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.

The Source of SMEs' Competitive Performance in COVID-19: Matching Big Data Analytics Capability to Business Models.

Literature notes that firms are keen to develop big data analytics capability (BDAC, e.g. big data analytics (BDA) management and technology capability) to improve their competitive performance (e.g. financial performance and growth performance). Unfortunately, the extant literature has limited understanding of the mechanisms by which firms' BDAC affects their competitive performance, especially in the context of small and medium-sized enterprises (SMEs). Using resource capability as the theoretical lens, this paper specifically examines how BDAC influences SMEs' competitive performance via the mediating role of business models (BMs). Also, this study explores the moderating effect of COVID-19 on the relationship between BDAC and BMs. Supported by Partial Least Squares-Structural Equation Modelling (PLS-SEM) and data from 242 SMEs in China, this study finds the mediating roles of infrastructure and value attributes of BMs in enhancing the relationship of BDAC on competitive performance. Furthermore, the improvement of financial performance comes from the matching of BDA management capability with infrastructure attributes of BMs, while the improvements in growth come from the matching of BDA management capability and BDA technology capability with value attributes of BMs. The result also confirms the positive moderating effects of COVID-19 on the relationship of BDA management capability and value attributes of BMs. This study enriches the integration of BDAC and BMs literature by showing that the match between BDAC and BMs is vital to achieve competitive performance, and it is helpful for managers to adopt an informed BDA strategy to promote widespread use of BDAs and BMs.

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.

Theorising the Microfoundations of analytics empowerment capability for humanitarian service systems.

The world is facing an unprecedented humanitarian crisis due to the COVID-19 pandemic. Humanitarian service systems are being empowered to tackle this crisis through the use of vast amounts of structured and unstructured data to protect vulnerable individuals and communities. Analytics has emerged as a powerful platform to visualise, predict, and prescribe solutions to humanitarian crises, such as disease containment, healthcare capacity, and emergency food supply. However, there is a paucity of research on the microfoundations of the humanitarian analytics empowerment capability. As such, drawing on dynamic capability theory and by means of a systematic literature review and thematic analysis, this study proposes an analytics empowerment capability framework for humanitarian service systems. The findings show that analytics culture, technological sophistication, data-driven insights, decision making autonomy, knowledge and skills, and training and development are crucial components of the analytics empowerment's capability to sense, seize, and remedy crisis situations. The paper discusses both theoretical and practical research implications.

Identification of Health Expenditures Determinants: A Model to Manage the Economic Burden of Cardiovascular Disease.

The purpose of this paper is to investigate the determinants influencing the costs of cardiovascular disease in the regional health service in Italy's Apulia region from 2014 to 2016. Data for patients with acute myocardial infarction (AMI), heart failure (HF), and atrial fibrillation (AF) were collected from the hospital discharge registry. Generalized linear models (GLM), and generalized linear mixed models (GLMM) were used to identify the role of random effects in improving the model performance. The study was based on socio-demographic variables and disease-specific variables (diagnosis-related group, hospitalization type, hospital stay, surgery, and economic burden of the hospital discharge form). Firstly, both models indicated an increase in health costs in 2016, and lower spending values for women (p < 0.001) were shown. GLMM indicates a significant increase in health expenditure with increasing age (p < 0.001). Day-hospital has the lowest cost, surgery increases the cost, and AMI is the most expensive pathology, contrary to AF (p < 0.001). Secondly, AIC and BIC assume the lowest values for the GLMM model, indicating the random effects' relevance in improving the model performance. This study is the first that considers real data to estimate the economic burden of CVD from the regional health service's perspective. It appears significant for its ability to provide a large set of estimates of the economic burden of CVD, providing information to managers for health management and planning.

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.

Mating Performance: Assessing Flirting Skills, Mate Signal-Detection Ability, and Shyness Effects.

Several people today experience poor mating performance, that is, they face difficulties in starting and/or keeping an intimate relationship. On the basis of an evolutionary theoretical framework, it was hypothesized that poor mating performance would be predicted by poor flirting skills, poor mate signal-detection ability, and high shyness. By employing a sample of 587 Greek-speaking men and women, we found that more than 40% of our participants experienced difficulties in starting and/or keeping an intimate relationship. We also found that poor flirting skills, poor mate signal-detection ability, and high shyness were associated with poor performance in mating, especially with respect to starting an intimate relationship. The effect sizes and the odds ratios indicated that flirting skills had the largest effect on mating performance, followed by the mate signal-detection ability and shyness.