Search results

The purpose of this paper is to study the profit optimization of manufacturers who provide personalized products to customers, thus avoiding additional operational costs and response times in the production process of personalized product design.

First, the authors present an integrated model for optimizing profit where the design and production of personalized products are both considered. The authors propose the concept of personalization level and four cases of personalization level are considered including top, high, medium and low levels. Polynomial-time optimal rules are provided for each level by analyzing 17 subcases where all possible personalized products are considered. Then, the authors compare the obtained profit of personalized products with the optimal profit of standardized products.

At low and high levels of personalization, personalized manufacturing is more profitable for specific products with specific characteristics. At the top and middle level of individuation, assuming that the product has certain characteristics, whether the best choice to produce personalized products depends on the level of individuation chosen by customers.

An important decision issue for manufacturers is whether to produce personalized or standardized products. In this study, the authors consider this decision problem and analyze the operational functions of personalized products. The authors hope to provide theoretical support in the operational management of manufacturers offering personalized products.

Considering the pervasiveness of technology, this article offers an understanding of how the metaverse can impact digital entrepreneurship. The objective will be to gather professional evidence on how the revival of this new technology can bring entrepreneurial development.

Through a qualitative study approach based on applying the metaverse in digital business contexts and analysing 533 practitioner sources from the Nexis Uni database, it will be possible to identify the concepts and application techniques of this emerging technology. The research adopts a qualitative methodology based on a mixed thematic and content review using tools such as ATLAS.ti and Leximancer.

This study finds three relevant macro-topics for metaverse and digital entrepreneurship (technology, immersive and design) and eight concepts (private solutions, digital twins, gamification, public solutions, new business worlds, co-design, collaborative spaces and stakeholders' participation). The uncovered elements demonstrate professionals' interest in a new mode of digital entrepreneurship using the metaverse. This interest highlights the commitment of companies and entrepreneurs toward discovering new services delivered in virtual and parallel worlds that find the creation of digital twins as their essence. Therefore, the study explores ongoing relationships for developing increasingly technically complex metaverse platforms and customer service offerings.

The study has some limitations as the selection of the database and the way the cases are focused on, which may be a stimulus for future studies. The analysis has innumerable theoretical and practical implications. In the first case, this research will shed light on an empirical case concerning the conceptual difference between innovation or greater transformation of business models through the metaverse. This work will directly contribute to the global discussion by identifying a model for applying emerging technology to digital entrepreneurs. Finally, from a practical point of view, the authors will provide new insights to digital entrepreneurs by showing them applications, best practices, and platforms they can use for their businesses.

On a practical level, the authors show practical opportunities coming from the metaverse for digital entrepreneurs. This study may inspire CEOs, managers, and future entrepreneurs to use the metaverse to expand their businesses by diversifying their services into numerous sectors.

To the best of the authors' knowledge, this study represents one of the first efforts to study the metaverse by framing it from theoretical and practical perspectives of digital entrepreneurship. Additionally, future research implications may guide researchers in this brilliant research field.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.

This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.

Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.

This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.

The purpose of this study is to examine how robotic anthropomorphism and personalized design may affect consumers' reactions to brands after service failure.

This study conducted two studies based on cognitive appraisal theory and artificial intelligence device acceptance theory. Study 1 explored the mechanisms by which the type of anthropomorphic design of the service robot (humanoid robot/nonhumanoid robot) influenced revisit intention after service failure through a one-factor between-subjects design based on a restaurant dining scenario. Study 2 was based on a hotel check-in scenario and explored the moderating effect of robot personalization design on the above mechanisms through a 2 (anthropomorphic design: humanoid robot/nonhumanoid robot) × 2 (personalized design: self-name/no name) between-subjects design.

Study 1 shows that consumers have higher performance expectations for nonhumanoid robots, leading to a higher tolerance for service failure, which in turn generates higher revisit intentions. Study 2 shows that consumers' performance expectations are significantly enhanced after custom naming of humanoid robots, so the serial mediation mechanism for the effect of robot anthropomorphic design on revisit intention does not hold.

This study extends the research of artificial intelligence device acceptance theory in the field of service failure and exploratively proposes an intervention mechanism for the negative effects of the anthropomorphic design of service robots.

This study aims to explore the potential of virtual positioning systems (VPSs) in revolutionising library access. It examines the benefits and challenges associated with implementing VPSs.

The study takes a comprehensive approach by analysing library users’ current challenges in accessing physical resources and services within traditional library settings. It analyses the benefits of VPSs in enhancing library access, considering factors such as improved navigation, accessibility for personalised recommendations, virtual tours and interactive experiences. The study also examines the implications of implementing VPSs regarding library resource management, staff training and infrastructure requirements.

The findings reveal that VPS has the potential to address various challenges faced by library users, such as limited availability of resources, inconvenient locations and inadequate access for individuals with special needs. VPS offers improved navigation, enhanced accessibility, personalised recommendations, virtual tours and interactive experiences. Implementing VPSs requires robust technological infrastructure, user adoption, privacy considerations and system maintenance. Libraries must invest in hardware, network infrastructure, staff training and data protection measures.

This study contributes to the ongoing discourse surrounding the transformation of libraries and the assimilation of emergent technologies. It highlights the potential of VPSs in revolutionising library access. By embracing the latent potential of VPSs, libraries can transcend physical boundaries, enhance user experiences and ensure seamless access to a wealth of resources in a digitised world.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

This editorial for the 6th World Conference on Production and Operations Management (P&OM) 2022 Special Issue delves into the transformative role of advanced artificial intelligence (AI)-driven chatbots in reshaping operations, supply chain management and logistics (OSCM). It aligns with the conference’s theme of exploring the intersection between P&OM and strategy during the Technological Revolution.

Utilizing a conceptual approach, this paper introduces the “ERI Framework,” a tool designed to evaluate the impact of AI-driven chatbots in three critical operational dimensions: efficiency (E), responsiveness (R) and intelligence (I). This framework is grounded in disruptive debottlenecking theory and real-world applications, offering a novel structure for analysis.

The conceptual analysis suggests immediate benefits of chatbots in enhancing decision-making and resource allocation, thereby alleviating operational bottlenecks. However, it sees challenges such as workforce adaptation and potential impacts on creativity and sustainability.

The paper suggests that while chatbots present opportunities for optimizing operational processes, organizations must thoughtfully address the emerging challenges to maintain productivity and foster innovation. Strategic implementation and employee training are highlighted as key factors for successful integration.

Bridging the gap between the burgeoning proliferation of chatbots and their practical implications in OSCM, this paper offers a first perspective on the role of AI chatbots in modern business environments. By providing insights into both the benefits and challenges of chatbot integration, it offers a preliminary view essential for academics and practitioners in the digital age.

Food delivery apps (FDAs) have rapidly developed due to mobile technologies, changes in modern consumption and increased online use during the COVID-19 pandemic. Despite this, the platforms are struggling to build a stable customer base and make a profit. This study combined tech-driven motivators in an information system success model (ISSM) and emotional attachment operated by affect transfer theory (ATT) to explain why people might continue to use an FDA.

An online quantitative cross-sectional survey was carried out via the Prolific platform. Data were collected from 416 FDA users and analyzed to test the hypotheses using the partial least squares based structural equation modeling.

The study found that system quality, information quality and emotional attachment directly influence the continued use of FDA. Moreover, the results suggest that perceived social interactivity and perceived playfulness are significant antecedents of emotional attachment.

The findings of this study enrich the current literature on continuance intention in online food delivery services by integrating the technology- and affective-based factors. The findings also provide various practical implications for app designers and marketers to foster an emotional bond with users, resulting in high retention rate.

The study proposes the integrated framework of ISSM and ATT for enhancing understanding of consumer behaviors in the post-adoption stage for FDA.