Sarwat Jahan

In today’s data-driven environment, Big Data Analytics (BDA) plays a vital role in enhancing decision-making quality and organizational performance. However, limited empirical research exists on how the five characteristics of big data (5Vs: Volume, Velocity, Variety, Veracity, and Value) influence decision-making effectiveness in China’s industrial sector. Addressing this gap, the present study builds on Simon’s decision-making theory and the information processing perspective to develop and test a research model linking BDA to decision-making and performance outcomes. Using a self-designed structured survey, data were collected from 312 managers across medium and large-sized manufacturing firms in China. Structural equation modeling (SEM) was employed to examine the relationships among constructs. The results show that all five BDA characteristics significantly enhance the quality and efficiency of decision-making, which in turn positively impacts organizational performance. Furthermore, multi-group analysis revealed no significant difference in the BDA–decision-making relationship between medium and large enterprises. This study contributes theoretically by integrating BDA with decision-making theory and practically by offering managers evidence-based insights on how to leverage big data for more informed and effective decision-making across industrial operations.

Emotion is a psychological state of mind that is closely related to various sensations, such as happiness, excitement, neutrality, anger, and frustration. In the modern technological era, emotions play a crucial role in understanding human behavior and motivations. They also have a significant impact on human-computer interactions. If machines can understand human emotions, it would be a remarkable advancement in natural language processing, particularly in the domain of sentiment analysis. Humans experience a wide range of emotions that can change over time. If machines could interpret these emotions and adapt their interactions accordingly, it would greatly enhance the ease and effectiveness of communication between humans and machines. In our study, we employed intelligent algorithms from the field of artificial intelligence, including Naïve Bayes and K-Nearest Neighbor, to interpret human emotions. We utilized speech datasets in the form of audio, sourced from the IEMOCAP database. These algorithms demonstrated the ability to recognize and interpret various emotional states, such as anger, happiness, neutrality, frustration, excitement, and sadness.

Based on social exchange theory, this research introduced a model to understand factors that contribute towards inclusive leadership (IL) and its effect on innovative work behavior (IWB) with employee engagement (EE) as a mediator. Although the connection between IL and IWB is previously established. However, few studies have tested the potential mediation effect of EE particularly in the IT sector. Therefore, this investigation provides new insights as to how IL stimulates the innovation through the meditating impact of EE. Data collected through questionnaire were then analyzed through structural equation modeling. 116 participants have provided the data for this study. We found a positive association of IL with IWB, while the mediating effect of EE was also empirically established. This study is novel because it explained how IL can persuade their subordinates to exhibit positive behavioral outcome (i.e. IWB). Furthermore, the current study also might help the policymakers to devise effective leadership training programs so that their managers may adopt IL style and make the employees more engaged and innovative.

Drawing upon the componential theory of creativity and social information processing theory, this research elucidates how and why the synergy of green HR practices and green strategies stimulate green creativity. It also explores the possible mediation effect of green transformational leadership (TFL) on the relationship between organizational interventions and green creativity. Survey questionnaires were used to collect data from managers working in large manufacturing firms. The structural equation modeling technique was applied to test the hypothesized relationships. Findings of research revealed that green management initiatives and green TFL stimulate green creativity. Moreover, the intervening impact of green TFL on the relationship between the aforementioned relationships was also established. Policymakers should devise green strategies and provide support to green HR practices for the stimulation of green creativity, whereas HR managers must ensure the compatibility of HR functions with corresponding organizational green strategies. Employees involved in green creative behaviors should be rewarded and retained. Training must be provided in order to keep employees abreast of the latest practices for environment conservation. Furthermore, managers should exhibit a green TFL style to advance green management initiatives and fuel green creativity among employees. This study highlights the significance of the synergy between green HR practices and the firm’s green strategies to stimulate employees’ green creativity. Furthermore, green management initiatives were also found to be the contextual precursor of green TFL, which enhances our understanding of the green TFL style. Lastly, the mediation effect of green TFL implies that it can serve as a proximal HR outcome to implement the organizational green agenda.

The purpose of mentioned study is to observe the impact of internet usage on the academic performance of undergraduate students in different universities of Karachi. Though study on appearance reflects the entire Pakistan but for convenience, it is limited only to the Karachi City for data collection. The accessible population is the university students of Karachi city only. The research is conducted by adopting descriptive survey method, and convenience sampling technique was used in order to collect data from the respondents on 5-point Likert Scale. The primary data were collected by researchers' designed questionnaire while secondary data were collected via different journals. T-test and ANOVA were used for the purpose of data analysis. The findings of this exploration revealed that the usage of internet among the undergraduate students is helpful in enhancing their academic performance, and it also showed that male students are more inclined towards using internet as compared to female students. The result of this investigation can be opted by the other higher education institute and universities in Pakistan. It is recommended that university and institute supervisors must provide proper guidance to the students regarding the appropriate use of the internet so that students can enhance their performance positively.