Impact of comic factor in TV ads on buying behavior of university students

This study was carried out to find out the impacts of comic factor in TV advertisement on buying behavior of college and university students. Questionnaire was designed and used to gauge the effects of comedy in advertisements. Sample size was 120 and it was comprised of university business students (Preston University Islamabad, International Islamic University Islamabad and Qauid-e-Azam University Islamabad). Data Analysis provided coefficient of determination R square value as 0.576. With significant Alpha figure of all variables, F value was 26.907 at 0.01 level. Outcomes revealed that comedy in TV advertisement convinces the consumer’s attention, interest, desire and action (AIDA) to buy the product. Objective of the study was achieved as it was found out that comic factor in TV ads affect the attention, interest, desire and action and hence buying behavior of students. Results also revealed that buying behavior of students due to comic factor in TV ads is gender free

Estimation of Laffer Curve: Evidence from Pakistan

Laffer curve is a tradeoff between tax cuts and tax revenues. The sketch of Laffer curve was drawn by Arther Laffer at a dinner with his friends on his napkin that reduction in taxes will increase tax revenues. Lafer curve explained that lower tax rate could encourage an additional output and it would increase income, which builds bigger tax base in an economy. In this research we have estimated the Laffer curve on the basis of Taxes on goods and services for Pakistan economy for the period of 1990 to 2016. Time series data have been collected from the world economic data base. Results explained that all the variables are significant and signs of coefficients are in accordance with the theory. Results confirm that Laffer curve is alive in Pakistan economy but its existence is in the prohibited area of Laffer curve. It is suggested that plans of taxation should be modified in such a way that maximum number of people could bring under the tax brackets. The taxation structure of the economy should ensure to lower the indirect taxes, encourage the progressive taxes and elasticity of taxation should be increased. The pattern of taxation should be encouraged after introducing some sort of incentives such as relief in the income tax and higher bank deposit rates should be realize

Modeling of open government data for public sector organizations using the potential theories and determinants-A systematic review

Open government data (OGD) has huge potential to increase transparency, accountability, and participation while improving effciency in operations, data-driven and evidence-based policymaking, and trust in government institutions. Despite its potential benefits, OGD has not been widely and successfully adopted in public sector organizations, particularly in developing countries. Therefore, the purpose of this study is to explore the theories/frameworks and potential determinants that influence the OGD adoption in public sector organizations. To ascertain the various determinants of OGD adoption in public sector organizations, this study involved a systematic review of already established theories and determinants addressed in the public sector open data domain. The review revealed that the TOE (technology, organization, environment) framework was dominantly employed over theories in the earlier studies to understand organizational adoption to OGD followed by institutional theory. The results, concerning potential determinants, revealed that some of the most frequently addressed determinants are an organization's digitization/digitalization capacity, compliance pressure, financial resources, legislation, policy, regulations, organizational culture, political leadership commitment, top-management support, and data quality. The findings will enrich researchers to empirically investigate the exposed determinants and improve the understanding of decision-makers to leverage OGD adoption by taking relevant measures

Factors influencing citizens’ intention to use open government data—A case study of Pakistan

Open government data (OGD) has gained much attention worldwide, however, there is still an increasing demand for exploring research from the perspective of its adoption and diffusion. Policymakers expect that OGD will be used on a large scale by the public, which will result in a range of benefits, such as: faith and trust in governments, innovation and development, and partic-ipatory governance. However, not much is known about which factors influence the citizens’ intention to use OGD. Therefore, this research aims at empirically investigating the factors that influence citizens’ intention to use OGD in a developing country using information systems theory. Improved knowledge and understanding of the influencing factors can assist policymakers in determining which policy initiatives they can take to increase the intention to widely use OGD. Upon conducting a survey and performing analysis, findings reveal that perceived usefulness, social approval, and enjoyment positively influences intention, whereas voluntariness of use negatively influences OGD use. Further, perceived usefulness is significantly affected by perceived ease of use, and OGD use is significantly affected by OGD use intention. However, surprisingly, the intention to use OGD is not significantly affected by perceived ease of use. The policymakers suggest increasing the intention to use OGD by considering significant factors

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Open government data adoption model for public sector organizations in Pakistan

Open government data (OGD) is an integral part of open government policies and is referred to as making any public sector data and information available in formats and ways that enable free access, use and distribution and facilitate exploitation. OGD bears a pivotal role and a new way of increasing transparency and accountability, counteracting corruption, stimulating innovation and economic growth, facilitating participation and collaboration and improving services. Further, it is vital means of achieving sustainable development goals (SDGs). Despite these motivations, there is very limited OGD among public sector organizations in Pakistan which is affected by several technological, organizational, and environmental factors. Besides, the lack of a theoretical model for determining OGD adoption in Pakistan‘s public sector is recognized as the main gap. Therefore, the primary objective of this study is to develop and test the OGD adoption model for public sector organizations in Pakistan. A quantitative research method was adopted to accomplish the primary research objective. The data from 249 decision-makers of public sector organizations were collected through a survey method using judgment sampling, a purposive sampling technique. Data analysis was carried out using descriptive statistics in SPSS 25.0 and regression in Partial Least Squares-Structural Equation Modelling (PLS-SEM) using SmartPLS (version 3.3.3). Results revealed that data resource, dataset quality, perceived benefits, data-driven culture, digitization capacity, need for transparency, compliance pressure, and civil society participation were the factors that influence OGD adoption intention. Further, the organization‘s OGD adoption intention leads to influence the OGD adoption positively. However, centralization did not influence the intention to adopt the OGD of public sector organizations in Pakistan. The total variance of 59.1% is explained by various factors for adoption intention, whereas 61% variance is explained by adoption intention for OGD adoption. Theoretically, this study validates the TOE (technology, organization, environment) framework in the context of OGD adoption with adoption intention as a significant positive factor of OGD adoption. Practically, the study suggests considering data resources, dataset quality, perceived benefits, need for transparency, and compliance pressure by the government, policy practitioners, and public sector organizations for increasing adoption of OGD on a large scale among public sector organizations in Pakistan. Future studies should conduct multi-group analysis on different demographic characteristics like crises and emergencies (COVID-19), organization size, and officers appointed for OGD tasks in organizations and organizations that have e- government/OGD platforms or not. Further, future scholars should investigate the inter- relationships between technological, organizational, and environmental factors

Big data value dimensions in food disaster domain

The age is turned into Big Data which includes huge volume of data, real-time processing and complex operations. In this connection, the way to understand the world is reshaping with the growing trends of big data; enabling the handling of voluminous datasets with variety and velocity. The utility and potential of big data for natural disaster are growing, since the data volume and access to datasets are increasing rapidly. The main challenge in realizing social and commercial value of big data is that data has no value in itself, yet it becomes valuable when it is used. Therefore, the objective of this paper is to identify the different dimensions of big data value based on its usage in flood disaster domain. We find three dimensions of big data value to be classified as Natural, Social and Business. The natural aspect of flood disaster includes Climate Change analysis, Earth Observation and Remote Sensing. Social aspect focuses on interlink of flood and human society whereas Business aspect covers using data-based decision-making systems for managing flood. The different dimensions give broader view of data usage for flood disaster in big data era

Investigating the Determinants of the Adoption of Solar Photovoltaic Systems—Citizen’s Perspectives of Two Developing Countries

The adoption of solar photovoltaic (PV) systems is seen as an important part of the sustainable energy transition. In this regard, it is crucial to identify the determinants of solar (PV) systems’ adoption to facilitate this process. Therefore, this article aims to examine the determinants of SPVS adoption by contrasting the relationships in a cross-cultural environment. For the accomplishment of the purpose, this paper follows a quantitative method in which data is analysed by adopting the PLS-SEM approach using SmartPLS 3.3.9. After analysing the collected data of 464 consumers from Somalia and Pakistan, it is found that perceived usefulness, perceived ease-of-use, compatibility, observability, and perceived trust are significant predictors. However, no significant difference in influencing determinants has been observed between the two cultures using multi-group analysis. Further, perceived trust is not revealed as a significant determinant of behavioural intention in the Somalian context. The strongest relationship is found between attitude and behavioural intention in both cultures. In Somalia, the results reveal a variance of 49% in attitudes and 51% in intention to adopt SPVSs. In Pakistan, a variance of 60.1% in attitudes and 76.8% in intention to adopt SPVSs is found. Implications for both academics and managers to scale-up the adoption of SPVSs are made

Examining the factors of open government data usability from academician's perspective

This article examines factors that can be argued to influence the academician's behavioral intentions in using open government data (OGD). Policy-makers and practitioners will determine policy instruments in increasing acceptance and use of OGD by maintaining a good understanding of these factors. In this article, Rogers' Diffusion of Innovations (DOI) theory has been proposed and used in order to empirically examine these factors taking perceived characteristics of innovations. Relevant hypotheses have been developed through the literature review, forming a preliminary research model, while respective influences of the factors on the behavioral intention to use open government data have been statistically tested. Results have shown that compatibility and voluntariness have had a strong influence on behavioral intention, whereas a 66.2% variance has been found in academicians' behavioral intentions to use open government data

FACTORS INFLUENCING DIGITAL PAYMENTS ADOPTION IN PAKISTAN

Digital payments are the financial transactions enabled through digital technologies and bear the huge potential to bring change in the social life of unbanked masses in developing and developed countries. However, a challenge encountered among developing countries is the insignificant adoption of digital payments caused by different factors of human, technological, economic and legal nature. Therefore, the purpose of this research is to empirically examine the factors that influence consumers’ adoption of digital payments in Pakistan by employing information systems theory. A better understanding of these factors can help policymakers to develop policy instruments they can implement to increase the adoption of digital payments in Pakistan. A survey was conducted to collect data. A simple random sampling technique was used. The data was analyzed using Variance-based Structural Equation Modeling (VB-SEM) to test the proposed hypotheses. Performance expectancy, facilitating conditions, and grievance redressal were the significant predictors of citizen’s use intention and lead to use behaviour. Interestingly, a 62.6% variance in behavioural intention and 55.9% in use behaviour of digital payments has been found. Future researchers should investigate the adoption of digital payments on a large population to find concrete evidence and greater insights