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The antecedents and determinants of entrepreneurial capabilities and competencies remain one of the incontestable questions that drive the exploitation and discovery of effective financial and digital opportunities. In the present paper, we propose a conceptual model based on Kirzner's alertness theory [entrepreneurial alertness] and rely on two factors [entrepreneurial orientation and marketing orientation] as key accelerators of entrepreneurial financial alertness. We assume that entrepreneurial financial alertness (EFA) might have a direct impact on entrepreneurial finance-based digital transformation (EFDT), which in turn, is expected to predict both innovation entrepreneurial finance (IEF) and SMEs' entrepreneurial performance (SMEEP). Structural equation modeling (SEM) was employed using data collected from a purposive sample size of 214 Jordanian entrepreneurs. Our findings largely support the impact of EFA on EFDT. EFDT was also supported having a significant impact on both IEF and SMEEP. Our study has many implications for both researchers and practitioners in the area of entrepreneurial finance-based digital transformation. The study has great added-value by proposing and examining a solid theoretical foundation covering the most influential factors that drive digital entrepreneurial transformation as such transformation stands as an emerging and pressing issue, not fully tackled by prior studies. [ABSTRACT FROM AUTHOR]

Solar energy measurements are often charted. These charts keep track of the conditions, events, and inspections done on each solar panel. This study describes a paradigm for measuring and monitoring solar energy via the Internet of Things. This service provides monitoring and technical help during corrective inspections within the quality reliability depth services range. These features and monitoring services can provide highly competent, higher-optimization, and excellent services that are safe, fast, and smooth. Compliance with local and international legal regulations, reliability of work, reduction of time and costs spent on corrective inspections, reduction of downtime and production losses, and management of monitoring and testing processes under expert coordination will all benefit from technical support services provided by approved companies for corrective inspections in the company. [ABSTRACT FROM AUTHOR]

Disruption from the COVID-19 pandemic has caused major upheavals for manufacturing, and has severe implications for production networks, and the demand and supply chains underpinning manufacturing operations. This paper is the first of its kind to pull together research on both—the pandemic-related challenges and the management interventions in a manufacturing context. This systematic literature review reveals the frailty of supply chains and production networks in withstanding the pressures of lockdowns and other safety protocols, including product and workforce shortages. These, altogether, have led to closed facilities, reduced capacities, increased costs, and severe economic uncertainty for manufacturing businesses. In managing these challenges and stabilising their operations, manufacturers are urgently intervening by—investing in digital technologies, undertaking resource redistribution and repurposing, regionalizing and localizing, servitizing, and targeting policies that can help them survive in this altered economy. Based on holistic analysis of these challenges and interventions, this review proposes an extensive research agenda for future studies to pursue. [ABSTRACT FROM AUTHOR]

Purpose: Pricing is widely acknowledged as a market entry challenge for servitising companies. The purpose of this research is to ascertain the attributes that contribute to willingness to pay (WTP) for industrial services and suggest incorporating those attributes to a pricing model. Design/methodology/approach: Three attributes (Quality of Service, Nearness of Service Provider and Brand Equity of Service Provider) were analyzed at three respective levels to ascertain their importance on WTP. Conventional conjoint analysis (CCA), using an orthogonal design, was the method used. The 346 respondents were decision-makers and top management professionals from various industries. Findings: Brand Equity emerged as the most significant attribute contributing to WTP, having more than 45% importance – followed by the Quality and Nearness. Research limitations/implications: The scope of the study is limited to the industries and its Allies. However, the relative importance of the attributes may vary depending on the type of service. Practical implications: The importance of attributes and their WTP preference helps future researchers create a pricing model involving these attributes. This helps service providers price their services rationally, thus succeeding in servitization. Social implications: Product life is extended because the manufacturers themselves are servicing it and also help recycle the product with their expertise. Servitization is also helpful for the Indian economy, as it is turning into a manufacturing economy. Originality/value: This research investigates three attributes that contribute to WTP, in accordance with their level of contribution. It also provides a direction to establish an adequate pricing model for industrial services. [ABSTRACT FROM AUTHOR]

The quantity of photovoltaic systems generated relies on the climate, and to generate significant variations depending on the climatic conditions. Its electricity production processes exhibit unpredictability, instability, and intermittent behavior. The augmented convolutional long-short memory combination network is recommended for PV energy combined forecasting in accordance to overcome the challenge of considerable instability in the high proportion of sustainable energy output. The LSTM and CNN models' training procedure uses the enhanced artificial bee colony (ABC) to adjust their characteristics. To determine the projected model parameters of PV power, the estimated input parameters of every model are stacked and then reassembled. The studies showed that the forecast precision was significantly higher than the reliability rate of the Convolutional LSTM combinational network as well as the forecasting findings from the BP, CNN, and LSTM independent computational methods. This forecast specificity was predicated on the enhanced ABC to optimize the variables of the CLSTM combinational network for forecasting Photo Voltaic power. For comparative study and assessment, the most recent operational data from a Photovoltaic power plant in northern China have been utilized. The suggested technique resolved more quickly and was higher weather-adaptable than the individual neural network and hybrid forecasting models mentioned prior. [ABSTRACT FROM AUTHOR]

Purpose: There is always a need to discover how a paradox between a customer's desire for a more personalized experience and their privacy and security concerns would shape their intention to continue using contactless payment methods. However, personalization–privacy paradox has not been well-covered over the area of contactless payment. Therefore, this study aims to empirically examine the impact of personalization–privacy paradox on the customers' continued intention (CIN) to use contactless payment. Design: /methodology/approach – The empirical part of the current study was conducted in Saudi Arabia by collecting the primary data using online questionnaire from a convenience sample size of 297 actual users of contactless payment methods. Findings: Based on structural equation modeling, personalization and privacy invasion were approved to significantly impact perceived value of information disclosure (PVD). Strong causal associations were confirmed between perceived severity, structural assurance and response cost with privacy invasion. Finally, both PVD and privacy invasion significantly predict CIN. Research limitations/implications: There are other important factors (i.e. technology interactivity, technology readiness, social influence, trust, prior experience, etc.) were not tested in the current study. Therefore, future studies would pay more attention regarding the impact of these factors. The current study data were also collected using a convenience sample of actual users of contactless payment methods. Therefore, there is a concern regarding the generalizability of the current study results to other kind of customers who have not used contactless payment. Originality/value: This study has integrated both personalization–privacy paradox and protection motivation theory in one model. The current study holds value in providing a new and complete picture of the inhibitors and enablers of customers' CIN to use contactless payment, including new types of inhibitors. Furthermore, personalization–privacy paradox has not been fully examined over the related area of Fintech and contactless payment in general. Therefore, this study was able to extend the theoretical horizon personalization–privacy paradox to new area (i.e. contactless payment) and new cultural context (Saudi Arabia). [ABSTRACT FROM AUTHOR]

Because of the two sequenced methods stated above, SG and AMP, are being used in different ways, present a deep learning methodology for taxonomic categorization of the metagenomic information which could be utilized for either. To place the suggested pipeline to a trial, 1000 16 S full-length genomes were used to generate either SG or AMP short-reads. Then, to map sequencing as matrices into such a number space, used a k-mer model. Our analysis of the existing approaches revealed several drawbacks, including limited ability to handle complex hierarchical representations of data and suboptimal feature extraction from grid-like structures. To overcome these limitations, we introduce DBNs for feature learning and dimensionality reduction, and CNNs for efficient processing of grid-like metagenomic data. Finally, a training set for every taxon was obtained by training two distinct deep learning constructions, specifically deep belief network (DBN) and convolutional neural network (CNN). This examined the proposed methodology to determine the best factor that determines and compared findings to the classification abilities offered by the RDP classifier, a standard classifier for bacterium identification. These designs outperform using RDP classifiers at every taxonomic level. So, at the genetic level, for example, both CNN and DBN achieved 91.4% accuracy using AMP short-reads, but the RDP classifier achieved 83.9% with the same information. This paper, suggested a classification method for 16 S short-read sequences created on k-mer representations and a deep learning structure, that every taxon creates a classification method. The experimental findings validate the suggested pipelines as a realistic strategy for classifying bacterium samples; as a result, the technique might be included in the most commonly used tools for the metagenomic research. According to the outcomes, it could be utilized to effectively classify either SG or AMP information. [ABSTRACT FROM AUTHOR]

Social media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the perceptions of prospective tourists through parameters by which the tourism and hospitality service sector can withstand the widespread implications to the sector as a result of the current pandemic. In turn this will lead to weighing up the means for recovery. The identified parameters are then classified, categorized and linked up with supply chain drivers to obtain a holistic picture that can feed into strategic planning from which the tourism and hospitality service sector could utilize to establish a resilient supply chain. This data can provide deep insight for both theorists and practitioners to utilize. It was found that reforming six supply chain drivers, whilst at the same time developing core competencies, is the central essence of a resilient supply chain within the tourism and hospitality business sector (who are at present working hard to counterbalance the many threats and consequent risks posed due to the pandemic). [ABSTRACT FROM AUTHOR]

This study explores the operational characteristics of supply chain operations during disaster scenarios within Bangladesh and the profound impact of disruption risks due to poor operational effectiveness and structural dynamics within the supply chain network. Via the analysis of representative case studies and interviews with over 500 stakeholders, we identify and analyse the many structural and administrative complexities and subsequent disruption risks surrounding the utilisation of traditional supply chain infrastructures for emergency disaster scenarios. The findings illustrate the severe impact of poor demand estimation, inadequate selection of supply items, bureaucratic and extended chain of administration, poor central coordination among the participating organizations, absence of interoperability, poor infrastructure, and lack of information sharing. We propose a new emergency supply chain infrastructure to mitigate these threats offering contribution to the literature as well as support to disaster relief efforts within Bangladesh. [ABSTRACT FROM AUTHOR]

With the advancement in the field of human resource development (HRD), emphasis on its contribution to organizational sustainability has gained greater attention ([24]; [28]; [17]). As the workforce in the manufacturing industry is changing and the pace of changes in HRD policies and practices are not in tune with the changes in workforce composition, the study filled the crucial gap in its given context. The fourth article authored by [1], "Impostor syndrome - an integrative framework of its antecedents, consequences and moderating factors on sustainable leader behaviors", addresses the individual perspective on HRD practices. [Extracted from the article]

Purpose: In India, one of the prime focuses of a post-graduate management program is to prepare students and make them job-ready. Masters in Business Management (MBA) program helps students to imbibe theoretical and practical skills which are required by the industry, which can make them hit the ground running from the day they start their career. Many students (almost 40–50%) get pre-placement offers based on their performance in summer internship. The selection for summer interns by the corporate happens within a few months of the student joining the MBA program. Signaling theory in education indicates that the level of productivity of an individual is independent of education, but the educational qualification acts as a testimony for higher ability. However, this theory does not explain the reason for the mismatch between "education and work" or "education and the disparity in salary" between individuals who earn differently but have the same qualification. The paper aims to explore three attributes namely – "employability"– the chance of being employable; "pre-placement offers" – the chance of securing a job offer based on the performance in internship and "salary" – the chance of bagging a good job offer with a high salary. Design/methodology/approach: The authors have used longitudinal data consisting of 1,202 students who graduated from reputable business schools (B-Schools) in India. In the study, the authors have used predictive analytics on six years data set that have been gathered. The authors have considered 24 attributes including educational background at the graduate level (BE, B Tech, B Com, BSc, BBA and others), score secured in class ten (high, medium and low), score secured in class twelve (high, medium and low), score secured in graduation (high, medium and low), competency in soft skills (high, medium and low), participation in co-curricular activities (high, medium and low) and social engagement status (high, medium and low). Findings: The findings of the study contradict the signaling theory in education. The findings suggest that the educational qualification alone cannot be the predictor of the employability and the salary offered to the student. The authors note that the better performance at a lower level of qualification (class 12) is the strong predictor in comparison to the student performance at their graduation and post-graduation level. The authors further observed at the post-graduate management education level that soft skills and participation in co-curricular activities are the major deciding factors to predict employability and pre-placement job opportunity and marks secured in class 12 is one more factor that gets added to this list to predict salary. The paper can immensely help management graduates to focus on key aspects that can help to hone appropriate skills and also can help management institutions to select the right students for management programs. Research limitations/implications: The analysis and the predictive model may apply to Indian B-Schools wherein the quality of students are almost the same or better. Predictive analytics has been used to explain the employability of management graduates alone and not any other. Practical implications: The authors' study might be useful for those students who often fail to understand "what" skills are the most important predictors of their performance in the pre-placement and final-placement interviews. Moreover, the study may serve as a useful guide to those organizations that often face dilemmas to understand "how" to select an ideal candidate for the particular job profile from a campus. Originality/value: The authors believe that the current study is one of the few studies that have attempted to examine the employability of management graduates using predictive analytics. The study further contradicts that the signaling theory in education does not help better explain the employability of the students in extremely high-paced business environments. [ABSTRACT FROM AUTHOR]

Advances in technology have made it easy to integrate multiple modern systems to form complex system‐of‐systems (SoS) to achieve unparalleled levels of functionality that are otherwise not achievable by the constituent systems in isolation. In fact, with the recent explosion of machine learning techniques to build autonomous systems such as drones and self‐driving cars, there is a pressing need to ensure that they collaboratively and safely operate in an SoS context. However, in general, the characteristic emergent behaviors of complex SoS – that directly impact its operational measures of success or Measures of Effectiveness (MOEs) – is very difficult, if not impossible, to manually explore, anticipate, and arbitrate just from knowledge of its underlying systems. Further, there are multiple scenarios of evolution in such complex SoS, including evolution in the emergent behavior of the SoS. The continuous, continual, and evolving nature of the SoS and constituent system environment's state and possible actions, adds further complexity. In this paper, we present a novel approach that leverages Reinforcement Learning, a machine learning approach, to inculcate adaptable intelligence in constituent systems to adapt their behaviors in tandem with the evolution of emergent behavior at the SoS level. By augmenting the reward mechanism of RL by leveraging SoS‐Constituent System MOE Relationship, that relates and ranks System MOEs vs. SoS MOEs, we inculcate an Intelligent‐Behavior Evolution Agent, with the necessary constraints to learn to maximize the SoS and system‐level MOEs, while adapting itself to the evolution in SoS. We illustrate our approach and demonstrate its feasibility and potential by applying it to a power grid SoS case example. The effectiveness and performance of the approach are quantified. [ABSTRACT FROM AUTHOR]

A complex system is characterized by emergence of global properties which are very difficult, if not impossible, to anticipate just from complete knowledge of component behaviors. Emergence, hierarchical organization and numerosity are some of the characteristics of complex systems. Recently, there has been an exponential increase on the adoption of various neural network‐based machine learning models to govern the functionality and behavior of systems. With this increasing system complexity, achieving confidence in systems becomes even more difficult. Further, ease of interconnectivity among systems is permeating numerous system‐of‐systems, wherein multiple independent systems are expected to interact and collaborate to achieve unparalleled levels of functionality. Traditional verification and validation approaches are often inadequate to bring in the nuances of potential emergent behavior in a system‐of‐system, which may be positive or negative. This paper describes a novel approach towards application of machine learning based classifiers and formal methods for analyzing and evaluating emergent behavior of complex system‐of‐systems that comprise a hybrid of constituent systems governed by conventional models and machine learning models. The proposed approach involves developing a machine learning classifier model that learns on potential negative and positive emergent behaviors, and predicts the behavior exhibited. A formal verification model is then developed to assert negative emergent behavior. The approach is illustrated through the case of a swarm of autonomous UAVs flying in a formation, and dynamically changing the shape of the formation, to support varying mission scenarios. The effectiveness and performance of the approach are quantified. [ABSTRACT FROM AUTHOR]

One of the main aspects of the Web 2.0 revolution has been social commerce that has resulted in many people across the world increasingly engaging with commercial activities over social media platforms. However, the academic and research interest in social commerce is still low, and more studies are required to accelerate awareness of the most important issues relating to social commerce, in particular, social trust and value cocreation. Thus, the present study aims to propose a conceptual model that is intended to enable greater understanding of the causal interactions between social commerce constructs, social trust, and customer value cocreation. We collected data using a sample of 300 followers and fans of online Facebook communities, and we analysed them by using a structural equation model. The results show that social commerce constructs positively impact on social trust. Furthermore, we found that social trust positively impacts on the three dimensions of customer value cocreation. We found that social trust mediates the relationship between the social commerce and customer value cocreation dimensions. The paper presents a considerable theoretical contribution for being the first study that links social commerce constructs with social trust. The linkage between social commerce constructs, social trust, and customer value cocreation dimensions will also be beneficial for social media marketing strategists and managers. [ABSTRACT FROM AUTHOR]

Architecting complex systems and complex system‐of‐systems (SoS) have evinced keen interest recently. Architectural design decisions have a significant bearing on the operational measures of success, referred to as Measures of Effectiveness (MOEs), of the systems and SoS. Architecting complex systems and SoS involves making architecture design decisions despite uncertainty (due to knowledge gaps) on the implications associated with the decisions. The learning of whether the decision is optimal or not, and the impact on the MOEs and the emergent behavior of the SoS, often occur later, resulting in Learning Cycles. This paper proposes an integrated decision learning framework for architecture design decisions for complex systems and SoS. The proposed framework adopts a decision oriented view that factors the uncertainty associated with architectural decisions and the learning cycles and feedback loops experienced. The framework enables leverage of machine learning approaches to learn from the decision learning cycles experienced and factor it into the uncertainty assessments of the decisions. By inculcating various aspects such as knowledge gaps and learning cycles, by building models such as Learning Cycle Model and Uncertainty Model, and by incorporating deployment approaches such as codification of decision attributes and decision uncertainty assessments, the proposed framework enables progressive maturity of the architectural knowledge base and aids robustness in architecture design decisions. [ABSTRACT FROM AUTHOR]

A complex system is characterized by emergence of global properties which are very difficult, if not impossible, to anticipate just from complete knowledge of component behaviors. Emergence, hierarchical organization and numerosity are some of the characteristics of complex systems. With increasing system complexity, achieving confidence in systems becomes increasingly difficult. With the recent trend towards significant footprint of complex system's functionality being governed by machine learning based models and algorithms, there is a need to ensure that emergent behavior associated with such systems are well analyzed and understood. Traditional verification and validation approaches are often inadequate to bring in the nuances of potential emergent behavior, which may be positive or negative. This paper describes a novel approach towards application of formal methods for analyzing and evaluating emergent behavior of complex systems that are governed by machine learning models. The proposed approach involves developing a machine learning classifier model that learns on potential negative and positive emergent behaviors, and leveraging the classifier in a formal verification model checking environment to assert negative emergent behavior. The approach is illustrated through an example of a pitch control system of an aircraft. The effectiveness and performance of the approach are quantified. [ABSTRACT FROM AUTHOR]

Purpose Research questions that this paper attempts to answer are – do the features in general email communication have any significance to a teaching faculty member leaving the business school? Do the sentiments expressed in email communication have any significance to a teaching faculty member leaving the business school? Do the stages mentioned in the transtheoretical model have any relevance to the email behaviour of an individual when he or she goes through the decision process leading to the decision to quit? The purpose of this paper is to study email patterns and use predictive analytics to correlate with the real-world situation of leaving the business school.Design/methodology/approach The email repository (2010–2017) of 126 teaching faculty members who were associated with a business school as full-time faculty members is the data set that was used for the research. Of the 126 teaching faculty members, 42 had left the business school during this time frame. Correlation analysis, word count analysis and sentiment analysis were executed using "R" programming, and sentiment "R" package was used to understand the sentiment and its association in leaving the business school. From the email repository, a rich feature set of data was extracted for correlation analysis to discover the features which had strong correlation with the faculty member leaving the business school. The research also used data-logging tools to extract aggregated statistics for word frequency counts and sentiment features.Findings Those faculty members who decide to leave are involved more in external communication and less in internal communications. Also, those who decide to leave initiate fewer email conversations and opt to forward emails to colleagues. Correlation analysis shows that negative sentiment goes down, as faculty members leave the organisation and this is in contrary to the existing review of literature. The research also shows that the triggering point or the intention to leave is positively correlated to the downward swing of the emotional valence (positive sentiment). A number of email features have shown change in patterns which are correlated to a faculty member quitting the business school.Research limitations/implications Faculty members of only one business school have been considered and this is primary due to cost, privacy and complexities involved in procuring and handling the data. Also, the reasons for exhibiting the sentiments and their root cause have not been studied. Also the designation, roles and responsibilities of faculty members have not been taken into consideration.Practical implications Business schools all over India always have a challenge to recruit good faculty members who can take up research activities, teach and also shoulder administrative responsibilities. Retaining faculty members and keeping attrition levels low will help business schools to maintain the standards of excellence that they aspire. This research is immensely useful for business school, which can use email analytics in predicting the intention of the faculty members leaving their business school.Originality/value Although past studies have studied attrition, this study uses predictive analytics and maps it to the intention to quit. This study helps business schools to predict the chance of faculty members leaving the business school which is of immense value, as appropriate measures can be taken to retain and restrict attrition. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this study was to understand the knowledge sharing in projects based on knowledge flow patterns. The impact of attrition, thereby leading to a loss of tacit knowledge, inability to capture and reuse knowledge and inability to understand the knowledge flow patterns, which leads to lack of structured workspace collaboration, are frequently faced challenges in organizations. The change in knowledge sourcing behaviors by the current generation workforce has a high reaching impact in driving collaboration among employees. Design/methodology/approach -- This paper attempts to study this impact and identify means to improve the effectiveness of collective knowledge sharing via social computing platforms. As part of this study, customized solutions are devised based on knowledge flow patterns prevalent in teams. Knowledge network analysis (KNA), a socio-metric analysis, is performed to understand knowledge flow patterns among employees in a team which helps understand the relationships between team members with respect to knowledge sharing. KNA helps in understanding ties and interactions between human and system resources. Findings -- Significant changes were observed in knowledge sourcing and sharing behaviors. Capture of the tacit knowledge of employees further resulted in reducing the impact of knowledge attrition. For instance, targeted communities of practice (CoPs) based on the presence of cliques within teams enabled teams to complete projects effectively and efficiently. Practical implications -- The results are used to identify push and pull networks to enable effective knowledge management (KM). Results of this study reveal that analyzing knowledge flow patterns in a team and deploying a customized social computing platform that is tailored to address the needs of specific knowledge flow patterns within that team, significantly enhances collaborative sharing as opposed to a standardized "one-size-fits-all" platform. Originality/value -- This paper is an original creation after research by the authors for a continuous assessment of KM within the organization. [ABSTRACT FROM AUTHOR]