Islamic Branding and Brand Resonance: A Multi-Group Analysis of Malaysia & Pakistan

The study examines the underlying heterogeneity within the global Muslim consumer market in connection with Islamic branding and its impact on the brand resonance of an Islamic brand. This comparative study empirically examines the differences existing within the Muslim consumer in Malaysia and Pakistan with respect to the influence of religiosity, Islamic brand knowledge and Islamic corporate social responsibility (ICSR) on Islamic branding perceptions and its subsequent impact on the brand resonance of an Islamic brand. This study uses the Partial Least Squares - Structural Equation Modeling (PLS-SEM) analysis in which the constructs scores are calculated according to a composite algorithm. The Measurement Invariance of Composite Models (MICOM) was applied before conducting Multi-Group Analysis (MGA) in PLS-SEM. The MICOM procedure consisted of three steps, including measurement of configural invariance, measurement of compositional invariance, and assessment of the equality of a composite’s mean value and variance across groups. The study reveals significant differences between the two Muslim consumer markets in terms of Islamic branding antecedents and the influence of this branding ideology on brand resonance of an Islamic brand. It can provide valuable insights to brand managers targeting the global Muslim consumers and policymakers. Currently, limited studies have applied the PLS-SEM, MGA technique through MICOM analysis

Software Project Complexity and Project Success: Mediating Role of Project Dynamism and Moderating Role of Agility-Based Project Management

This article examines the relationship between project complexity and project success while investigating the mediating role of project dynamism, and the moderating role of agility-based project management approach with the application of contingency theory. Quantitative approach has been used for data collection and analysis throughout this study. Data was collected [n = 341] from employees working at different management levels in software project-based firms across Pakistan. For data analysis, SPSS Process Macro was used with simple mediation (model 4) and moderated mediation analysis (model 14). The moderated mediation model 14 depicted that project complexity does not have a significant negative effect on a project’s success, whereas project dynamism mediates the relationship between project complexity and project success. Furthermore, the agility-based project management approach plays a vital role being the only moderator between project dynamism and project success. The present study findings suggested to policy or decisions makers that adoption of agility-based project management practices in dynamic environment can significantly enhance the chances of project success. This study has highlighted the concern that projects with greater number of complexities and dynamism should be handled using agility-based project management approach in order to achieve project success. Organizations must learn implementing agility-based project management approach for projects that are both complex and dynamic in nature

Challenges of Digital Marketing Adoption in FMCG Sector in Pakistan: A MICMAC-ISM Approach

The study is aimed to analyze the interrelationships of challenges of digital-marketing adoption in Fast Moving Consumer Goods (FMCG) sector in Pakistan. The design comprises of review of up-to-date literature, primary data gathering, modeling and analysis. The data are collected through survey from an expert’s panel recruited from stakeholders on the basis of predetermined criteria by using matrix type questionnaire. The literature discourse for extraction of list of challenges, Interpretive Structural Modeling (ISM) to extract the underlying model of interrelationships, and Matriced\u27 Impacts Croise\u27s Multiplication Appliquée a UN Classement (Cross Impact Matrix Multiplication Applied to Classification) popularly known as MICMAC for analysis are employed as research methods. Results of literature survey reveal that there are total fifteen challenges of digital-marketing adoption in FMCG-sector. Results of ISM modeling show that customers’ digital engagement, consumer trust concerns, engaging relevant content, utilizing multi-media channels, integration of AI, continuous optimization, building digital capabilities, marketing innovation, responsive customer service, and handling new sources of data occupy Level I. Managing supplier & customer coordination occupies Level II. Organizational resistance occupies Level III. Integration of online and offline channels occupies Level IV. Data privacy & security occupies Level V. Technological barrier occupies Level VI. The results of data-centric and scale-centric MICMAC analysis substantiate the results of ISM modeling. It is a real time data based unique type of study that provides understanding to stakeholders particularly to marketers, regulators, FMCG mangers, researchers and technologists

Unsupervised Machine Learning Based Anomaly Detection in High Frequency Data: Evidence from Cryptocurrency Market

The rapid integration of cryptocurrencies into the global financial ecosystem has introduced unprecedented challenges in market surveillance, risk management, and anomaly detection. While conventional statistical models such as ARIMA (Autoregressive Integrated Moving Average) and GARCH (Generalized Autoregressive Conditional Heteroscedasticity) have been widely used for anomaly detection, their reliance on assumptions of normality and stationarity often fails to capture the complexities of high-frequency, non-linear cryptocurrency trading. Furthermore, traditional risk metrics including down-to-up volatility, negative conditional skewness, and relative frequency may overlook short-term anomalies due to data aggregation limitations. In order to address these issues, this paper proposes machine-learning model for detecting anomalies in cryptocurrency markets using Jupyter Notebook. We compare four advanced unsupervised machine learning models, i.e, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest (iForest), One-Class Support Vector Machine (OC-SVM), and Local Outlier Factor (LOF) for anomaly detection by using Monte Carlo simulations. The findings indicate that DBSCAN has the highest precision (79.7%) with the fewest false positives, making it ideal for supervisory monitoring. However, the high false positive rates of OC-SVM and Isolation Forest limit their use. By using data of six well-known cryptocurrencies at three different temporal resolutions (daily, hourly, and 15-minute) the performance of these four unsupervised learning techniques also examined and confirmed that the anomalies identified by DBSCAN are also consistent with the other three methods. Additionally, for robustness of results, we use UpSet Plots to incorporate the shared anomalies and found across the three unsupervised learning methods. Number of anomalies also depends on the volatility and time interval of cryptocurrencies, more volatile / high frequency more anomalies. The study presents sound methodological approach for facilitating financial monitoring and mitigating risks in the cryptocurrencies market, and provides useful information for market players, analysts and policymakers. These results emphasize the importance of choosing algorithms based on specific surveillance targets to promote greater stability in digital asset environments

Environmental Sustainability in Technologically Advanced Economies: The Role of Eco-Digitalization, Green Finance, and Green Technology

This study investigates the effects of eco-digitalization, green technology, and green finance on environmental sustainability in the presence of affluence and population. The sample size consists of a panel of 19 technologically advanced economies covering the time span from 1980 to 2023. The econometric model is designed using the STIRPAT framework. The empirical results are based on panel time series analysis. The panel unit root tests illustrate that variables are stationary at the first difference and follow the I (1) order of integration. The panel cointegration test confirms the presence of long-run relationships between the variables. The empirical findings reveal that eco-digitalization, green technology, and green finance help to boost environmental sustainability by reducing carbon emissions and ecological footprints in technologically advanced economies. Furthermore, the empirical investigation proceeds using two major technological phases in the sampled economies. The results reveal heterogeneous effects of technological innovations and population growth on environmental quality across the phases of technological advancement. Our findings are helpful for policymakers, environmentalists, and development practitioners in designing and implementing policies that help mitigate carbon emissions and achieve environmental sustainability

Nexus of Consumer Trust in FinTok Influencers, Consumer Engagement, Data Privacy Concern, Financial Literacy and Travel Scam Avoidance

oai:ojs2.jes.ac.pk:article/1This study aims to investigate the effectiveness of FinTok influencers in preventing online travel scams by enhancing consumer trust, financial literacy, and data privacy concerns. The research examines how these influencers can contribute to improving consumer protection and safety in digital transactions. A survey was conducted with a sample of 250 TikTok users (who actively follow FinTok influencers) and have booked travel online at least once. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS software, the study analyzed the relationships between consumer trust, financial literacy, and data privacy concerns in mitigating exposure to travel scam. PLS-SEM estimations indicate that FinTok influencers play a significant role in building consumer trust and enhancing financial literacy and data privacy concerns, which, in turn, reduce susceptibility to online travel scams. Consumer engagement emerged as a critical mediator, emphasizing the need for influencers to maintain credibility and transparency. Additionally, financial literacy was identified as a key factor in empowering consumers to make informed decisions and avoid travel scams. This study contributes to the literature on digital consumer protection by highlighting the potential of social media influencers in combating online fraud. By focusing on the niche of FinTok, it provides practical insights for organizations seeking to leverage influencers for promoting financial literacy and consumer safety

Consumer Buying Intention in Livestreaming Commerce: An Integrated Model of Socio-Technical System Theory and Uses & Gratifications Theory

This study develops a framework to analyze the determinants influencing consumer buying intention in livestreaming commerce by being a pioneer that integrates socio-technical system theory with the uses and gratifications theory. This study contributes literature on understanding consumer behavior within livestreaming commerce by examining how hedonic and utilitarian gratifications mediate the influence of interactivity, personalization, and visibility on consumer purchasing intention. The research also analyses the moderating role of perception of social media influencers on the link between hedonic gratifications, utilitarian gratifications, and purchase intention. Data were collected from 396 consumers across the TikTok platform in Vietnam market in the sector of home appliances, and partial least squares structural equation modelling (PLS-SEM) was applied for data analysis using Smart PLS 4.0. The results indicate that both hedonic and utilitarian gratifications mediate the relationship between socio-technical determinants including interactivity, personalization, visibility and buying intention. Interestingly, the moderating effect of perception of social media influencer on the relationship between hedonic gratifications and purchase intention is significantly positive, whereas no such moderating impact is evident for the relationship of utilitarian gratifications and purchasing intention. These findings also offer for marketing practitioners aiming to enhance consumer buying intention through livestreaming commerce

Threshold Analysis, Financial Inclusion and Financial Stability in Developing Economies: Assessing the Moderating Role of Digital Financial Inclusion

This study examines the relationship between financial inclusion and financial stability in developing economies, with a focus on the moderating role of digital financial inclusion (DFII) and the identification of threshold effects. Using a dynamic panel analysis with a two-step System GMM estimator, the analysis covers 72 developing countries from 2012 to 2022. A composite financial stability index (FSI) is developed using Principal Component Analysis (PCA) to capture financial soundness and market depth. The inclusion dimensions model results indicate that penetration and usability of financial services have a negative impact on financial stability (FSI). Conversely, accessibility has a positive influence on FSI. However, both indices traditional financial inclusion (TFII) and DFII in model 2 analysis reveal a negative relationship between both indices and financial stability. The results of the third moderation model show that DFII strongly moderates these TFII effects by enhancing access efficiency and transparency. A threshold effect is identified in model 5 analysis of this study, suggesting that the benefits of inclusion diminish and potentially reverse beyond a certain level. The findings suggest the need for balanced financial inclusion policies that integrate both traditional and digital financial services

Times Varying Spectral Coherence Examination of Consumer Price Indices in Pakistan: A Wavelet Transform

Aim of this study is to examine the coherence of consumer price indices (CPI) variants in Pakistan using time series data. The techniques of data analysis are descriptive statistics and wavelet analysis. Plots of CPI variants show more frequent changes as compared to the base year / month from January 1990 to January 2008 and comparatively minor fluctuation subsequently. Wavelet power spectra of CPI General Index (CPI-Gen), CPI Food MoM (CPI-FMoM), CPI General YoY (CPI-GenYoY), and CPI Food YoY (CPI-FYoY) show weak correlation between wavelets and mother wavelet at low frequency bands, and vice versa at high frequency bands in sample period. In CPI Food Index and CPI General MoM, there is very strong correlation between the mother and daughter wavelets. Cross wavelet spectra show that CPI-General vs CPI-Food, CPI General vs CPI General (YoY), and CPI Food vs CPI Food (YoY)) at low frequency bands have weak co-movements, whereas, that is strong at high frequency bands. Cross wavelet spectra of CPI-General vs. CPI General (MoM) and CPI-Food vs. CPI-Food (MoM, at high bands have very strong co-movement. Wavelet coherence spectra show that at low frequency bands there is high coherence and correlation among variables, whereas, that is relatively low at high frequency bands. Wavelet coherence spectra as contained of CPI-General vs. CPI General (MoM), and CPI-Food vs. CPI Food (MoM) at high frequency bands show very weak coherence and correlation and the results also show that both the variables are in phase at most of the frequency and time resolutions

Effect of Financial Inclusion, Energy Efficiency, and Human Capital on Energy Poverty in Developing Countries

This study discovers the link between financial inclusion and energy poverty in developing nations, an area often overlooked. Using a theoretical framework, it investigates optimal associations by analyzing panel data from 45 developing countries from 2004 to 2023. Key variables include financial inclusion, energy efficiency, government expenditures, GDP, and human capital. Employing dynamic common correlated effects (DCCE) and method of moment\u27s quantile regression (MMQR) through STATA software, the study finds that financial inclusion significantly reduces energy poverty. The variables GDP, human capital, government expenditures, and energy efficiency positively reduce energy poverty in developing nations. Analysis indicates policy measures that should improve rural financial inclusion by using mobile banking networks and microfinance institutions primarily for clean energy spending. Further recommendations include integrating financial literacy with energy initiatives, strengthening governance, and fostering private investment via transparent regulations and public-private partnerships. The study enhances understanding of how financial inclusion reduces energy poverty while helping establish suitable policies for sustainable energy development in impoverished regions