Search results

The purpose of this systematic literature review was to collect and review the studies published worldwide in English language presenting the persuasive role of information/content credibility (IC) on the information adoption behavior (IAB) of social media users.

The relevant literature was searched by the reviewers from two specialized databases, i.e. Library, Information Science and Technological Abstract (LISTA) and Library and Information Science Abstracts (LISA), whereas five general databases, i.e. Emerald, Springer Link, Taylor and Francis Online, ProQuest Dissertations and Theses Global and Google Scholar, using the Internet (portal) services available at the two Pakistani universities, i.e. University of Sargodha and the University of the Punjab. The search was updated in February 2022.

The results of this study revealed that there was a significant impact of IC over the IAB of social media users.

This study is the first of its nature that aims to collect and present a systematic review of the literature based on empirical evidence of the influential role of IC on the IAB of social media users.

This study aims to examine sustainability reporting through the lens of global reporting initiative (GRI) standards in developing economies, particularly in Pakistan, from the perspective of stakeholder theory, legitimacy theory, and system theory.

Qualitative and quantitative analyses on economic, social and environmental areas of sustainability reporting based on the GRI standards are applied across 57 organizations listed on the Pakistan stock exchange over the years 2016–2020.

The results from the content analysis and descriptive statistics show that overall sustainability reporting increased persistently over time and limited organizations disclose economic, social and environmental sustainability based on GRI standards. Moreover, the result from the two-tailed correlation analysis shows positive relations between economic, social and environmental sustainability reporting.

Following the GRI standards, the regulators, government and policymakers need to assess the sustainability reporting based on GRI standards to improve corporate operations' transparency, stakeholder trust and legitimacy. The organizations should move beyond the compliance of regulatory norms and adopt the globally accepted sustainability GRI standards to improve sustainability reporting. The same kind of sustainability reporting is also advised for other countries with similar backgrounds and sustainability challenges.

The integrated sustainability reporting framework based on GRI standards enables the organizations to work as a system of interconnected economic, social and environmental sustainability to resolve the issue of sustainability reporting, ensure the trust of multiple stakeholders and legitimize their business operations in society.

To the best of the authors' knowledge and thorough review of literature, this is the first study that examines the sustainability reporting based on GRI in the developing country of Pakistan to extend the findings of previous studies from conventional sustainability reporting to the globally accepted GRI based sustainability reporting. Using system theory, this study provides an additional contribution to the consideration concerning sustainability reporting based on GRI standards in the context of Pakistan.

Science and technology parks (STP) are established to facilitate innovation and the rapid development of cutting-edge technologies. The innovation performance of tenants is the primary feature of all successful STPs globally. The purpose of this study is to investigate firms’ innovation and economic performance at the National Science and Technology Park (NSTP), Islamabad, Pakistan.

The CDM (the acronym of the three authors’ names, Crépon, Duguet and Mairesse) model following a two-stage approach was used to analyze the survey data of 105 tenants. The innovation performance of tenants was estimated through probit regression at Stage 1, and the economic performance of tenants given their innovation performance was examined at Stage II using Tobit regression and the Heckman model.

The findings suggest that compatibility of innovation with the existing competitive advantage of a firm increases the innovation performance of firms, whereas collaboration of firms with NUST for research and development has only a marginal effect on innovation performance. However, the tenant’s business and social networking were weak, possibly due to the short time spent on NSTP.

These STPs are expected to be hubs of technology development and transfer by fostering open innovation through internal and external collaborations. To the best of the authors’ knowledge, this is the first study to estimate the innovation performance of tenants at NSTP, the first fully integrated STP in Pakistan. Despite shortcomings, the innovation and economic performance of NSTP tenants warrant further public policy support to inculcate open innovation culture.

This book chapter aimed to examine the relationships between green supply chain practices and environmental performance in Malaysian Small and Medium Enterprises (SMEs). The practices of green supply chain in achieving environmental performance have been one priority concerns in Malaysia. However, green supply chain practices adoption remains emergence.

This manuscript adopted the multiple regression analysis in investigating the green supply chain (GSC) practices and environmental performance's variables. The self-administered surveys were randomly disseminated to Malaysian SMEs and 59 responses were returned. The result theoretically ascertained the positive relationship between GSC practices and environmental performance measures.

The findings are in aligned with the Resource-Based View (RBV) theory that conceptualized the GSC practices and strategies to sustain the environmental performance within the SMEs. In a nutshell, these findings would serve as the research implications and recommendations to the scholars, industrial practitioners, and policymakers who are interested in these GSC practices and environmental performance. This would further serve as a guideline for companies that tend to implement these GSC practices for improving its environmental performance.

The research revealed that ‘eco-design and packaging’ and ‘reverse logistics’ are significant to environmental performance, but both ‘green procurement’ and ‘investment recovery’ are not significant to environmental performance. The literature gaps exhibited for this manuscript; hence, future studies should be carried out on supply chain practices and environmental performance since there is no prescriptive method in sustaining environmental performance that matches all conditions in Malaysian SMEs.

The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.

For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.

The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.

In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

This study aims to investigate the complex relationship between green intellectual capital (GIC), green human resource management (GHRM) and green innovation (GI) for improvement in the environmental performance (EP) of an organization.

The data collected from 456 food manufacturing firms in Pakistan was used for structural equation modeling through SmartPLS. Hypotheses were tested through path analysis, predictive relevance and effect size of variables.

The findings show that GIC and GHRM have no direct impact on EP; instead, GI plays a mediating role to make GIC and GHRM helpful in improving an organization’s EP. Also, the environmental strategies play a significant role in the EP and act as a moderator in the relationship between GI and EP.

The Global Climate Risk Index has ranked Pakistan as the fifth most vulnerable to climate change. Industrial activities are contributing significantly to carbon emissions, and therefore, it is vital to mitigate and adapt to climate change to improve the organization’s EP. The findings of this study show that GIC, GHRM and GI can significantly enhance the EP of food manufacturing firms in Pakistan.

The present research aims to empirically test the “Barriers to abusive supervision model” to find how employee-related (core self-evaluations) and situational factors (perceived job dependency) make an employee trapped in the spiral of supervisory abuse. In addition, the work–family spillover lens is used to explain how employees' retaliation is targeted at their families in response to abuse from their bosses.

The current study has employed a three-wave longitudinal moderated mediation design and analysed data from 265 employees working in the hospitality industry of Pakistan.

The results of this study have shown that low core-self evaluations put employees in a spiral of supervisory abuse and they instil aggression towards their families. This association is further strengthened when employees are dependent on their job.

This study is one of the first to use the “Barriers to Abusive supervision” model to answer who and in which conditions tend to trap in the spiral of abuse and integrate the work-to-family interface model for elaborating the outcomes to the family domain.

Big Data can be utilised for efficient use of resources and to provide better services to the resident in order to enhance the delivery of urban services and create sustainable build environment. However, the adoption of Big Data faces many challenges at the implementation level. Therefore, the purpose of this paper is to identify the challenges towards the efficient application of Big Data in smart cities development and analyse the inter-relationships.

The 14 Big Data challenges are identified through the literature review and validated with the expert’s feedback. After that the inter-relationships among the identified challenges are developed using an integrated approach of fuzzy Interpretive Structural Modelling (fuzzy-ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (fuzzy-DEMATEL).

Evaluation of interrelationships among the challenges suggests that diverse population in smart cities and lack of infrastructure are the significant challenges that impede the integration of Big Data in the development of smart cities.

This study will enable practitioners, policy planners involved in smart city projects in tackling the challenges in an optimised manner for the hindrance free and accelerated development of smart cities.

This research is an initial effort to develop an interpretive structural model of Big Data challenges for smart cities development which gives a clearer picture of how the identified challenges interact with each other.

The purpose of this paper is to evaluate the impact of short-term loan (STL) vs long-term loan (LTL) on wheat productivity of small farms in Sindh, Pakistan.

The econometric estimation is based on cross-sectional data collected in 2016 from 18 villages in three districts, i.e. Shikarpur, Sukkur and Shaheed Benazirabad, Sindh, Pakistan. The sample data set consist of 180 wheat farmers. The collected data were analyzed through different econometric techniques like Cobb–Douglas production function and Instrumental variables (two-stage least squares) approach.

This study reconfirmed that agricultural credit has a positive and highly significant effect on wheat productivity, while the short-term loan has a stronger effect on wheat productivity than the long-term loan. The reasons behind the phenomenon may be the significantly higher usage of agricultural inputs like seeds of improved variety and fertilizers which can be transformed into the wheat yield in the same year. However, the LTL users have significantly higher investments in land preparation, irrigation and plant protection, which may lead to higher wheat production in the coming years.

In the present study, only those wheat farmers were considered who obtained agricultural loans from formal financial institutions like Zarai Taraqiati Bank Limited and Khushhali Bank. However, in the rural areas of Sindh, Pakistan, a considerable proportion of small-scale farmers take credit from informal financial channels. Therefore future researchers should consider the informal credits as well.

This is the first paper to examine the effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan. This paper will be an important addition to the emerging literature regarding effects of credit studies.