analysis of existing uniforms’ distribution systems. The authors used a hybrid simulation model to assess the impact of three business cases: present mode of operation, implementing smart shelves or the smart distributors. The authors show that smart dispensers allow a greater control on scrubs’ utilization while eliminating the cross-contamination of the medical personnel.

Through this research study, the authors provide hospitals’ managers a scientific support for uniforms’ (scrubs) distribution process improvement. The authors use a hybrid simulation model to compare innovative solutions for uniforms’ distribution systems in the form of “smart cabinets” supported by Radio Frequency Identification (RFID)/Internet of Things (IoT) technologies and choose the most appropriate design for the hospital to meet two main challenges: a) inefficiency of uniform replenishment-distribution system and b) noncompliancy with infection control regulations caused by the distribution system.

From a methodological perspective, this paper addresses concerns from researchers calling quantitative research methods and using case-based research strategy to address IoT issues and assess the system in practice. From a broader point of view, this work confirms the predominant interest of RFID-IoT research work in the arena of supply chain management and logistics as the technology is used for tracking purpose and for monitoring applications. It is also one response to the research community suggesting that “hospitals should evaluate the medical effectiveness of the new technologies as well as the cost before adoption”.

With the vigorous development of the e-commerce delivery service industry, delivery service has become an important factor for e-tailers to obtain the market competitive advantage. However, how to choose the best delivery service strategy is a difficult problem for e-tailers in practice. The purpose of this paper is to investigate the effect of delivery service on e-tailers and online platforms.

Based on the Stackelberg game, the research study establishes and solves three models, namely dual self-supporting delivery service model, dual third-party delivery service model and differential delivery service model.

The results show that when the self-supporting and third-party delivery cost coefficients are all small, no matter which delivery service providers the competitor selects, the e-tailer selects delivery with a lower service fee. When the self-supporting and third-party delivery service fee are all low, no matter which delivery service providers the competitor selects, the e-tailer selects delivery with a smaller service cost. Both service fee and service cost determine the choice of e-tailers' delivery strategy. When the commission rate is moderate, both e-tailers are willing to choose the self-supporting delivery strategy, but the platform only prefers to provide self-supporting delivery to them with a lower delivery service sensitivity coefficient.

This paper analyzes the optimal delivery service strategies for e-tailers to compete with competitors, and explores the impacts of parameters for e-tailers and online platforms in their decision-making. The findings provide valuable implications for relevant practices.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

Photos are powerful tools to attract individuals’ attention and convey service experiences. Yet exactly how visual cues in a photo contribute to the perceptions of the staged servicescape, and how these perceptions inspire online booking/reservation behaviors, remains underexplored. Addressing the gap, this study aims to uncover (1) how perceptual information mediated by an online photo contributes to the formation of consumers' holistic perceptions of the service environment and (2) how such consumers' holistic perceptions further influence customers' online purchasing behaviors.

This research adopts an innovative crowdsourcing approach and refers to field data on consumers' online hotel booking behaviors to examine relationships among inferred servicescape dimensions, consumers' holistic perceptions of the mediated servicescape and their actual online booking/reservation behaviors (e.g. page-view and meta-click behaviors).

Confirmatory factor analysis and path analysis indicated that five mediated servicescape dimensions (i.e. color, lighting, furnishings, layout and style) contribute significantly to consumers' perceptions of the mediated servicescape (CPMS) and exert different impacts on CPMS. Connecting the crowdsourced rating and consumer behavioral data, CPMS is found to influence consumers' aggregated page-view and meta-click behavior, especially in the US market.

Building upon servicescape theory, the medium theory and the online booking literature, this research proposes a novel conceptual framework of CPMS to theorize the process by which visual cues in online photos contribute to CPMS and subsequent online purchase behaviors. Findings from this research extend Bitner's servicescape framework to mediated service contexts and provide practical implications for promoting service businesses.

The vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.

In this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).

The results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value <0.04. However, MOSA and NSGA-II algorithms have better performance in terms of central processing unit (CPU) time index. In addition, there is no meaningful difference between the algorithms in terms of MID and diversity indexes. Finally, the impacts of changing the parameters of the model on the results are investigated by performing sensitivity analysis.

In this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.

This study aims to investigate the determinant factors as barriers and facilitators of the B2B degree of digital use and customer–brand engagement in travel services by applying technology and behavioral theories.

A face-to-face survey was administered to retail travel agencies offering offline and online services (N = 301). Structural equation modeling using the partial least square method was conducted using Smart PLS 3.0 software to examine the proposed hypotheses in the research model.

The results revealed that the integrated composite model significantly predicts the B2B degree of digital use and brand engagement. The study established that facilitators had positive effects, and barriers negatively impacted the degree of digital use, which positively impacted brand engagement. However, the facilitator perceived cost and barrier lack of critical mass showed the opposite influence. The effects of facilitator customer pressure and barriers, information and communication technology infrastructure problems and security risks were insignificant. The results also established that the buyer firm size moderated the relationships between barriers and facilitators with the degree of digital use.

This study combined technology and behavioral theories to explain the buyer–seller relationship. The expanded framework contributed to understanding B2B digital usages and brand engagement in the seller–intermediary relationship. This study conceptualized firm size as a contingency variable and established its moderating effect. The study defined cost as a formative construct and an organizational factor. The study suggested practical implications for travel agencies and online travel service sellers.