Pakistan Journal of Commerce and Social Sciences (ISSN 1997-8553)
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Consumer Privacy Concerns and Information Sharing Intention in Omnichannel Retailing: Mediating Role of Online Trust
Full text linkObjective of this research is to find out the relationship between customers\u27 perceptions of an organization\u27s privacy information practices and their information-sharing intention in the context of omnichannel retailing. The study aimed to address the gap in research regarding privacy concerns and information sharing across integrated online and offline channels. Based on the Stimulus-Organism-Response (SOR) framework, research model of this study is proposed. The data collected from 392 omnichannel customers through an online survey and it was analyzed using Partial Least Squares Structural Equation Modeling. The results pointed out that customers\u27 perceptions of privacy practices (collection, unauthorized secondary use, improper access, and errors) positively influence their online trust and information-sharing intention. Online trust partially mediates the relationship between specific privacy concerns and information-sharing intention. Finally, the study concludes that omnichannel retailers need to prioritize transparency, implement robust data protection measures, as well as build trust to encourage customers to share information across channels
Islamic Branding and Brand Resonance: A Multi-Group Analysis of Malaysia & Pakistan
Full text linkThe 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
The Behavioral Intention of Young Travelers to Use Virtual Reality Technology in Cultural Tourism Destinations: An Application of Technology Acceptance Model
Full text linkTechnology plays a crucial role in safeguarding cultural and heritage assets for tourism destinations. Despite youths\u27 apparent technological proficiency, there has been limited research on their intention to use virtual reality (VR) in such settings. This study stands as one of the pioneering efforts to examine young individuals\u27 behavioral intention to utilize VR technology in cultural heritage tourism destinations within the Borneo region, specifically Sarawak. Drawing from the concept of the technology acceptance model, this study investigates how various factors of perceived usefulness (such as accessibility to information, information quality, and media richness) and perceived ease of use (such as interactivity) influence the behavioral intention to use VR technology in cultural tourism settings. Statistical Package for Social Sciences (SPSS) and WarpPLS were used for data analysis. This study gathered data from 250 valid responses from young visitors at cultural tourism sites in Sarawak, Malaysia. Employing a quantitative methodology, the interrelationships among the study variables were examined through partial least squares - structural equation modelling. The current research reveals that young individuals prioritize factors such as information quality, media richness, and interactivity when considering their intention to use VR technology in cultural tourism destinations. However, the accessibility of information was not found to be a significant concern. This study lies in its focus on the Borneo region, offering new insights into the adoption of VR technology in cultural heritage tourism among youths
A Dual-Learning Pathway: How Digital Orientation and Financial Literacy Shape Digital Transformation in Chinese Agriculture Enterprises
Full text linkThis study mainly explores the direct effects of financial literacy, acquisitive learning, and experiential learning on digital transformation, while also analyzing the direct influence of digital orientation on both acquisitive and experiential learning. At the same time, this study also explores the mediating effects of acquisitive learning and experimental learning between digital orientation and digital transformation. This research is grounded in dynamic capability theory, focuses on Chinese agricultural firms. Data were collected through a structured questionnaire from 279 managers of Agricultural industrialization leading enterprises in China, and analyzed using PLS-SEM. The research findings indicate that financial literacy, acquisitive learning, and experiential learning all significantly enhance digital transformation, with acquisitive learning serving as a more crucial mediating factor in the relationship between digital orientation and digital transformation. Furthermore, the significance of digital orientation in promoting acquisition learning and experimental learning has also been confirmed. The research in this article provides theoretical basis and practical guidance for how agricultural enterprises can enhance their digital capabilities by improving financial literacy and learning mechanisms in the process of digital transformation
Environmental Sustainability in Technologically Advanced Economies: The Role of Eco-Digitalization, Green Finance, and Green Technology
Full text linkThis 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
Influence of Brand Personality Congruence, Brand Attachment, Brand Love and Obsessive Passion on Compulsive Buying Behavior
Full text linkThe importance of brand personality congruence (BPC), brand attachment (BA) and brand love (BL) in the luxury marketing is paramount. Still, their relationships in enhancing the customers obsessive passion (OP) and compulsive buying behavior (CBB) through the moderating role of materialism remains tenuous in luxury branding. Addressing these gaps, this study aims to empirically investigate the BPC framework and its impact on customers CBB in the luxury branding of Pakistan by using self-congruity theory. In a cross-sectional study design, survey-based data is collected from 377 consumers of luxury brands, visiting retail stores. For data analysis, PLS-SEM technique is employed by using SmartPLS 4.
The findings reveal positive relationship between brand personality congruence and customers\u27 positive feelings, including brand love and brand attachment which subsequently transforms into compulsive purchase behavior. Additionally, materialism moderates, thus strengthens the relationship between the customers OP and CBB in luxury branding. This study is significant since it has examined the relationships from self-congruity theory which is giving a new lens of understanding how the brand personality congruence can lead to unexplored and hidden but important aspects such as feelings, love and attachment which subsequently enhances the compulsive buying behavior. The study will help practitioners and marketers in developing advertising and promotional strategies that target consumer self-identity and align with the brand\u27s personality to encourage compulsive purchasing
Unsupervised Machine Learning Based Anomaly Detection in High Frequency Data: Evidence from Cryptocurrency Market
Full text linkThe 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
The Impact of Netflix’s AI Powered Recommendation System on Consumers’ Behavioral Intentions
Full text linkCurrent studies primarily focus on the recommender systems (RSs) through the algorithmic accuracy, and which insufficient to fully measure accurately the practical effectiveness of the RSs and understand user experience. There is an urge examine the dynamic interactions on the critical user context of "beyond accuracy" features such as diversity, novelty, and serendipity to understand RS’s value creation mechanism holistically. The aim of the study is to explore the multidimensional effects of key features (Stimuli) of artificial intelligence (AI)-RSs on consumer behavior (Response) based on the Stimulus-Organism-Response (S-O-R) theoretical framework which is a gap in the literature. The study was modeled four key features of RSs (accuracy, diversity, novelty, serendipity) as stimuli, user engagement, satisfaction, and perceived risk as organism (mediating constructs), and behavioral intentions as the response. Data was collected from 437 participants, and analyses were conducted using the covariance-based structural equation modeling (CB-SEM) by AMOS 24.0 and SPSS 25.0 statistical programs. The findings explains that the empirical assumptions of the S-O-R model were met to a high degree, successfully explaining 61% of the variance in behavioral intentions. Specifically, satisfaction and engagement have strongest positive effects on behavioral intentions. The findings suggest that the effectiveness of AI-RSs should no longer be evaluated solely through algorithmic accuracy, but rather through the dynamics of user-centered value creation (satisfaction, engagement, and risk management). Furthermore, serendipity have the strongest direct effect on engagement, highlighting the importance of “beyond-accuracy objectives” that trigger curiosity. Accuracy and diversity significantly and negatively reduce risk, demonstrating a critical role in increasing system trustworthiness. Contrary to the accuracy-diversity trade-off problem, a strong positive relationship exists between accuracy and diversity, suggesting that these multiple goals reinforce each other in the context of user perception. These results provide important theoretical and practical consequences for the design of multi-objective recommender systems (MORS)
Technology Readiness and Technology Acceptance in Virtual Reality Tourism: An Integration of TOE and TAM Frameworks
Full text linkVirtual reality (VR) tourism presents a creative way to improve travel experiences and offers smart travel. On the other hand, compared to wealthy nations with sophisticated information infrastructure and smart tourism support, the exploitation of VR tourism in emerging tourism economies looks to be limited. Focusing on Ho Chi Minh City, Vietnam, a case study for growing tourism businesses, this paper attempts to establish a model identifying elements involving VR tourism acceptance in developing markets. Expanding the Technology Acceptance Model (TAM) with the Technology, Organization, and Environment (TOE) framework helps this model to emphasize elements like organizational readiness and knowledge of VR tourism in developing countries. Using partial least squares structural equation modeling (PLS-SEM) on data set of 260 tourism companies, the results show that perceived ease of use is less important, perceived usefulness of VR tourism has the largest effect on adoption intentions. Policy variables have little effect; critical elements are technological developments and organizational ability, compatibility. Therefore, in growing tourism industries, innovation, usefulness and availability of VR tourism play the most significant role. These findings suggest theoretical and practical implications on VR tourism adoption in developing markets
Behavioral Intention to Adopt FinTech Services: A Comparative Study Between Digital Immigrants and Digital Natives in Pakistan
Full text linkThis study aims to investigate the key factors influencing the adoption of financial technology among digital immigrants and natives by extending the diffusion of innovation theory. A convenience sampling technique was employed to collect data from 160 digital immigrants and 193 digital natives, resulting in a total of 353 responses. A structural equation modelling approach was employed to conduct a multigroup analysis comparing the responses of digital immigrants and digital natives using SmartPLS software. This study reveals significant relationships between relative advantage, complexity, compatibility, trust, data security, and intention to adopt FinTech services. Moreover, this study emphasizes the importance of addressing data security concerns and ensuring compatibility with existing financial systems to foster trust and facilitate the adoption of FinTech. Notably, the present research holds important implications for stakeholders, including academia, the financial industry, and policymakers, informing modern strategies to promote financial inclusion by targeting all age groups of users in this digital financial landscape