Search results

Leaders have been facing serious challenges in managing organizations during COVID-19, which has brought the need for implementing sudden technological change across the globe. Hence, it was important to identify effective leadership styles to successfully manage the transformational process during the period. Therefore, the current study aims to explore and compare the effectiveness of transformational and ethical leadership (EL) in terms of achieving organizational goals during COVID-19 in public and private sector organizations in Pakistan.

Comparative research was carried out to find out the effectiveness of transformational and EL during and pre-COVID-19 in public and private sector organizations using the lens of social exchange theory. Data was collected from 214 respondents representing 67.6% of public and 32.4% of private sector organizations of Pakistan at two different points in time. Detailed comparative analyses were conducted in AMOS version 24 to assess the effectiveness of leadership styles before and during COVID-19 times.

On the whole, transformational leadership (TL) was found to have a greater impact on organizational effectiveness (OE) in comparison with EL in both pre-and during COVID-19 situations. Moreover, the effectiveness of TL significantly increased and the same decreased for EL during COVID-19. Additional analyses indicated that TL was effective for the private sector and EL for public sector organizations during COVID-19.

The study has not considered the mediating mechanisms of employee motivation, engagement and performance in the relationship between transformational and EL styles and OE, which can be explored in the future.

These results have important implications for private and public sector organizations and suggest that the adoption of a TL style will generate better results in the private sector and an EL style in public sector organizations to achieve OE in uncertain situations such as COVID-19.

The study shows that leadership with more care and concern for humanity tends to perform better in terms of generating results for OE. Therefore, both transformational and EL are based on individualized consideration for employees and are effective during COVID-19 in private and public sector organizations in Pakistan.

The study has carried out the comparative analyses in three different ways, including leadership styles (transformational and ethical), type of organization (private and public) and time frames (pre and during COVID-19), which is a true contribution of the research in the Pakistani context.

This research aimed to raise awareness about the need for safety measures and features of online food delivery applications (OFDAs) to build electronic trust (e-trust) with augmented purchase intention among customers. Moreover, this study explores the attributes of electronic commerce (e-commerce) and how e-trust influences the purchasing intention of consumers while ordering food through OFDAs including fear of contagious diseases. Determinants of e-commerce in the digital era profoundly impact the performance of enterprises.

Data from 493 food consumers collected from Federal Capital Territory (FCT) Pakistan, who were regular users of OFDAs. To scrutinize the dataset, confirmatory factor analysis (CFA) was conducted to assess the construct validity in this research study. Structural equation modeling (SEM), which is facilitated by Smart-PLS, was employed to examine the direct, moderation and mediation effects of the proposed model.

Results revealed the positive and significant impact of the e-trust on the purchase intention. Additionally, e-trust acted as a mediating factor in the connection between electronic security (e-security), electronic privacy (e-privacy), usability, electronic payment (e-payment), electronic innovativeness (e-innovativeness) and the buyer's purchase intention. Furthermore, the fear of contagious viruses negatively moderated the e-trust and purchase intention that weakened buying behavior.

This research is primarily centered on enhancing the comprehension regarding safety orientation within the context of an evolving restaurant industry. The findings of this study hold substantial contributions for academics, web developers, application designers, OFDAs, restaurants and other businesses since they indicate the attractiveness of OFDAs in generating feelings of pleasure and boosting users' intentions to keep using the application.

This paper aims to develop an original framework of innovation and to explore the complexity of association between individual and collective (team-based) entrepreneurship, and their simultaneous impacts on innovation in context of small and medium enterprises (SMEs).

An integral theoretical framework is developed to encourage innovation and the hypothetical relations are tested with the help of structural equation modeling (SEM) through AMOS. Data were gathered through survey technique and the questioners were distributed through email among 700 entrepreneurs from SMEs operating in five major industrial cities of Punjab province Pakistan.

The results of SEM analyses confirm that both the individual entrepreneur and the collective efforts of all the business members contribute to innovation in SMEs. Entrepreneur’s personality traits have a direct positive impact on innovation while the centralized decision-making by entrepreneur is not associated with innovation. Centralized decision-making is found to be negatively associated with communication and have insignificant positive association with collaboration. Factors associated with the team-based entrepreneurship like communication and collaboration among members of the SME’s contribute to the entrepreneurial orientation and collective entrepreneurship. Entrepreneurial orientation and collective entrepreneurship have direct positive impact on innovation in SMEs.

It is imperative for SMEs to encourage decentralized organizational culture and participative leadership to bring innovation into their products and processes and further to improve their competitive advantage.

To the best of author knowledge, present study is a first attempt that explores the complex association between individual and team-based entrepreneurship and further, empirically investigate the simultaneous impacts of these variables on innovation in context of SMEs.

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.