Search results

Facing the increasing competition and uncertainty, when and how to improve service innovation performance with the help of digital business strategy has become an important issue for global service firms. In this study, organizational memory level and dispersion are regarded as moderating variables and market intelligence response is introduced as a mediator, aiming at clarifying the boundary conditions and mechanism of digital business strategy affecting service innovation performance.

A survey was conducted among middle and senior managers from 245 service firms in China. The data were analyzed using SPSS and Mplus software for reliability and validity analysis, hypothesis testing and robustness testing.

Digital business strategy was positively related to the service innovation performance of service firms. Market intelligence responsiveness mediated the positive effect of digital business strategy on service innovation performance of service firms. The positive effect between digital business strategy and market intelligence responsiveness was strengthened when the level and dispersion of organizational memory were moderate.

This study suggests that it is a very effective approach for service firms to initiate digital business strategy to improve service innovation performance. Furthermore, market intelligence responsiveness is crucial because it can help service firms quickly respond to market changes and adapt them accordingly. Managers of service firms should recognize that the benefits of digital business strategy are maximized only when the level and dispersion of organizational memory are moderate.

This study is the first to address the question of how and when digital business strategy drives service innovation performance in the context of digitization. In addition, this study enriches and advances organizational learning theory because it discusses the differential impact of digital business strategy on service innovation performance under varying degrees of organizational memory level and dispersion.

Research on the underlying mechanisms that transfer the effects of performance appraisal (PA) on employees’ behaviors and intentions remains scarce. The social exchange view of performance appraisal can be a source of deeper understanding about these underlying mechanisms. This study aims to investigate how psychological empowerment (PE) and organizational commitment (OC) explain the link between performance appraisal and three important distal outcomes: voice behavior (VB), organizational citizenship behavior (OCB), and turnover intentions (TOI).

The current study utilizes two wave – two source data from a sample of 250 employees and their supervisors from private organizations in Pakistan and tested the mediation model using SMART-PLS.

Results demonstrated that organizational commitment mediated the effects of performance appraisal on VB, OCB, and TOI, whereas psychological empowerment mediated the effects of performance appraisal on VB and OCB. These results have significant implications for theory and practice.

This study adopts the social exchange perspective to examine the mediation mechanisms linking PA with the three distal outcomes: VB, OCB and TOI. The paper identifies two novel mediators of PA – outcome relationship, i.e., psychological empowerment and organizational commitment.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

The pedestrian dead reckoning (PDR) based on smartphones has been widely applied in continuous indoor positioning. However, when the position of the mobile phone and the walking patterns of the pedestrian are mixed, traditional PDR tends to become confused and thus degrade performance. To address this issue, this paper aims to propose an improved PDR scheme by focusing on gait pattern recognition and the impact of short-period but negative transitions on tracking.

The overall solution uses the inertial sensor integrated within the phone for positioning. A binary classifier-based change point detection algorithm is used to identify the transition points in pedestrian gait. Additionally, to enhance the accuracy of gait recognition, this paper presents a combined CNN-attention-based bi-directional long short-term memory(ABiLSTM) model, integrating convolutional neural networks (CNN), bi-directional long short-term memory (Bi-LSTM) and an attention mechanism, to recognize the current gait pattern. The outcomes of this gait pattern recognition are then applied to PDR. Based on distinct gait patterns, corresponding PDR strategies are devised to enable continuous tracking and positioning of pedestrians.

Through experimental verification, the CNN-ABiLSTM model achieves a gait recognition accuracy of 99.52% on the self-constructed data set. The pedestrian navigation estimation method proposed in this paper, which is based on gait recognition assistance, demonstrates a 32.56% improvement in accuracy over traditional positioning algorithms in multi-gait scenarios.

The improved PDR scheme algorithm significantly enhances the robustness and smoothness of pedestrian tracking, particularly during multiple gait transitions. This, in turn, provides strong support for the utilization of low-cost inertial sensors integrated within mobile phones for indoor positioning applications.

The purpose of this paper is to optimize a suitable electrochemical method in evaluating the corrosion rate of structural materials of 20# carbon steel, P280GH carbon steel, 17-4PH stainless steel, 304 stainless steel and Alloy 690TT in high-temperature and high-pressure (HTHP) water of pressurized water reactor secondary circuit system.

Weight-loss method has been used to obtain the corrosion rate value of each structural material in simulated HTHP water. Besides, linear polarization method and weak polarization curve-based three-point method and four-point method have been compared in obtaining a sound corrosion rate value from the potentiodynamic polarization curve. Scanning electron microscopy (SEM) and atomic force microscope have been used to characterize the microstructure and corrosion morphology of each structural material.

Although there is deviation in gaining the corrosion rate value compared to weight-loss test, the weak polarization curve-based four-point method has been found to be a suitable electrochemical method in gaining corrosion rate value of structural materials in HTHP waters.

This paper proposes a suitable and reliable electrochemical method in gaining the corrosion rate value of structural materials in HTHP waters. The proposed weak polarization curve-based four-point method provides a timesaving and high-efficient way in corrosion rate evaluation of secondary circuit structural materials and thus has a potential application in nuclear power plants.

The impact on both the environment and operator health is significant. As high-alumina silica glass finds applications in smart devices such as curved mobile phone screens, the grinding of complex curved surfaces necessitates cleaner and more efficient cooling and lubrication methods to enhance processing quality and improve grinding yield rates. This study aims to focus on grinding high-alumina silica glass using micro-lubrication technology and compares its performance with traditional cutting fluid cooling methods.

In the fabrication of mobile phone cover plates composed of high-alumina silicon glass, the incorporation of micro-lubrication grinding technology was undertaken, with the conventional cutting fluid cooling approach serving as the benchmark control group for comparative analysis.

The results indicate that increasing the spray pressure of micro-lubrication within a specific range contributes to reducing grinding surface roughness. At a grinding speed ranging from 25 to 35 m/s, using micro-lubrication can effectively replace the traditional cutting fluid cooling method, resulting in glass surfaces with roughness levels between 0.22 and 0.26. However, at grinding speeds exceeding 35 m/s, the insufficient pressure of the micro-lubricant mist hinders most of the oil mist from entering the grinding zone, leading to inferior cooling performance compared to cutting fluid cooling. Notably, at a grinding speed of 35 m/s, micro-lubrication demonstrates better effectiveness in suppressing chipping during glass grinding compared to traditional cutting fluid cooling methods.

Through the application of micro-lubrication grinding technology, a marked improvement in the grinding quality of high-alumina silicon mobile phone cover plate glass can be achieved, leading to a reduction in surface roughness, a decrease in processing defects and ultimately satisfying the demands for high-precision and high-quality fabrication of such cover plates.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0297

Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.

The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.

Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.

A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open innovation. While there is a growing body of knowledge that has examined how, in a knowledge economy, a firm’s knowledge and innovation activities are closely linked, there is no systematic review available of the key antecedents, perspectives, phenomenon and outcomes of knowledge spillovers.

The authors have conducted dual-stage research. First, the authors conducted a systematic review of literature (97 research articles) by following the theories–contexts–methods framework and the antecedent-phenomenon-outcomes logic. The authors identified the key theories, contexts, methods, antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. In the second stage, the findings of stage one were leveraged to advance a nomological network that depicts the strength of the relationship between the observable constructs that emerged from the review.

The findings demonstrate how knowledge spillovers can help incumbent organisations and start-ups to achieve improved innovation capabilities, R&D capacity, competitive advantage and the creation of knowledge ecosystems leading to improved firm performance. This study has important implications for practitioners and managers – it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. The emerging network showed that the antecedents of knowledge spillovers have a direct relationship with the creation of a knowledge ecosystem orchestrated by incumbents and that there is a very strong influence of knowledge capacities and knowledge types on the selection of external knowledge partners/sources.

This study has important implications for practitioners and managers. In particular, it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. This will enable managers to take important decisions about what knowledge capacities are required to achieve innovation outcomes. The findings suggest that managers of incumbent firms should be cautious when deciding to invest in knowledge sourcing from external partners. This choice may be driven by the absorptive capacity of the incumbent firm, market competition, protection of intellectual property and public policy supporting innovation and entrepreneurship.

Identification of the key antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. The findings from Stage 1 helped us to advance a nomological network in Stage 2, which identifies the strength and influence of the various observable constructs (identified from the review) on each other. No prior study, to the best of the authors’ knowledge, has advanced a nomological network in the context of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context.

The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel friction pairs.

A PTFE layer was sintered on the surface of a steel disk, and a lubricant with additives was applied to the surface of the steel disk. A friction and wear tester was used to evaluate the tribological properties and insulation capacity. Fourier transform infrared spectrometer was used to analyze the changes in the composition of the lubricant, and X-ray photoelectron spectroscopy was used to analyze the chemical composition of the worn surface.

It was found that incorporating the PTFE film with PSAIL 2280 significantly enhanced both the friction reduction and insulation capabilities at the electrical contact interface during sliding. The system consistently achieved ultra-low friction coefficients (COF < 0.01) under loads of 2–4 N and elucidated the underlying lubrication mechanisms.

This work not only confirm the potential of PTFE films in insulating electrical contact lubrication but also offer a viable approach for maintaining efficient and stable low-friction wear conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0222/

This paper introduces a novel method to improve building safety management by leveraging building information modeling (BIM) and adaptive information retrieval techniques. The integration aims to overcome the limitations of traditional safety management methods in connecting construction processes with risk management efficiently.

The proposed method involves developing industry foundation classes (IFC) ontologies and integrating them with a safety document ontology to form a comprehensive BIM-based safety context framework. Custom reasoning rules and an inference engine are constructed to enable automatic context-aware safety information retrieval. The methodology is demonstrated through an adaptive information retrieval system using job hazard analysis (JHA) documents.

The implementation of the BIM-based adaptive information retrieval system shows significant improvements in identifying and managing construction risks. By mapping job-specific risks to corresponding safety measures, the system enhances risk detection and management tailored to particular construction tasks. The results indicate a marked improvement in the precision and accuracy of safety assessments and recommendations, aligning them closely with planned construction activities and conditions.

This paper offers an innovative approach to construction safety management through the development of a BIM-facilitated context-aware information retrieval system. This approach provides a more intelligent and automated framework for identifying and managing risks in construction projects. By focusing on specific job steps and related risks, the system enhances the effectiveness and accuracy of safety measures, contributing to better overall building safety management.