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This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.

As buyers and suppliers seek to create value, they face the challenge of creating an environment that promotes coordination and information sharing and discourages opportunism. While the literature suggested dyadic mechanisms to create such an environment, this study focuses on ties beyond the buyer–supplier dyad. Specifically, close connections to one's partner's partners (CPP) are crucial in the realization of benefits for buyers and suppliers.

Drawing from embeddedness theory and governance theory, the authors developed a contingency framework to examine when CPP are beneficial or counterproductive considering two dyadic attributes – relational capital (RC) and partner dependence. Analyses were conducted using data from a dyadic survey complemented with archival data on 106 buyer–supplier relationships (BSRs).

The study reveals that CPP both help and hurt in the realization of benefits. Stark asymmetries exist between the impact of CPP on the buyer and supplier sides. For buyers, CPP exert a direct positive effect on operational and innovation benefits. For suppliers, the effect of CPP on operational and innovation benefits is contingent on buyer dependence and RC – CPP serves as a substitute for buyer dependence and RC. There are no such contingency effects for buyers. Further analysis identifies situations for suppliers when CPP hurt the realization of benefits.

The study highlights the importance of CPP to foster efficiency and innovation within BSRs and illustrates how their impact varies across contingency conditions and across the parties within a dyad.

This study aims to develop and test an integrative model that examines the effects of customization and perceived employee authenticity on customer delight, which in turn influences customers’ willingness to recommend (WTR) and willingness to pay a premium (WTPP) as outcomes in a hotel context. The moderating role of online review valence in this process is also examined.

This study adopts a 2 (customization: low vs high) × 2 (perceived employee authenticity: low vs high) × 2 (online review valence: negative vs positive) experimental design. A total of 409 US consumers were recruited and randomly assigned to a hotel check-in scenario. Partial least squares structural equation modeling was used to test the hypothesized relationships.

Findings confirmed the role of customer delight in mediating customization and employee authenticity on WTR and WTPP. In addition, perceived employee authenticity was a stronger driver of customer delight for consumers exposed to negative online reviews than for those exposed to positive reviews.

The findings provide useful guidance in designing efficient service strategies for generating a delightful customer experience. Hotel practitioners should provide customized services and manage employees in a way that helps them deliver authentic services that achieve customer delight. Understanding that customer expectations formed through online reviews play a significant role in service evaluations, hotel managers make an extra effort to monitor online reviews and manage customer expectations.

Although existing research suggests that customer delight plays an important role in positive consumer outcomes, there is still potential space to explore the theoretical mediational mechanisms underlying this effect and the moderating effect on this relationship between customer delight and consumer responses. This study contributes by testing the moderating impact of online review valence and the mediating impact of customer delight.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

This study aims to (1) validate the efficacy of contractual and relational governance in enhancing operational performance and (2) explore the influence of product complexity on the effectiveness of these governance mechanisms, thereby determining the optimal approach for varying levels of product complexity.

By utilizing a comprehensive theoretical framework encompassing transaction cost economics, social exchange theory and contingency theory, this research explores the intricate interplay between governance mechanisms, product complexity and operational performance, drawing insights from a dataset comprising 246 responses within Mainland China’s manufacturing sector. To rigorously test the proposed hypotheses, this study employed a hierarchical regression analysis.

The findings of this study are summarized as follows: (1) while both contractual governance and relational governance have a significant impact on operational performance, relational governance is found to be more effective than contractual governance in enhancing operational performance; and (2) the moderation effect of product complexity is evident, as it weakens the impact of contractual governance while simultaneously enhancing the positive influence of relational governance on operational performance.

The study uncovers a moderation effect of product complexity on the relationship between governance mechanisms and operational performance. This finding adds an original contribution to the literature by highlighting how product complexity can interact with governance strategies, providing practical insights for industries dealing with varying levels of product complexity.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.

This study examines the impact of servitization in the form of repair and maintenance services on consumers' quality perceptions, purchase intentions and recommendation intentions while considering consumer frugality as a moderator in the retail ready-to-wear sector.

A quantitative approach based on consumer research was pursued. Study 1 tested the research model using a fictitious ready-to-wear brand within an experimental design. To increase the generalizability of results, Study 2 retested the model with a well-known ready-to-wear brand. For both studies, regression, mediation and moderation analyses were conducted in SPSS.

Both studies showed that servitization positively influences perceived quality. Servitization positively affects purchase intentions and recommendation intentions indirectly via the mediating role of perceived quality. Frugality moderates the relationship between servitization and perceived quality for the fictitious brand (Study 1), whereas it is not significant for a well-known ready-to-wear brand (Study 2). Servitization positively influences perceived quality regardless of consumers' frugality levels for a stronger brand.

This study suggests and tests an original conceptual model that relies on signaling theory. It is among the first studies to examine the impact of servitization on retail fashion consumers' quality perceptions and consequent purchase and recommendation intentions. This study also contributes to the literature by presenting empirical findings based on consumer research on servitization while considering frugality as a moderator.

Bundling products with additional services can contribute to quality perceptions and consequently to purchase and recommendation intentions for ready-to-wear brands.

The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process and its impact on performance and second, introducing cardboard prototyping as a Kaizen tool offering a novel approach to testing and simulating improvement scenarios.

The study employs value stream mapping, root cause analysis, and brainstorming tools to identify root causes of poor performance, followed by deploying a Kaizen event to redesign and optimize an electronic device assembly process. Using physical models, bottlenecks and opportunities for improvement were identified by the Kaizen approach at the workstations and assembly lines, enabling the testing of various scenarios and ideas. Changes in lead times, throughput, work in process inventory and assembly performance were analyzed and documented.

Pre- and post-improvement measures are provided to demonstrate the impact of the Kaizen event on the performance of the assembly cell. The study reveals that implementing lean tools and techniques reduced costs and increased throughput by reducing assembly cycle times, manufacturing lead time, space utilization, labor overtime and work-in-process inventory requirements.

This paper adds a new dimension to applying the Kaizen methodology in manufacturing processes by introducing cardboard prototyping, which offers a novel way of testing and simulating different scenarios for improvement. The paper describes the process implementation in detail, including the techniques and data utilized to improve the process.

The purpose of this paper is to examine the contribution of big data in the transforming process of an IT firm to a learning organization.

The authors adopted a qualitative research approach to define and interpret the ideas and experiences of the IT firms’ employees and to present them to the readers directly. For this purpose, they followed a single-case study design. They researched on a small and medium enterprise operating in the IT sector in Düzce province, Turkey. This paper used a semi-structured interview and document analysis as data collecting methods. In all, eight interviews were conducted with employees. Brochures and website of the organization were used as data sources for the document analysis.

As a result of in-depth interviews and document analysis, the authors formed five main themes that describe perception of big data and learning organization concepts, methods and practices adopted in transforming process, usage areas of big data in organization and how the sample organization uses big data as a learning organization. The findings of this paper show that the sample organization is a learning IT firm that has used big data in transforming to learning organization and in maintaining the learning culture.

The findings contribute to literature as it is one of the first studies that examine the influence of big data on the transformation process of an IT firm to a learning organization. The findings reveal that IT firms benefit from the solutions of big data while learning. However, as the design of the research is single-case study, the findings may be specific to the sample organization. Future studies are required that examine the subject in different samples and by different research designs.

In literature, research on how IT firms’ managers and employees use big data in organizational learning process is limited. The authors expect that this paper will shed light on future research that examines the effect of big data on the learning process of the organization.