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Digital twin (DT) and building information modeling (BIM) are interconnected in some ways. However, there has been some misconception about how DT differs from BIM. As a result, industry professionals reject DT even in BIM-based construction projects due to reluctance to innovate. Furthermore, researchers have repeatedly developed tools and techniques with the same goals using DT and BIM to assist practitioners in construction projects. Therefore, this study aims to assist industry professionals and researchers in understanding the relationship between DT and BIM and synthesize existing works on DT and BIM.

A systematic review was conducted on published articles related to DT and BIM. A total record of 54 journal articles were identified and analyzed.

The analysis of the selected journal articles revealed four types of relationships between DT and BIM: BIM is a subset of DT, DT is a subset of BIM, BIM is DT, and no relationship between BIM and DT. The existing research on DT and BIM in construction projects targets improvements in five areas: planning, design, construction, operations and maintenance, and decommissioning. In addition, several areas have emerged, such as developing geo-referencing approaches for infrastructure projects, applying the proposed methodology to other construction geometries and creating 3D visualization using color schemes.

This study contributed to the existing body of knowledge by overviewing existing research related to DT and BIM in construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research.

The use of over-the-top (OTT) platforms grew substantially after the declaration of the COVID-19 pandemic in 2020. With the pandemic receding, there is a concern that users may not continue with their subscriptions. To counter this, OTT service providers must strategize proactively to retain and acquire new users once the pandemic abates. Positing that understanding the consumption values that users ascribe to OTT platform usage can provide useful customer retention insights, the purpose of this paper is to use the theory of consumption value (TCV) to study the values that users derived from their use of OTT following the onset of the pandemic.

The mixed-method approach is used to collect qualitative and quantitative data. Analysis of qualitative responses collected through interviews of 12 current OTT platform users helped identify two categories of OTT platform-specific values: attribute-level and benefit-based. Next, the study examined the association of values thus identified with one another, as well as with continued intentions to use OTT platforms, by analyzing data collected from 371 existing users.

The findings indicated that functional value quality and social value, representing the attribute-level values, were positively associated with two benefit-based values – functional value price and emotional value (EMV). Next, EMV was not only associated with intentions but also partially mediated the association of attribute-level values with intentions. Premium subscription purchased and increased viewing time were confirmed to have moderating effects on the association between attribute-level and benefit-based values.

The study is amongst the foremost research initiatives to examine consumption values derived from OTT platform usage after the onset of the pandemic. Its novelty also comes from its identifying OTT platform-specific consumption values for the first time and adding a new dimension to the TCV by examining the interplay of these values in the OTT platform context.

In the era of hyper-competitiveness, firms, especially project-based management structures, have to focus on ideas for both new and existing sets of products and services, i.e. ambidextrous innovation. The ambidextrous innovation can be helpful, but achieving such a level is a problem to be solved. This study aims to yield ambidextrous innovation by using innovative culture and knowledge that has been gained from learning.

The present research collected data from Saudi Arabian public-sector firms. The data collected is analyzed using the partial least squares structural equation modeling (PLS-SEM).

The findings of the study suggest that a range of factors can be operationalized in project-based firms to establish organizational learning and innovation culture. These factors include agile-based project management, leveraging existing innovative capabilities and growth mindset in case of innovative organizational culture and additional factors of agile-based knowledge management along with others in case of organizational learning. The PLS-SEM further concluded that both organizational learning and innovative organizational culture, in turn, help project-based Saudi Arabian public-sector firms to develop their ambidextrous innovation capability.

The PLS-SEM further concluded that both the organizational learning and innovative organizational culture, in turn, help project-based Saudi Arabian public-sector firms to develop their ambidextrous innovation capability.

Entrepreneurial ventures aspiring to disrupt existing market incumbents often use business-model innovation to increase the attractiveness of their offerings. A value proposition is the central element of a business model, and is critical for this purpose. However, how entrepreneurial ventures modify their value propositions to increase the attractiveness of their comparatively inferior offerings is not well understood. The purpose of this paper is to analyze the value proposition innovation (VPI) of aspiring disruptors.

The authors used a flexible pattern matching approach to ground the inductive findings in extant theory. The authors conducted 21 semi-structured interviews with managers from startups in the global electric vehicle industry.

The authors developed a framework, showing two factors, determinants and tactics, that play a key role in VPI connected by a continuous feedback loop. Directed by the determinants of cognitive antecedents, development drivers and realization capabilities, aspiring disruptors determine the scope, focus and priorities of various configuration and support tactics to enable and secure the success of their value proposition.

The authors contribute to theory by showing how cognitive antecedents, development drivers and capabilities determine VPI tactics to disrupt existing market incumbents, furthering the understanding of configuration tactics. The results have important implications for disruptive innovation theory, and entrepreneurship research and practice, as they offer an explanatory framework to analyze strategies of aspiring disruptors who increase the attractiveness of sustainable technologies, thereby accelerating their diffusion.

This study aims to analyse whether the presence of supply chain complexity (SCC) influences firms to improve their supply chain (SC) resilience and SC robustness capability. This study also examines an important paradox: whether investing in both exploitation and exploration practices is conflicting or complementary to enabling SC resilience and robustness in the presence of SCC.

The authors used a survey-based approach to collect 242 useful responses from SC professionals of Pakistani firms, an important emerging economy context. The data were analysed with covariance-based structural equation modelling to statistically validate the model.

The analysis reveals several key findings: the presence of SCC has a direct, positive influence on SC resilience and SC robustness; while exploitation practices only partially mediate the nexus between SCC and SC resilience, they fully mediate the relationship between SCC and SC robustness; while exploration practices partially mediate the nexus between SCC and SC resilience, they do not mediate the relationship between SCC and SC robustness and SCC has a significant influence on SC resilience and SC robustness sequentially through exploitation and exploration (i.e. one after the other).

These findings help to reconcile the exploitation versus exploration paradox in cultivating SC resilience and SC robustness in the presence of SCC. The findings assist SC managers in determining how to deploy their limited resources most effectively to enhance SC resilience and SC robustness while facing SCC.

The authors devise and empirically validate a unique framework that demonstrates how the presence of SCC works as a stimulus to build SC resilience and SC robustness.

The study provides the emergence and evolution of the socioemotional wealth (SEW) concept in the family business field from 2007 (the inception date) until 2021. To provide a better overview of this notion, the study unfolds a deeper understanding of this term throughout the systematic literature review (SLR).

The study applies a systematic literature review (SLR) by analyzing the sample of 185SEW articles extracted from the Scopus database. To identify all relevant studies, the article selection process was carefully designed and divided into two phases with clear steps: identification of studies via databases and identification of studies via previous studies' reference lists. Selected studies were analyzed using the Bibliometrix R-tool, resulting in an analysis of the evolution of the trends in the SEW literature, citation analysis, and network analysis. Finally, this SLR included the content analysis of the 25 most-cited SEW articles.

The study provides a relevant and comprehensive overview facilitating empirical and theoretical research in this field and paving the way to develop new themes. The bottom line of the important findings is that the SEW concept is relatively new, alluding to a wealthy venue for future works. Other results and implications are discussed on the family business and SEW theme. Additionally, the study provides suggestions which could be used for future works in this area.

This is the first article related to the SEW concept in the family business. It portrays a clear picture of this field, providing relevant information on what has been done, as well as what the future possibilities are that might bode the future horizons in family businesses.

Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

The purpose of the paper is to identify existing and common critical success factors adapted for implementing Industry 4.0 technology, which is essential to survive in the vulnerability, uncertainty, complexity and ambiguity (VUCA) environment by using systematic literature review (SLR) methodology with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) and content analysis strategy.

The SLR methodology with the PRISMA and content analysis strategy adapted to review 74 papers in peer-reviewed academic journals and industry reports published from 2014 to 2021.

Based on a review of relevant literature, two theoretical contributions have been added to the literature on Industry 4.0. First, this review reveals that 35 (47%) out of total 74 studies assessing the Industry 4.0 implementation in the manufacturing industry, the service industry can also create value through Industry 4.0 implementation, with a lot of potential to increase productivity, which literature has not explicitly focused on. Second, this paper proposes the 12 most common critical factors (training and development, organizational culture, top management support, organizational structure, innovation capability, technological infrastructure, security system, standardization of procedures, financial resources, communication and cooperation, change management and governance) that can be considered as the significant critical factors for successful implementation of Industry 4.0.

The novelty part related to methodological perspective by using the PRISMA approach for systematic review, which cannot be found extensively in existing literature in the context of the Industry 4.0 phenomenon to analyze critical factors.

Research in the business-to-business (B2B) and user entrepreneurship literature agrees that “user-driven” perspectives allow entrepreneurs to develop innovative products superior to conventional products. Other researchers argue that such “user-driven” products have limited success and limited impact in certain markets (e.g. niche and industrial markets). This study aims to understand the extent to which user input or co-creation becomes critical in determining product performance.

The key informant approach is used for data collection. Data were collected using a survey instrument via an online panel. Existing scales are used to measure all the focal constructs. Partial least square-based structural equation modeling was used to check for the psychometric properties of the scales and test the hypotheses.

The results indicate that user entrepreneurship is significantly related to firm collaboration efforts and customer collaboration efforts in the B2B market. Both firm collaboration efforts and customer collaboration efforts are significantly related to product performance and mediate the relationship between user entrepreneurship and product performance. Also, findings show that there is an “n” relationship between firm collaboration efforts and product performance.

This study supports the concerns raised by researchers about the dark side of value co-creation and highlights that value co-creation can impede product performance when user entrepreneurs lay too much emphasis on the collaboration processes.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.