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Digital twin (DT) is an emerging technology that enables sophisticated interaction between physical objects and their virtual replicas. Although DT has recently gained significant attraction in both industry and academia, there is no systematic understanding of DT from its development history to its different concepts and applications in disparate disciplines. The majority of DT literature focuses on the conceptual development of DT frameworks for a specific implementation area. Hence, this paper provides a state-of-the-art review of DT history, different definitions and models, and six types of key enabling technologies. The review also provides a comprehensive survey of DT applications from two perspectives: (1) applications in four product-lifecycle phases, i.e. product design, manufacturing, operation and maintenance, and recycling and (2) applications in four categorized engineering fields, including aerospace engineering, tunneling and underground engineering, wind engineering and Internet of things (IoT) applications. DT frameworks, characteristic components, key technologies and specific applications are extracted for each DT category in this paper. A comprehensive survey of the DT references reveals the following findings: (1) The majority of existing DT models only involve one-way data transfer from physical entities to virtual models and (2) There is a lack of consideration of the environmental coupling, which results in the inaccurate representation of the virtual components in existing DT models. Thus, this paper highlights the role of environmental factor in DT enabling technologies and in categorized engineering applications. In addition, the review discusses the key challenges and provides future work for constructing DTs of complex engineering systems.

This purpose of this paper is to analyze how consumers’ online recommendations affect the omnichannel webrooming experience based on the internet, physical and mobile channels.

Two experimental studies are implemented. Study 1 analyzes the impact of an online review on the physical interaction with the product. Study 2 modifies the moment of receiving the online recommendation and its social tie.

Webrooming improves the shopping experience. Online recommendations from anonymous customers increase confidence in the product’s adequacy, although this effect depends on the moment of receiving the recommendation and the level of confidence before interacting physically with the product. Friend recommendations reinforce preferences regardless of previous online experiences.

This research examines the effects of different types of online recommendations on offline shopping experiences, choice and confidence. Confidence is stressed as a key variable in omnichannel behavior.

The findings offer practical value for electronic word-of-mouth marketing, omnichannel marketing, as well as online and physical channel management.

This is one of the first studies that examine the impact of online consumer recommendations on shopping experiences combining online, mobile and physical channels. The results reveal the importance of recommendations’ source and moment of reception for determining consumers’ preferences, choice and confidence.

La presente investigación analiza cómo las recomendaciones online afectan a la experiencia webrooming omnicanal, basada en el canal físico, online, y móvil.

Se llevaron a cabo dos experimentos. El Estudio 1 analiza el impacto de una revisión online positiva en la interacción posterior con el producto. El Estudio 2 modifica el momento de recibir la recomendación y el vínculo social entre emisor y receptor.

El proceso webrooming mejora la experiencia de compra. Las recomendaciones online de clientes anónimos incrementan la auto-confianza sobre la adecuación del producto, aunque este efecto depende del momento de recibir la recomendación y del nivel de auto-confianza previo a la interacción física con el producto. Las recomendaciones de amigos refuerzan las preferencias, independientemente de la experiencia online previa.

Esta investigación examina los efectos de diferentes tipos de recomendaciones online en experiencias offline, le elección y la auto-confianza. La auto-confianza se revela como una variable clave del comportamiento omnicanal.

Los resultados ofrecen implicaciones para la gestión del marketing boca-oído y omnicanal, así como la gestión de la experiencia en el canal físico y el online.

Este es uno de los primeros estudios que analizan el impacto de recomendaciones online en experiencias de compra que combinan canales online, offline y móvil. Los resultados revelan que la importancia de la fuente y del momento de recibir la recomendación determinan las preferencias, elección, y auto-confianza de los consumidores.

Comercio minorista, Omnicanal, Webrooming, Auto-confianza, Boca-oído electrónico, Vínculo social

Trabajo de investigación

Technology-enabled business-to-business (B2B) services contribute the largest share to GDP growth and are fundamental for an economy’s value creation. This article aims to identify key service- and digital technology-driven B2B innovation modes and proposes a research agenda for further exploration.

This conceptual paper adopts a techno-demarcation view on service innovation, encompassing three core dimensions: service offering (the service product, or the “what”), service process (the “how”) and service ecosystem (the “who/for whom”). It delineates the implications of three digital technologies – the internet-of-things (IoT), intelligent automation (IA) and digital platforms – for service innovation across these core dimensions in B2B markets.

Digital technology has immense potential ramifications for value creation by reshaping all three core dimensions of service innovation. Specifically, IoT can transform physical resources into reconfigurable service products, IA can augment and automate a rapidly expanding array of service processes, while digital platforms provide the technical and organizational infrastructure for the integration of resources and stakeholders within service ecosystems.

This study suggests an agenda with six themes for further research, each linked to one or more of the three service innovation dimensions. They are (1) new recurring revenue models, (2) service innovation in the metaverse, (3) scaling up service innovations, (4) ecosystem innovations, (5) power dependency and lock-in effects and (6) security and responsibility in digital domains.

The purpose of this paper is to construct a digital collection and database of traditional clothing that is convenient for the digital dissemination and application of traditional clothing and provide resources for research on clothing fashion, traditional clothing techniques, clothing culture, history and clothing teaching.

A real object analysis method was used in this paper, based on 15 core elements of the internationally common DC metadata standard, and with consideration to the characteristics of clothing products and clothing industry application specifications, the core elements of DC are expanded to facilitate the detailed record of the characteristic information of clothing, especially the implicit clothing culture. A code symbol compilation method was developed to give each piece of clothing a unique number, facilitating identification, classification and recording. At last, a metadata construction scheme for traditional clothing was developed. A traditional embroidered children's hat and Mamianqunt serve as examples to demonstrate the metadata elements.

The clothing meta-database provides a main body of traditional clothing while also paying attention to the collection of cultural elements. It is composed of five layers of classified data, source data, characteristic data, connotation data and management data, as well as 28 data elements, providing ease of sharing and interoperation.

This paper expands the subset of fashion metadata by describing traditional clothing metadata, especially the excavation of clothing cultural elements, and developing code compilation methods so that each clothing product can obtain a unique identification number, thereby building a traditional clothing metadata construction scheme consisting of five data layers and containing 28 data elements. This scheme records the information about each layer of traditional clothing in detail and provides shared data for discipline research and industry applications.

This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.

This chapter examines the phenomenon of doxxing: the practice of publishing private, proprietary, or personally identifying information on the internet, usually with malicious intent. Undertaking a scoping review of research into doxxing, we develop a typology of this form of technology-facilitated violence (TFV) that expands understandings of doxxing, its forms and its harms, beyond a taciturn discussion of privacy and harassment online. Building on David M. Douglas's typology of doxxing, our typology considers two key dimensions of doxxing: the form of loss experienced by the victim and the perpetrator's motivation(s) for undertaking this form of TFV. Through examining the extant literature on doxxing, we identify seven mutually non-exclusive motivations for this form of TFV: extortion, silencing, retribution, controlling, reputation-building, unintentional, and doxxing in the public interest. We conclude by identifying future areas for interdisciplinary research into doxxing that brings criminology into conversation with the insights of media-focused disciplines.

The COVID-19 situation affected the whole landscape of retailing in India and around the world. However, some businesses have used the pandemic-related difficulties into opportunities. E-tailing is one of the ways that helped people in India to continue shopping their essential products and choosing their luxury products without making any physical visits during the lockdown. This research understands the current situation through an observation study and suggests the e-tailing model suitable during the COVID-19 and beyond.

We used secondary data to make the observational study. We also conducted two case studies and interviews with grocery shops and an automotive company.

This research suggests a simple collaborative e-tailing model combining all supply chain players to reduce people’s movement, timely delivery and enhanced service to meet customers demand during the lockdown period.

This paper has considered two real cases for discussion and also obtained information from public domain. The proposed model has been discussed with the case companies, and it hoped to support business planning for online services.

This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them.

This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach.

The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information demand. The AISCN approach used heuristic algorithm to solve a composite objective function which takes all these factors into consideration, so that the optimized information-sharing pattern can be obtained.

This paper introduces a set of factors that can be used to describe the correlation between information-sharing pattern and performance of crowd network. By quantifying such correlation based on these factors, this paper proposes an adaptive information-sharing approach which can explore the optimized information-sharing pattern for a variety of crowd networks. As the approach is a data-driven approach that explores the information-sharing pattern based on the network’s performance on historical tasks and network’s characteristics, it is adaptive to the dynamic change (change of incoming tasks, change of network characteristics) of the target crowd network. To ensure the commonality of the information-sharing approach, the proposed approach is not designed for a specific optimization algorithm. In this way, during the implementation of the proposed approach, heuristic algorithms can be chosen according to the complexity of the target crowd network.