Junaid Iqbal, Mubashir Ahmad Aukhoon and Zahoor Ahmad Parray
This study examines the complicated relationship between psychological wellbeing, joy at work, creative ability and the mediating influence of psychological capital, drawing…
Abstract
Purpose
This study examines the complicated relationship between psychological wellbeing, joy at work, creative ability and the mediating influence of psychological capital, drawing insights from self-determination theory within the context of the banking sector.
Design/methodology/approach
The study utilized random sampling to gather comprehensive data from 465 banking employees. Following data collection, structural equation modeling was employed to test the hypotheses formulated based on the collected data.
Findings
Findings underscore a significant association between psychological wellbeing and both joy at work and creative ability within the banking sector. Moreover, the study elucidates that psychological capital acts as a crucial mediator, illuminating the pathway through which psychological wellbeing influences joy at work and subsequently enhances creative ability.
Practical implications
This research offers valuable insights for organizational leaders and policymakers, emphasizing the imperative of prioritizing psychological wellbeing initiatives and nurturing positive work environments to enhance employee satisfaction, productivity and innovative contributions within the banking sector.
Originality/value
The application of self-determination theory as a theoretical framework provides a robust foundation for understanding the dynamics between psychological factors and workplace outcomes. The banking industry, often characterized by high stress levels and demanding work environments, stands to benefit substantially from interventions aimed at fostering psychological wellbeing. By cultivating positive mental health and capitalizing on joy at work, organizations can stimulate employee creativity ability, thereby fostering innovation and adaptive problem-solving capabilities crucial in the contemporary banking landscape.
Details
Keywords
Mubashir Ahmad Aukhoon, Junaid Iqbal and Zahoor Ahmad Parray
The primary objective of this study was to understand the impact of Corporate Social Responsibility on Employee Green Behavior, examining the mediating role played by Green Human…
Abstract
Purpose
The primary objective of this study was to understand the impact of Corporate Social Responsibility on Employee Green Behavior, examining the mediating role played by Green Human Resource Management Practices and the moderating influence of Employee Green Culture.
Design/methodology/approach
To accomplish this, a careful research approach was taken, using a thoughtfully designed random sampling method to encompass 300 banking employees, ensuring a robust representation of the diverse workforce in the banking sector.
Findings
The empirical findings identified green human resource management practices as a pivotal mediator and employee green culture as a significant moderator. It elucidated how the strategic implementation of green human resource management practices can act as an amplifier, strengthening the positive effects of corporate social responsibility on employee green behavior. This insight underscores the strategic importance of aligning human resource practices with sustainability goals to further enhance the environmental consciousness of employees. It was revealed that the presence of a nurturing organizational culture, one that encourages and supports environmentally responsible behaviors can significantly bolster the association between corporate social responsibility and green behavior among employees.
Originality/value
These findings underscore the essential role of organizational culture as a catalyst for the successful implementation of corporate social responsibility initiatives and the cultivation of a sustainable corporate ethos. This comprehensive research underscores the profound significance of corporate social responsibility, green human resource management practices and employee green culture in fostering and promoting environmentally responsible behaviors within the banking industry. These findings hold substantial implications not only for businesses but also for policymakers.
Details
Keywords
Muhammad Usman Shehzad, Jianhua Zhang, Sajjad Alam, Ziao Cao, Fredrick Ahenkora Boamah and Mubashir Ahmad
Drawing on the knowledge-based view (KBV), the purpose of the study is to examine the impact of collaborative culture (CC) on frugal innovation (FI). It also advances insight into…
Abstract
Purpose
Drawing on the knowledge-based view (KBV), the purpose of the study is to examine the impact of collaborative culture (CC) on frugal innovation (FI). It also advances insight into the pathways for stimulating distinct aspects of innovation capacity by assessing the mediating effects of knowledge management (KM) processes and the moderating role of perceived organizational support (POS).
Design/methodology/approach
Based on the data gathered from 430 participants from 80 Pakistani manufacturing and service firms, this study used structural equation modeling to evaluate hypotheses in the established research model.
Findings
The findings reveal that CC positively fosters the KM processes and different aspects of FI. The results indicated the positive direct impact of KM processes on frugal functionality (FF) and frugal cost (FC) while insignificant on the frugal ecosystem (FE). This study found partial mediation of KM processes on the relationship among CC, FF and FC, but the KM process does not mediate the relationship between CC and FE. The results also demonstrated that POS moderation enhances the impacts of CC on KM processes and FF while notably weakening the impacts of CC on FC and FE.
Research limitations/implications
To understand the crucial role of knowledge capital in companies’ innovation capability, future research should examine the mediating function of KM capability (knowledge process capability and knowledge infrastructure capability) and moderating role of environmental turbulence in the relationship between CC and different aspects of innovation capability.
Practical implications
This study significantly advances a better understanding of the relationship between CC and specific facets of innovation capacity by emphasizing the importance of driving the KM process and improving POS.
Originality/value
This study has contributed to the theoretical and practical efforts on KBV, emphasizing the critical importance of CC in fostering a conducive environment for KM processes and innovation.
Details
Keywords
Ismail Abiodun Sulaimon, Hafiz Alaka, Razak Olu-Ajayi, Mubashir Ahmad, Saheed Ajayi and Abdul Hye
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully…
Abstract
Purpose
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.
Design/methodology/approach
To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM2.5, PM10, NO2 and O3 in the atmosphere at various times of the day.
Findings
The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.
Research limitations/implications
This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.
Practical implications
This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.
Originality/value
The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.
Details
Keywords
Fredrick Ahenkora Boamah, Jianhua Zhang, Muhammad Usman Shehzad and Mubashir Ahmad
This research aims to establish a comprehensive approach that integrates the aspects to describe how knowledge is focused, developed, reassigned, and implemented to increase…
Abstract
Purpose
This research aims to establish a comprehensive approach that integrates the aspects to describe how knowledge is focused, developed, reassigned, and implemented to increase project effectiveness. This study examines the interaction of social factors that influences tacit knowledge sharing, absorptive capacity, and project site performance.
Design/methodology/approach
The data were collected from Chinese project-based organizations and examined using structural equation modeling (SEM) to test the model and evaluate the hypothesis.
Findings
The findings reveal that good knowledge governance and tacit knowledge sharing are essential prerequisites to boost the project’s absorptive capability. Furthermore, social dynamics favorably modify the link between absorptive capacity, tacit knowledge sharing, and project results. The findings are supportive of the proposed model in general.
Originality/value
This research addresses the critical issue of project knowledge management systems and presents a comprehensive framework that broadens the technical and interpretative bounds of current models designed to achieve project success.
Details
Keywords
Rahat Gulzar, Sumeer Gul, Manoj Kumar Verma, Mushtaq Ahmad Darzi, Farzana Gulzar and Sheikh Shueb
Sharing and obtaining information over social media has enabled people to express their opinions regarding any event. Since the tweets regarding the Russia-Ukraine war were…
Abstract
Purpose
Sharing and obtaining information over social media has enabled people to express their opinions regarding any event. Since the tweets regarding the Russia-Ukraine war were extensively publicized on social media, this study aims to analyse the temporal sentiments people express through tweets related to the war.
Design/methodology/approach
Relevant hashtag related to the Russia-Ukraine war was identified, and tweets were downloaded using Twitter API, which were later migrated to Orange Data mining software. Pre-processing techniques like transformation, tokenization, and filtering were applied to the extracted tweets. VADER (Valence Aware Dictionary for Sentiment Reasoning) sentiment analysis module of Orange software was used to categorize tweets into positive, negative and neutral ones based on the tweet polarity. For ascertaining the key and co-occurring terms and phrases in tweets and also to visualize the keyword clusters, VOSviewer, a data visualization software, was made use of.
Findings
An increase in the number of tweets is witnessed in the initial days, while a decline is observed over time. Most tweets are negative in nature, followed by positive and neutral ones. It is also ascertained that tweets from verified accounts are more impactful than unverified ones. russiaukrainewar, ukraine, russia, false, war, nato, zelensky and stoprussia are the dominant co-occurring keywords. Ukraine, Russia and Putin are the top hashtags for sentiment representation. India, the USA and the UK contribute the highest tweets.
Originality/value
The study tries to explore the public sentiments expressed over Twitter related to Russia-Ukraine war.
Details
Keywords
Aasif Ahmad Mir, Sevukan Rathinam and Sumeer Gul
Twitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic…
Abstract
Purpose
Twitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.
Design/methodology/approach
To fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.
Findings
A gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.
Research limitations/implications
The main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.
Practical implications
The study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.
Originality/value
The paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.
Details
Keywords
Sumeer Gul, Tariq Ahmad Shah, Muzaffer Ahad, Mir Mubashir, Suhail Ahmad, Muntaha Gul and Shueb Sheikh
The study aims to showcase public sentiments via social media, Twitter, during 2014 floods of Jammu and Kashmir, India.
Abstract
Purpose
The study aims to showcase public sentiments via social media, Twitter, during 2014 floods of Jammu and Kashmir, India.
Design/methodology/approach
The study is based on content analysis of tweets related to Kashmir floods. Search was performed with “#kashmirfloods” and was confined to tweets posted from 4 September 2014 through 3 November 2014. A naturalistic approach was applied to examine the content and classify tweets into 5 major and 25 sub categories. Data as such collected were tabulated in SPSS 21 for analysis.
Findings
During the study period, individuals, news channels, and organisations posted a total of 36,697 tweets related to Kashmir floods. It all started with an outburst of tweets which goes on declining (exponentially) with every passing day. People express themselves in a number of ways with informational tweets used more during the time of disaster. Individuals expressing their sentiments outscore other types of sentiments with text-based tweets ranking high. About 44 per cent of tweets were retweeted, and nearly 31 per cent tweets were marked favourite. Comparatively, more number of informational and help tweets were retweeted or marked favourite. Contextual richness of tweet (i.e. number of embedded expressions) enhances its visibility by means of getting liked and/or retweeted. A statistically significant positive association is observed between the number of expressions in a tweet and the number of times it is liked (favourite) or retweeted.
Research limitations/implications
Twitter plays a pivotal role during natural calamities like Kashmir floods to connect people in the hour of need and help. It provides a platform where the plight of people is heard across the globe and which encourages people to unite and overcome hurdles together.
Originality/value
This study examines the sentiments of people expressed during Jammu and Kashmir (India) Floods 2014 on social media – Twitter.
Details
Keywords
Rehan Zahid, Masjuki Hj. Hassan, Abdullah Alabdulkarem, Mahendra Varman, Md. Abul Kalam, Riaz Ahmad Mufti, Nurin Wahidah Mohd Zulkifli, Mubashir Gulzar, Muhammad Usman Bhutta, Mian Ashfaq Ali, Usman Abdullah and Robiah H. Yunus
There is a continuous drive in automotive sector to shift from conventional lubricants to environmental friendly ones without adversely affecting critical tribological performance…
Abstract
Purpose
There is a continuous drive in automotive sector to shift from conventional lubricants to environmental friendly ones without adversely affecting critical tribological performance parameters. Because of their favorable tribological properties, chemically modified vegetable oils such as palm trimethylolpropane ester (TMP) are one of the potential candidates for the said role. To prove the suitability of TMP for applications involving boundary-lubrication regime such as cam/tappet interface of direct acting valve train system, a logical step forward is to investigate their compatibility with conventional lubricant additives.
Design/methodology/approach
In this study, extreme pressure and tribological characteristics of TMP, formulated with glycerol mono-oleate (GMO), molybdenum dithiocarbamate (MoDTC) and zinc dialkyldithiophosphate (ZDDP), has been investigated using four-ball wear tester and valve train test rig. For comparison, additive-free and formulated versions of polyalphaolefin (PAO) were used as reference. Moreover, various surface characterization techniques were deployed to investigate mechanisms responsible for a particular tribological behavior.
Findings
In additive-free form, TMP demonstrated better extreme pressure characteristics compared to PAO and lubricant additives which are actually optimized for conventional base-oils such as PAO, are also proved to be compatible with TMP to some extent, especially ZDDP. During cylinder head tests, additive-free TMP proved to be more effective compared to PAO in reducing friction of cam/tappet interface, but opposite behavior was seen when formulated lubricants were used. Therefore, there is a need to synthesize specialized friction modifiers, anti-wear and extreme pressure additives for TMP before using it as engine lubricant base-oil.
Originality/value
In this study, additive-free and formulated versions of bio-lubricant are tested for cam/tappet interface of direct acting valve train system of commercial passenger car diesel engine for the very test time. Another important aspect of this research was comparison of important tribological performance parameters (friction torque, wear, rotational speed of tappet) of TMP-based lubricants with conventional lubricant base oil, that is, PAO and its formulated version.
Details
Keywords
Sumaiya Usman, Fazeelat Masood, Mubashir Ali Khan and Naveed ur Rehman Khan
This paper aims to address essential questions regarding social entrepreneurial intentions. Do traits such as perceived social impact, social worth and social network influence…
Abstract
Purpose
This paper aims to address essential questions regarding social entrepreneurial intentions. Do traits such as perceived social impact, social worth and social network influence, social entrepreneurial intentions among the young populous generation of Pakistan? To get a deeper insight, this paper further raises questions regarding the relationship of these predictors and social entrepreneurial intentions with empathy which is considered as a key determinant and a distinguishing trait to become a social entrepreneur.
Design/methodology/approach
This paper involves a quantitative research design using a partial least square structural equation modeling approach to measure the effects of the structural model. For this, a cross-sectional survey was conducted with a purposive sample of 247 university students from Pakistan.
Findings
Results showed a positive relationship between antecedents and social entrepreneurial intentions. Overall analysis exhibited social worth as a dominant trait and social network as the least influencing trait to impact social entrepreneurial intentions.
Practical implications
It will help micro and macro-level policymakers including government officials and NGOs and educators to create awareness and provide support and encouragement to individuals who aim to initiate social enterprise.
Originality/value
The present study makes significant contributions to the social entrepreneurship literature, as it is one of the first academic studies on social entrepreneurial intentions in Pakistan. This paper enriches the theoretical foundation by assessing the influence of perceived social impact, social worth and social network on social entrepreneurial intentions. Also, the relationship of Empathy with each of these antecedents is examined for the first time in the social entrepreneurial intentions context which is a valuable contribution both theoretically and practically.