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This study aims to estimate the quality-adjusted changes in residential mobile consumer prices by controlling for the changes in the relevant service characteristics and quality, in a case study on Hungary between 2015 and 2021; compare the results with changes measured by the traditionally calculated official telecommunications price index of the Statistical Office; and discuss separating the hedonic price changes from the effect of a specific government intervention that occurred in Hungary, namely, the significant reduction in the value added tax rate (VAT) levied on internet services.

Since the price of commercial mobile offers does not directly reflect the continuous improvements in service characteristics and functionalities over time, the price changes need to be adjusted for changes in quality. The authors use hedonic regression analysis to address this issue.

The results show significant hedonic price changes over the observed seven-year period of over 30%, which turns out to be primarily driven by the significant developments in the comprising service characteristics and not the VAT policy change.

This paper contributes to the literature on hedonic price analyses on complex telecommunications service plans and enhances this methodology by using weights and analysing the content-related features of the mobile packages.

To explain how technology will replace a great deal of human labor in knowledge markets using a theory of reasoned action applied to demand and theories of procedural rationality, cost structure and system dynamics applied to supply.

Two illustrative scenarios are presented. The first is a third-party Best Treatments site, and its effect on the expert advice pharmaceutical representatives provide doctors. The second scenario is an online higher education business course module with embedded AI.

Both scenarios demonstrate the advantages of online expertise and teaching platforms over the in-person alternative in variable and marginal cost, ease and convenience of use, quality conformance, scalability, knowledge reach and depth and most importantly, speed of evolutionary adaptability. Despite such overwhelming advantages, a number of reasons why the substitution might be slowed are presented, and some strategies firms might adopt are discussed. Opportunities for service scholars to confirm, challenge and extend the conclusions are presented throughout the paper.

Increasing cost structure and adaptability advantages of online technology and AI over in-person delivery of expertise and training services are demonstrated. It is also demonstrated that the innovation-imitation cycle is accelerating because of exogenous innovation in knowledge access and online influence networks and an endogenous effect where imitators accelerate their innovation that drives innovators to accelerate their innovation, which drives imitators to further accelerate their imitation.

Existing studies have paid less attention to the impact of knowledge accumulation on digital transformation and its boundary conditions. Hence, this study aims to investigate the effects of ambidextrous knowledge accumulation on manufacturing digital transformation under the moderation of dynamic capability.

This study divides knowledge accumulation into exploratory and exploitative knowledge accumulation and divides dynamic capability into alliance management capability and new product development capability. To clarify the relationship among ambidextrous knowledge accumulation, dynamic capability and manufacturing digital transformation, the authors collect data from 421 Chinese listed manufacturing enterprises from 2016 to 2020 and perform analysis by multiple hierarchical regression method, heterogeneity test and robustness analysis.

The empirical results show that both exploratory and exploitative knowledge accumulation can significantly promote manufacturing digital transformation. Keeping ambidextrous knowledge accumulation in parallel is more conducive than keeping single-dimensional knowledge accumulation. Besides, dynamic capability positively moderates the relationship between ambidextrous knowledge accumulation and manufacturing digital transformation. Moreover, the heterogeneity test shows that the impact of ambidextrous knowledge accumulation and dynamic capabilities on manufacturing digital transformation varies widely across different industry segments or different regions.

First, this paper shifts attention to the role of ambidextrous knowledge accumulation in manufacturing digital transformation and expands the connotation and extension of knowledge accumulation. Second, this study reveals that dynamic capability is a vital driver of digital transformation, which corroborates the previous findings of dynamic capability as an important driver and contributes to enriching the knowledge management literature. Third, this paper provides a comprehensive micro measurement of ambidextrous knowledge accumulation and digital transformation based on the development characteristics of the digital economy era, which provides a theoretical basis for subsequent research.

An important and challenging question for air transportation regulators and airport operators is the definition and specification of airport capacity. Annual capacity is used for long-term planning purposes as a degree of available service volume, but it poses several inefficiencies when measuring the true throughput of the system because of seasonal and daily variations of traffic. Instead, airport throughput is calculated or estimated for a short period of time, usually one hour. This brings about a mismatch: air traffic forecasts typically yield annual volumes, whereas capacity is measured on hourly figures. To manage the right balance between airport capacity and demand, annual traffic volumes must be converted into design hour volumes, so that they can be compared with the true throughput of the system. This comparison is a cornerstone in planning new airport infrastructures, as design-period parameters are important for airport planners in anticipating where and when congestion occurs. Although the design hour for airport traffic has historically had a number of definitions, it is necessary to improve the way air traffic design hours are selected. This study aims to provide an empirical analysis of airport capacity and demand, specifically focusing on insights related to air traffic design hours and the relationship between capacity and delay.

By reviewing the empirical relationships between hourly and annual air traffic volumes and between practical capacity and delay at 50 European airports during the period 2004–2021, this paper discusses the problem of defining a suitable peak hour for capacity evaluation purposes. The authors use information from several data sources, including EUROCONTROL, ACI and OAG. This study provides functional links between design hours and annual volumes for different airport clusters. Additionally, the authors appraise different daily traffic distribution patterns and their variation by hour of the day.

The clustering of airports with respect to their capacity, operational and traffic characteristics allows us to discover functional relationships between annual traffic and the percentage of traffic in the design hour. These relationships help the authors to propose empirical methods to derive expected traffic in design hours from annual volumes. The main conclusion is that the percentage of total annual traffic that is concentrated at the design hour maintains a predictable behavior through a “potential” adjustment with respect to the volume of annual traffic. Moreover, the authors provide an experimental link between capacity and delay so that peak hour figures can be related to factors that describe the quality of traffic operations.

The functional relationships between hourly and annual air traffic volumes and between capacity and delay, can be used to properly assess airport expansion projects or to optimize resource allocation tasks. This study offers new evidence on the nature of airport capacity and the dynamics of air traffic design hours and delay.

This study aims to understand the impacts of Covid-19 on the progression of digitalization of banks in an emerging market. For this purpose, business model canvas (BMC) is used as a theoretical framework to explore these effects on each business elements of Turkish Banks’ business strategies.

Data are collected through structured interviews with the top managers of seven diversified banks. Interview questions are designed based on BMC.

The results show that the onset of the Covid-19 is a shock that has made digitalization a strategic issue that necessitates an urgent change in many business elements of banks such as customer relationships, communication channels, resource allocation, partnerships and financing. Further, it has stimulated redefining value proposition and collaboration/interaction among all financial institutions through digital platforms.

BMC can be used to explain decision-making and business processes of banks for exploring the effect of recent and/or unexpected developments in the business environment of an emerging economy. The results provide insights and recommendations to managers of financial institutions into the impacts of Covid-19 on banks’ operational and strategic processes. That allows financial institutions, including Fintechs, to use this information for taking precautions and proactive actions against shocks.

This study is an initial attempt to explore the impacts of the Covid-19 on banks in an emerging economy by using BMC. With that, this study contributes to the literature by explaining the effect of progression of digitalization in banking from a strategic business model perspective using a qualitative research method.

This paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.

It is well recognized that the protection scheme in a DC ring bus microgrid becomes very complicated due to the bidirectional power flow. To provide reliable protection, the differential current signal is decomposed into several basic modes using adaptive variational mode decomposition (VMD). In this method, the mode number and the penalty factor are chosen optimally by using arithmetic optimization algorithm, yielding satisfactory decomposition results than the conventional VMD. Weighted Kurtosis index is used as the measurement index to select the sensitive mode, which is used to evaluate the discrete Teager energy (DTE) that indicates the occurrence of DC faults. For localizing cable faults, the current signals from the two ends are used on a sample-to-sample basis to formulate the state space matrix, which is solved by using generalized least squares approach. The proposed protection method is validated in MATLAB/SIMULINK by considering various test cases.

DTE is used to detect pole-pole and pole-ground fault and other disturbances such as high-impedance faults and series arc faults with a reduced detection time (10 ms) compared to some existing techniques.

Verification of this method is performed considering various test cases in MATLAB/SIMULINK platform yielding fast detection timings and accurate fault location.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

This paper aims to propose a framework that combines the triple helix model with competitive strategies concepts to capture and guide any innovation-led national development strategies.

This paper adopted a methodological framework based on existing methods and guidelines, the most commonly reported approach for developing a methodological framework. The review of fundamental approaches to achieving fast and sustained economic development, triple helix model and competitive strategies helped develop the methodological framework. The framework was validated and tested using the case studies approach on Korea, Taiwan and Singapore.

Kuwait aims to create an innovative environment to benefit from the innovation strategies anchored by the East Asian miracle economies and how they used the triple helix actors at different developmental stages. First, Kuwait’s research institutes and universities need to design interactive programs and activities with industry and community to help innovate solutions to current and prospective challenges. Second, the government needs to provide a competitive business environment and effective policies. Thirdly, the Kuwait industry must be encouraged to innovate and infuse modern technology practices.

Developing countries are trying to use science, technology and innovation as an effective strategy for achieving sustained economic growth. However, since each country has its unique conditions, learning from other success stories proved difficult if not structured in a framework designed to serve a specific purpose such as the one the authors propose in this paper.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

The purpose of the present research is to introduce a combined framework that integrates innovative work behavior, product innovation process and customer knowledge management; then, to explore the mediating effect of customer knowledge management in the relationship between innovative work behavior and the product innovation process.

The basis for the present research is a cross-sectional design. Data collection from 154 employees occurred using the following structured questionnaires: Customer Knowledge Management (CKM), Innovative Work Behavior (IWB) and Product Innovation Process (PIP). Data processing used SPSS version 26.0, including the PROCESS (3.5) macro analysis.

The results show positive relationships between innovative work behavior and the product innovation process (r = 0.420, p < 0.01). Pearson's correlation shows a coefficient of 0.42, meaning that 42% of the variations in perceived product innovation are due to variations in innovative work behavior. The second condition of the mediation test involved testing the relationship between the independent variable (Innovative Work Behavior) and the mediating variable (Knowledge Management) and showed a significant relationship (r = 0.272, p < 0.01). The findings suggested that knowledge management that other determinants supported, such as collaboration in idea exploration, idea championing and encouragement of participation in idea implementation, significantly contributed to the product innovation process (r = 0.509, p < 0.01). The bootstrapping method confirmed that innovative work behavior supports product innovation through the mediation of customer knowledge management (z = 3.01, p = 0.002).

The cross-sectional design, along with the relatively low number of participants and the self-reporting nature of the questionnaires, represent the current study's main limitations. Developing the research model could integrate new variables, such as customer co-creation processes, performance-based compensation, employee citizenship activities and transformational leadership.

This research has both theoretical and practical implications. These emphasize the importance of further investigation into the factors influencing companies' innovation processes. They also provide managers with a means of finding a fit between the deployment of customer knowledge mechanisms and the achievement of innovative workplace behavior, to improve innovation process efficiency.

The current study broadens the empirical research area of customer knowledge management and its impact on both innovative work behavior and the product innovation process, particularly in knowledge-intensive market scenarios that require organizations to be innovative.