Resource integration and dynamic capability of frontline employee during COVID-19 pandemic: From value creation and engineering management perspectives.

The cornerstone of any successful organizations is the frontline employees. Frontline employees (FLEs) are always in action at the frontline of the business. They do not operate from the office space or from the corporate setting. Frontline employees directly interact with their customers. During the COVID-19 pandemic, many frontline employees experienced numerous challenges as most of the places there were full or partial lockdown imposed by the government agencies and the frontline employees could not be able to directly connect with their customers. Not many studies are there which investigated the issue of resource integration, dynamic capabilities, and engineering management abilities of the frontline employees such as technological capability, emotional intelligence, and psychological capability which perceived to influence the frontline employee adaptability and organization performance. In this background, the purpose of this study is to examine the relationship between frontline employee adaptability and organization performance during COVID-19 pandemic from technological, emotional, and psychological perspectives. With the help of dynamic capability view and different adaptability theories, a theoretical model has been developed conceptually. Later the conceptual model has been validated using partial least square - structural equation modeling technique considering 412 respondents from frontline employees of different organizations in Asia and EMEA. The study found that frontline employees' dynamic capabilities and engineering management abilities significantly and positively impact employee adaptability which in turn impact the performance of the organization mediating through employee job satisfaction and employee performance.

Stakeholder engagement for innovation management and entrepreneurial development: A meta-analysis.

This paper reviews contemporary studies in entrepreneurship literature related to innovation management (IM), stakeholder engagement (SE), and entrepreneurial development (ED), using bibliometric techniques and longitudinal statistical analysis of 1059 articles published in the Journal of Business Research (JBR) and other relevant business and management journals indexed in Scopus from 1974 until July 2020. We have employed a structured literature review and meta-analysis to explore the emerging research patterns in prospective observational studies encompassing the field of ED, SE, and IM. Our findings suggest that dynamics of the interaction of SE, IM, and ED are shaping the scholarship of academic research in entrepreneurship. Our meta-analysis reaffirms that contemporary research conducted at the intersection of SE, IM, and ED indicates the consolidation of these tenets in future research in entrepreneurship leading to an integrative view. Finally, we present future research directions at the intersection of SE, IM, and ED for entrepreneurship research.

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support.

Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms' productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm's predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

Role of fake news and misinformation in supply chain disruption: impact of technology competency as moderator.

Studies show that COVID-19 has increased the effects of misinformation and fake news that proliferated during the continued crisis and related turbulent environment. Fake news and misinformation can come from various sources such as social media, print media, as well as from electronic media such as instant messaging services and other apps. There is a growing interest among researchers and practitioners on how fake news and misinformation impacts on supply chain disruption. But the limited research in this area leaves a gap. With this background, the purpose of this study is to determine the role of fake news and misinformation in supply chain disruption and the consequences to a firm's operational performance. This study also investigates the moderating role of technology competency in supply chain disruption and operational performance of the firm. With the help of theories and literature, a theoretical model has been developed. Later, the conceptual model has been validated using partial least squares structural equation modeling. The study finds that there is a significant impact of misinformation and fake news on supply chain disruption, which in turn negatively impacts firms' operational performance. The study also highlights that firms' technology competency can improve the supply chain situation that has been disrupted by misinformation and fake news.

Big data driven innovation for sustaining SME supply chain operation in post COVID-19 scenario: Moderating role of SME technology leadership.

Due to the COVID-19 pandemic, there is an unprecedented crisis for businesses. The small and medium enterprises (SMEs) have been impacted even more, due to their limited resources. Extant literature has prescribed many treatments on how SMEs could survive in post COVID-19 situation, but studies did not analyse how big data driven innovation could improve supply chain management (SCM) process in the post COVID-19 pandemic under the moderating influence of SME technology leadership support. Thus, there is a research gap in this important domain. The aim of this study is to examine the impact of big data driven innovation and technology capability of the SME on its supply chain system. The study also investigates the moderating role of SME technology leadership support on SME performance in the post COVID-19 scenario. With the help of literature and resource-based view (RBV) and dynamic capability view (DCV) theory, a theoretical model has been developed conceptually. Later the model is validated using structural equation modelling (SEM) technique with 327 usable respondents from SMEs from India. The study found that both big data driven innovation and the techno-functional capability of SME impacts supply chain capability which in turn impacts the SME performance in the post COVID-19 scenario. The study also finds that there will be a moderating impact of SME technology leadership support on SME performance.