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This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.

Applying both the dynamic capability and configuration theoretical perspectives, the paper showcases the role of network configuration and dynamics of hybrid offerings in both developed and emerging markets by high-tech firms.

The current paper uses an exploratory qualitative research methodology based on in-depth case studies of three Finnish high-tech firms operating in the medical technology industry globally.

The findings from the study showed that dynamic capabilities such as sensing and customer engagement along with internal coordination and adaptation capabilities are critical to the success of hybrid market offerings. Moreover, dynamic capabilities were found to be influential in those emerging and advanced international markets where case firms were less familiar with market dynamics. Moreover, the configuration of these capabilities within functional units and coordination of marketing and R&D activities can be effective for creating hybrid offerings in international markets. Ultimately, this was found to be the case even though target market selection for hybrid offerings was influenced by the level of convergence and fragmentation of the market.

Applying the configuration theory, this is one of the first studies to specifically analyze the differences in organizational network configuration changes in relation to hybrid market offerings in both developed economies and emerging economies. The findings contribute to hybrid market offering literature by pointing out that not only internal capabilities are important for enacting hybrid offerings, but the roles of ecosystems and knowledge centers are also extremely important to develop hybrid offerings. This paper also highlights the criticality of under-studied dynamic capabilities such as market sensing and customer engagement in the context of hybrid offerings in international markets. This showcases the wider role of ecosystems in enabling technology firms to develop hybrid offerings.

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

The purpose of the work is to provide a comprehensive review of the digital image correlation (DIC) technique for those who are interested in performing the DIC technique in the area of manufacturing.

No methodology was used because the paper is a review article.

no fundings.

Herein, the historical development, main strengths and measurement setup of DIC are introduced. Subsequently, the basic principles of the DIC technique are outlined in detail. The analysis of measurement accuracy associated with experimental factors and correlation algorithms is discussed and some useful recommendations for reducing measurement errors are also offered. Then, the utilization of DIC in different manufacturing fields (e.g. cutting, welding, forming and additive manufacturing) is summarized. Finally, the current challenges and prospects of DIC in intelligent manufacturing are discussed.

Using real-time support systems may help operators in road construction to improve paving and compaction operations. Nowadays, these systems transform from descriptive to prescriptive systems. Prescriptive or operator guidance systems propose operators actionable compaction strategies and guidance, based on the data collected. It is investigated how these systems mediate the perceptions and actions of operators in road pavement practice.

A case study is conducted on the specific application of an operator guidance system in a road pavement project. In this case study, comprehensive information is presented regarding the process of converting input in the form of data from cameras and sensors into useful output. The ways in which the operator guidance systems translate data into actionable guidance for operators are analyzed from the technological mediation perspective.

Operator guidance systems mediate actions of operators physically, cognitively and contextually. These different types of action mediation are related to preconditions for successful implementation and use of these systems. Coercive interventions only succeed if there is widespread agreement among the operators. Persuasive interventions are most effective when collective and individual interests align. Contextual influence relates to designs of the operator guidance systems that determine human-technology interactions when using them.

This is the first study that analyzes the functioning of an operator guidance system using the technological mediation approach. It adds a new perspective on the interaction between this system and its users in road pavement practice.

The proliferation of mobile phones with integrated sensors makes large scale sensing possible at low cost. During mobile sensing, data mostly contain sensitive information of users such as their real-time location. When such information are not effectively secured, users’ privacy can be violated due to eavesdropping and information disclosure. In this paper, we demonstrated the possibility of unauthorized access to location information of a user during sensing due to the ineffective security mechanisms in most sensing applications. We analyzed 40 apps downloaded from Google Play Store and results showed a 100% success rate in traffic interception and disclosure of sensitive information of users. As a countermeasure, a security scheme which ensures encryption and authentication of sensed data using Advanced Encryption Standard 256-Galois Counter Mode was proposed. End-to-end security of location and motion data from smartphone sensors are ensured using the proposed security scheme. Security analysis of the proposed scheme showed it to be effective in protecting Android based sensor data against eavesdropping, information disclosure and data modification.

Although the industrial application of drones is increasing quickly, there is a scarcity of applications in manufacturing. The purpose of this paper is to explore current and potential applications of drones in manufacturing, examine the opportunities and challenges involved and propose a research agenda.

The paper reports the result of an extensive qualitative investigation into an emerging phenomenon. The authors build on the literature on advanced manufacturing technologies. Data collected through in-depth interviews with 66 drone experts from 56 drone vendors and related services are analyzed using an inductive research design.

Drones represent a promising AMT that is expected to be used in several applications in manufacturing in the next few years. This paper proposes a typology of drone applications in manufacturing, explains opportunities and challenges involved and develops a research agenda. The typology categorizes four types of applications based on the drones’ capabilities to “see,” “sense,” “move” and “transform.”

The proposed research agenda offers a guide for future research on drones in manufacturing. There are many research opportunities in the domains of industrial engineering, technology development and behavioral operations.

Guidance on current and promising potentials of drones in manufacturing is provided to practitioners. Particularly interesting applications are those that help manufacturers “see” and “sense” data in their factories. Applications that “move” or “transform” objects are scarcer, and they make sense only in special cases in very large manufacturing facilities.

The application of drones in manufacturing is in its infancy, but is foreseen to grow rapidly over the next decade. This paper presents the first academically rigorous analysis of potential applications of drones in manufacturing. An original and theory-informed typology for drone applications is a timely contribution to the nascent literature. The research agenda presented assists the establishment of a new stream of literature on drones in manufacturing.

This paper aims to propose an enhanced densely dehazing network to suit railway scenes’ features and improve the visual quality degraded by haze and fog.

It is an end-to-end network based on DenseNet. The authors design enhanced dense blocks and fuse them in a pyramid pooling module for visual data’s local and global features. Multiple ablation studies have been conducted to show the effects of each module proposed in this paper.

The authors have compared dehazed results on real hazy images and railway hazy images of state-of-the-art dehazing networks with the dehazed results in data quality. Finally, an object-detection test is taken to judge the edge information preservation after haze removal. All results demonstrate that the proposed dehazing network performs better under railway scenes in detail.

This study provides a new method for image enhancing in the railway monitoring system.

The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.