Influence of Transformational Leadership on Organizational Citizenship Behavior and Organizational Task Performance Cross Level Analysis on Micro Small and Medium Enterprises in Indonesia: Acces la Success

In industry 4.0, individual behavior in organization is still very much considered as the main determinant of organizational performance. Organizational citizenship behavior (OCB) is one of the unique behavior of individuals affecting the effectivity of the organization. This study focuses on organizational citizenship behavior (OCB) based on the aspects of antecedents and their consequences. This study aims at examining (1) the indirect influence of transformational leadership on individual performance through organizational citizenship behavior motivation (OCBM) and OCB, (2) the direct influence of OCB on individual performance and organizational performance, (3) the direct influence of OCB for individual performance and organizational performance. This study was conducted to 66 heads of small business and 410 employee at small business in West Sumatera and analyzed using cross level methods and hypothesis testing using hierarchical linear modeling (HLM). This study found that 1). OCBM and OCB simultaneously mediate the relation between transformational leadership with individual performance and also have role in elevating the individual performance, 2) individual performance is the mediator between OCBM with organizational performance and it contributes in elevating the organizational performance. The theoretical and practical implications of this study are the tranformational leadership (TL) is able to motivate the members to carry out OCB actively in organizations. Some limitations and future research directions are discussed.

If and to What Extent Does Organizational Learning Culture Predict Turnover Intentions of Telecommuting Call Center Agents?

This study focuses on measuring the relationship between organizational learning culture (OLC) and turnover intentions of telecommuting call center agents. Although many studies involve the call center industry from different perspectives, the literature is scant in studies that have assessed the relationship between OLC and turnover intent in telecommuting call center agents. Call centers exist in almost every organization worldwide. Organizations have centralized their customer service process through computerbased technologies allowing call center agents to work from home. In addition, in the post-COVID-19 era, telecommuting has become a permanent option for many call center employees. Indeed, in the call center industry, telecommuting has become an essential part of the business strategy that seeks to attract new and maintain current employees. In the call center industry, learning is a factor that influences job satisfaction and turnover intentions. Specifically, OLC increases job satisfaction and performance in telecommuting call center agents, influencing employees' turnover intentions. The study 's findings indicate that OLC is a needed factor that helps lower turnover intentions of telecommuting call center agents in the United States.

Pandemic Highlights Urgency for New Methodologies for Evidence Generation

Randomized controlled trials, which many consider to be the gold standard of clinical research, were developed in the 1940s. [...]individual patients in the real world react often very differently to a specific drug than what has been predicted by the "mean" of a clinical trial. Many biopharmaceutical companies and AI startups are betting that with enough data, these methods will work so well that they will help to accelerate the discovery of new therapies for the novel corona virus, 2019-nCoV (see go.nature.com/3aLd0ll). * Causal AI methods might be uniquely positioned to discover underlying causes of disease and clinical response to treatment on an individual level, making personalized medicine real.

Identifying Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk: An International Journal of Medical Toxicology and Drug Experience

Introduction Identifying individual characteristics or underlying conditions linked to adverse drug reactions (ADRs) can help optimise the benefit-risk ratio for individuals. A systematic evaluation of statistical methods to identify subgroups potentially at risk using spontaneous ADR report datasets is lacking. Objectives In this study, we aimed to assess concordance between subgroup disproportionality scores and European Medicines Agency Pharmacovigilance Risk Assessment Committee (PRAC) discussions of potential subgroup risk. Methods The subgroup disproportionality method described by Sandberg et al., and variants, were applied to statistically screen for subgroups at potential increased risk of ADRs, using data from the US FDA Adverse Event Reporting System (FAERS) cumulative from 2004 to quarter 2 2021. The reference set used to assess concordance was manually extracted from PRAC minutes from 2015 to 2019. Mentions of subgroups presenting potential differentiated risk and overlapping with the Sandberg method were included. Results Twenty-seven PRAC subgroup examples representing 1719 subgroup drug-event combinations (DECs) in FAERS were included. Using the Sandberg methodology, 2 of the 27 could be detected (one for age and one for sex). No subgroup examples for pregnancy and underlying condition were detected. With a methodological variant, 14 of 27 examples could be detected. Conclusions We observed low concordance between subgroup disproportionality scores and PRAC discussions of potential subgroup risk. Subgroup analyses performed better for age and sex, while for covariates not well-captured in FAERS, such as underlying condition and pregnancy, additional data sources should be considered.

Fitting Percentage of Body Fat to Simple Body Measurements: College Women

Percentage of body fat, age, weight, height, and 14 circumference measurements (e.g., waist) are given for 184 women aged 18–25. Body fat, one measure of health, was accurately determined by an underwater weighing technique which requires special equipment and training of the individuals conducting the process. Modeling body fat percentage using multiple regression provides a convenient method of estimating body fat percentage using measures collected using only a measuring tape and a scale. This dataset can be used to show students the utility of multiple regression and to provide practice in model building.

Industry Structure of Commercial Districts and the Impact of COVID-19

This paper aims to empirically analyze the difference in the closure rate of the commercial district according to the industry structure of the commercial district. Theoretically, the larger the number of stores in a commercial district, the greater the positive externality caused by the agglomeration economies in consumption, namely, the external economies of scale. However, the agglomeration economies could occur from comparison shopping or one-stop shopping, depending on the business structure of the commercial district. According to the empirical results of the regression analysis of all 1164 commercial districts in Korea, the more specialized a commercial district is by stores in a specific industry, the lower the closure rate of that commercial district. This means that the agglomeration economies in consumption are driven by comparison shopping rather than by one-stop shopping and implies that it is necessary to introduce incentives that allow stores in the same industry to cluster together in terms of policy. Meanwhile, if the closure is limited to a specific industry, it will cause an endogeneity problem since it affects the industry structure of the commercial district. Considering this, as a result of additional estimation by 2SLS and GMM using instrumental variables, the error in estimation due to the endogeneity problem was not large, confirming that COVID-19 corresponds to an overall external shock that is not limited to a specific industry. In addition, this paper presents diagnostic indicators for commercial districts to measure the impact of COVID-19. Through this, it will be possible to alleviate conflicts between social classes over compensation or subsidies for sanctions for quarantine. To the best of our knowledge, it is the first time to use all commercial districts in Korea for a research in evaluating the impact of COVID-19, and empirical results on agglomeration economies focusing on the consumption side are limited.

Research on the physical health changes of college students during the new crown epidemic based on big data analysis technology

Multiple comparisons are general term for hypothesis testing of whether there is a significant difference between the means of each sample after analysis of variance. ANOVA can only determine whether there is a difference between the population means, and multiple comparisons can further test the differences between the sample means. This study will select the LSD (Least significant difference) test method in the multiple comparison method to conduct a differential test on the physical fitness test data of four different grades from 2018 to 2021 since the COVID-19 epidemic, and analyze the physical fitness of Chinese students under the background of new coronavirus epidemic. Change characteristics of health level, and summarize relevant experience, urge relevant personnel to make targeted improvements to the future work of students' physical health training, and scientifically improve the physical health level of Chinese students. © 2023 SPIE.

Stock return anomalies identification during the Covid-19 with the application of a grouped multiple comparison procedure.

This study investigates the impact of COVID-19 pandemic on the Chinese stock market in 2020. Using daily data of three industries, this study addresses the identification of abnormal stock returns as a multiple hypothesis testing problem and proposes to apply a grouped comparison procedure for better detection. By comparing the numbers of daily signals and numbers of stocks with abnormal positive and negative returns, the empirical result shows that the three industries perform differently under the pandemic. Compared to the non-grouped testing procedure, the signals found by the grouped procedure are more prominent, which is advantageous for some situations when there tends to be abnormal performance clustering at the occurrence of major event. This paper on stock return anomalies gives a new perspective on the impact of major events to the stock market, like the global outbreak disease.

Impact of Financial Technology on Improvement of Banks' Financial Performance

This study investigates the main financial technologies adopted by banks to improve their financial performance. The study population consists of commercial banks listed on the Amman Stock Exchange and Abu Dhabi Securities Exchange, and includes financial information and data from 2012 to 2020. A total of 115 questionnaires, consisting of five questionnaires for each bank, were distributed to the study population in Jordan and the United Arab Emirates. The dependent variable is financial performance, while the independent variable is financial technology (FinTech). Multiple linear regression analysis was conducted to test the hypotheses. The results showed that FinTech has a positive effect on both total deposit and net profits. This study recommends that banks be encouraged to adopt inclusive strategies to attain sustainable development.

The role of traditional knowledge-based dynamic capabilities to improve the sustainable performance of weaving craft in Indonesia

PurposeIndonesian woven craft small- and medium-sized enterprises (SMEs) have encountered several difficulties in sustaining their success in the digital era. The performance of the business is contingent upon its ability to gain competitive advantage through traditional knowledge capabilities. The purpose of this research is to study the role of traditional knowledge management processes towards competitive advantage and sustainable performance for woven craft SMEs.Design/methodology/approachThis research used a quantitative approach with a survey strategy. Confirmatory research was conducted to test five hypotheses to determine the causal relationship of four variables, namely, traditional knowledge management, dynamic capabilities, competitive advantage and sustainable performance. This study used a purposive sampling strategy and gathered data from 385 respondents. The sample was selected based on predetermined criteria, including operation for more than five years and entrepreneurial activity using traditional knowledge as a resource to manage product innovation. The analytical technique used was structural equation modelling with the support of the AMOS programme.FindingsThe findings indicated that traditional knowledge management processes directly affect dynamic capabilities and sustainable performance. This study also found traditional knowledge management processes play a significant role in enhancing competitive advantage mediated by dynamic capabilities. However, traditional knowledge management processes have no significant effect on competitive advantage. Hence, there is a significant effect contributed by the relationship between traditional knowledge management processes and sustainable performance. Therefore, in the context of craft woven SMEs, the higher the traditional knowledge-based capabilities, the higher their sustainable performance.Originality/valueThe novelty shows a direct relationship between traditional knowledge management processes and sustainable performance. This study also found traditional knowledge management processes meditated by dynamic capabilities have a relationship with competitive advantage. Traditional knowledge management processes will trigger an increase in dynamic capability which is a source of business development;those conditions will increase sustainable performance. Traditional knowledge-based capability is an antecedent of sustainable performance. The benefits of this research can be used as scientific literature regarding the link between traditional knowledge management processes, competitive advantage and sustainable performance. The results of this study can also be used as a basis for empowering traditional woven craft SMEs in Indonesia.

An Adaptive-to-Model Test for Parametric Functional Single-Index Model

Model checking methods based on non-parametric estimation are widely used because of their tractable limiting null distributions and being sensitive to high-frequency oscillation alternative models. However, this kind of test suffers from the curse of dimensionality, resulting in slow convergence, especially for functional data with infinite dimensional features. In this paper, we propose an adaptive-to-model test for a parametric functional single-index model by using the orthogonality of residual and its conditional expectation. The test achieves model adaptation by sufficient dimension reduction which utilizes functional sliced inverse regression. This test procedure can be easily extended to other non-parametric test methods. Under certain conditions, we prove the asymptotic properties of the test statistic under the null hypothesis, fixed alternative hypothesis and local alternative hypothesis. Simulations show that our test has better performance than the method that does not use functional sufficient dimension reduction. An analysis of COVID-19 data verifies our conclusion.

Supply chain management practices, retail outlets attributes and organisational performance: a case of organised food retailers in India

PurposeThe aim of this study is to examine the impact of supply chain management (SCM) practices and retail outlet attributes on the performance of organised food retailers in India during COVID-19 pandemic.Design/methodology/approachAuthors conducted empirical research with SCM practices and retail outlet attributes to evaluate the retailers' performance. The authors present the results from 321 valid responses from the Indian food retailers. The study used structured equation modeling to present the analysis and the results.FindingsThe findings of this study reveal that most of the SCM practices (i.e. information technology, level of information sharing, customer relationship, supplier relationship and goal congruence) have a positive impact on performance except the quality of information sharing. The results show that in the time of the pandemic, the retail outlet attributes (image, service quality and convenience) have no significant relationship with the food retailer's performance while SCM practices are necessary to achieve better performance of the food retailers.Research limitations/implicationsA proactive approach is needed from retailers at the time of pandemic situations to manage the supply chains. The retailers must be extra careful in screening the suppliers for the short and long run. The reason being that in the situation of pandemic, customers might be more demanding and hence, food retailers need to pay special attention to gain growth. It is also required that the policymakers should give importance to the implementation of these SCM practices to be positioned differently.Originality/valueThe research can be valued in terms of its original contribution towards companies in overcoming the disruption caused by COVID-19 to maintain a balance between demand and supply and proposes a completely new theoretical framework. The proposed theoretical model would add value to the existing literature to help the retailers in process of their performance improvement. The study has provided insights for retail organisations, suppliers and government departments to manage their SCs more effectively and efficiently during the pandemic.

Research Acceleration in Self-Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery

The urgency of finding solutions to global energy, sustainability, and healthcare challenges has motivated rethinking of the conventional chemistry and material science workflows. Self-driving labs, emerged through integration of disruptive physical and digital technologies, including robotics, additive manufacturing, reaction miniaturization, and artificial intelligence, have the potential to accelerate the pace of materials and molecular discovery by 10–100X. Using autonomous robotic experimentation workflows, self-driving labs enable access to a larger part of the chemical universe and reduce the time-to-solution through an iterative hypothesis formulation, intelligent experiment selection, and automated testing. By providing a data-centric ion to the accelerated discovery cycle, in this perspective article, the required hardware and software technological infrastructure to unlock the true potential of self-driving labs is discussed. In particular, process intensification as an accelerator mechanism for reaction modules of self-driving labs and digitalization strategies to further accelerate the discovery cycle in chemical and materials sciences are discussed.

Behavioral inference from non-stationary policies: Theory and application to ridehailing drivers during COVID-19 lockdowns.

In the aftermath of a disruptive event like the onset of the COVID-19 pandemic, it is important for policymakers to quickly understand how people are changing their behavior and their goals in response to the event. Choice modeling is often applied to infer the relationship between preference and behavior, but it assumes that the underlying relationship is stationary: that decisions are drawn from the same model over time. However, when observed decisions outcomes are non-stationary in time because, for example, the agent is changing their behavioral policy over time, existing methods fail to recognize the intent behind these changes. To this end, we introduce a non-parametric sequentially-valid online statistical hypothesis test to identify entities in the urban environment that ride-sourcing drivers increasingly sought out or avoided over the initial months of the COVID-19 pandemic. We recover concrete and intuitive behavioral patterns across drivers to demonstrate that this procedure can be used to detect behavioral trends as they are emerging.

A Dynamic Interaction Semiparametric Function-on-Scalar Model

Motivated by recent work studying massive functional data, such as the COVID-19 data, we propose a new dynamic interaction semiparametric function-on-scalar (DISeF) model. The proposed model is useful to explore the dynamic interaction among a set of covariates and their effects on the functional response. The proposed model includes many important models investigated recently as special cases. By tensor product B-spline approximating the unknown bivariate coefficient functions, a three-step efficient estimation procedure is developed to iteratively estimate bivariate varying-coefficient functions, the vector of index parameters, and the covariance functions of random effects. We also establish the asymptotic properties of the estimators including the convergence rate and their asymptotic distributions. In addition, we develop a test statistic to check whether the dynamic interaction varies with time/spatial locations, and we prove the asymptotic normality of the test statistic. The finite sample performance of our proposed method and of the test statistic are investigated with several simulation studies. Our proposed DISeF model is also used to analyze the COVID-19 data and the ADNI data. In both applications, hypothesis testing shows that the bivariate varying-coefficient functions significantly vary with the index and the time/spatial locations. For instance, we find that the interaction effect of the population aging and the socio-economic covariates, such as the number of hospital beds, physicians, nurses per 1000 people and GDP per capita, on the COVID-19 mortality rate varies in different periods of the COVID-19 pandemic. The healthcare infrastructure index related to the COVID-19 mortality rate is also obtained for 141 countries estimated based on the proposed DISeF model.

Comparing time varying regression quantiles under shift invariance

This article investigates whether time-varying quantile regression curves are the same up to the horizontal shift or not. The errors and the covariates involved in the regression model are allowed to be locally stationary. We formalize this issue in a corresponding non-parametric hypothesis testing problem, and develop an integrated-squared-norm based test (SIT) as well as a simultaneous confidence band (SCB) approach. The asymptotic properties of SIT and SCB under null and local alternatives are derived. Moreover, the asymptotic properties of these tests are also studied when the compared data sets are dependent. We then propose valid wild bootstrap algorithms to implement SIT and SCB. Furthermore, the usefulness of the proposed methodology is illustrated via analysing simulated and real data related to COVID-19 outbreak. © 2023 ISI/BS.

Socio-economic impact of COVID-19 pandemic: evidence from India and Bangladesh

COVID-19 has destabilized economies, lives all over the globe with devastating impacts. Physical and mental health suffered hugely due to this pandemic. Moreover, educational institutes were forced to shift from offline to online mode. This study aims to identify critical societal constructs which got impacted due to the COVID-19 pandemic in India and Bangladesh. Further, a comparative analysis has been carried out to assess these impacts between these two countries from a social science point of view. A cross-sectional study has been conducted through a structured questionnaire based on pilot study. Google Survey form link of questionnaire was active from March 15, 2020 to July 14, 2020 in which 1023 responses were received with a response rate of 54%. After the elimination of missing responses, 802 complete responses were finally coded on 48 variables including demographic profile of the respondents. Factor analysis is used to identify critical constructs of the study whereas hypothesis testing to carry out for comparative understanding. The study found that COVID-19 pandemic has severely impact on psychological, educational, economic, and social aspects of both countries whereas Bangladesh has significant adverse impact than India. Economic stimulus program such as tax exemption, EMI moratoriums, mental health training, E-learning, low interest rate of soft loan disbursement are noteworthy requirement to overcome the adverse impact of COVID-19.

Economic policy uncertainty and corporate donation: evidence from private firms in Korea

This study examines the association between economic policy uncertainty (EPU) and private firms' corporate donations. Based on resource constraints and the conservation of resources (COR) theory, we argue that private firms are constantly facing resource constraints and their resource conservation motive becomes apparent when EPU is heightened. Therefore, we expect that corporate donations are negatively related to EPU. Using audited corporate donations from 48,903 private firms in Korea during 2002–2019, we find that private firms' donations are negatively related to EPU. We find that private firms operating in more competitive conditions increase their donations, but this positive association between market competition and donations is moderated by EPU. We find that private firms' donations increased when the progressive party is in power, but this positive relationship is also moderated by EPU. Our results suggest that firms reduce their level of corporate giving to conserve resources as a precautionary saving motive when they face higher EPU. Our paper contributes to the strand of literature on corporate donations and EPU by providing evidence that EPU significantly affects private firms' donations. We also find that firms' strategic motives and political pressure to engage in corporate donations are moderated by EPU.

Bricolage as an effective tool for the survival of owner-managed SMEs during crises

This study analyzes how the resources and capabilities of the owner-manager influence the firm's capacity to survive during crises. We conceptualize that only the deliberate use of available resources (bricolage) can enhance this capacity, and that "making-do" behaviors mediate the influence of the owner's social and human resources on the firm's capacity to survive crises. Based on a sample of 462 Chilean owner-managed small and medium enterprises (SME), we test our hypotheses using a complementary partial least squares-structural equation modeling (PLS-SEM) and fuzzy set-qualitative comparative analysis (fsQCA) approach. The results indicate that when founders deliberatively use their social and cognitive resources, they enhance the firm's capacity to survive in crisis environments. The fsQCA results complement these outcomes by showing that low levels of survival capacity are related to low levels of bricolage and founders' ties. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

The strategic role of firm agility in the relationship between IT capability and firm performance under the COVID-19 outbreak

PurposeLeveraging theory from the dynamic capability literature, this study aims to explore how information technology (IT) capability influences firm agility and subsequently translates into firm performance.Design/methodology/approachThis study examines the proposed relationships by using survey data from a sample of 296 Chinese retail firms. Structural equation modeling is used to test this study's hypotheses.FindingsThe following results are produced: the direct effect of IT capability on firm agility is confirmed;firm agility has a direct impact on firm performance;and the indirect effect of IT capability on firm performance via firm agility is demonstrated (i.e. partial mediation).Originality/valueThe catastrophic outbreak of the COVID-19 pandemic has heightened the importance of firm agility more than ever. Although the traumatic event is painful, however, there is nothing like a crisis to offer a tremendous business opportunity. In response to the pandemic circumstance, firms are required to operate their business by reacting to unpredictable and dynamic market changes quickly and efficiently. This study sheds light on why firms should develop their IT capability and how it affects firm performance via firm agility during the COVID-19 outbreak.