Abstract: The rapid advancement in Artificial Intelligence (AI) has dramatically impacted the field of marketing, by allowing companies to interact with their customers through automation, to optimize marketing campaigns, and to make better decisions in marketing; yet, there are still many challenges that small businesses face when considering AI-based marketing solutions due to limited resources, capability gaps, and uncertainty about the benefit of using AI in marketing. This review is a synthesis of recent academic research on the adoption of AI-based marketing tools among small businesses in order to identify the key barriers and enablers of adoption. Using existing models of technology acceptance and use, such as the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Resource-Based View (RBV), this review found that the adoption of AI-based marketing tools is influenced primarily by how useful a company perceives the tool, whether or not the costs of the tool outweigh the benefits, whether or not a company has the necessary knowledge to implement and use the tool, and whether or not a company trusts the tool to perform as promised. Additionally, incorporating industry-specific information highlighted the importance of third party service providers and developing capabilities to help support the adoption of AI-based marketing tools. This review also presents an integrative conceptual model of the relationships between the technological aspects of AI-based marketing tools, the organizational aspects of a company, the environmental aspects of a company, and the human aspects of a company, and provides a comprehensive way of viewing the adoption of AI-based marketing tools that can be used by both researchers and practitioners.

Abstract: This study empirically investigated the relationship between digital advertising strategies and the marketing performance of deposit money banks in Rivers State. The study adopted the quantitative research methodology and quasi-experimental research design method through cross-sectional survey design. The population comprised nineteen (19) listed deposit money banks operating in Rivers State. One hundred and twenty-seven (127) questionnaires were distributed; one hundred and nineteen (119) were fit for analysis. Face and content validity and Cronbach's Alpha were used to measure the validity and reliability. All were fit for the analysis. The Pearson Product-Moment Correlation and Partial Correlation tools were adopted with the aid of Statistical Package for Social Sciences (SPSS version 23.0). The findings of the study showed that social media advertising was significantly related with the marketing performance of deposit money banks in Rivers State; e-mail advertising significantly related with marketing performance of deposit money banks in Rivers State; pop-up advertising significantly related with marketing performance of deposit money banks in Rivers State and mobile-based advertising significantly related with marketing performance of deposit money banks in Rivers State. Based on the findings, the study concluded that digital advertising strategies (social media advertising, e-mail advertising, pop-up advertising, and mobile-based advertising) have a positive and significant impact on marketing performance (brand awareness and customer engagement) of deposit money banks in Rivers State. The study thus recommended that deposit money banks should ensure that their E-trade channels command trust in such a way that customers can effectively transact business through such channels without being defrauded. It must be proven at all instances by these deposit money banks that their social media channels are reliable; this must be guaranteed by providing customers with 24-hour information about their offerings.

Abstract: Purpose This study develops a conceptual model explaining how home-service businesses (e.g., plumbing, HVAC) adopt AI-driven lead-generation automation. While new tools abound, adoption among small firms remains low. This study uses behavioral, technological, and organizational perspectives to describe both adoption and performance outcomes.Design/Methodology/Approach This conceptual paper combines technology adoption theories (TAM, UTAUT, TOE, DOI) with industry findings. It proposes a framework linking owner attitudes, risk, system quality, vendor support, and resource constraints to performance outcomes like improved lead quality and operational efficiency.Findings The model suggests adoption depends on owner digital self-efficacy, trust, utility, complexity, workflow integration, and vendor support. Successful implementation enables benefits including faster response times, higher booking rates, and improved marketing ROI.Originality/Value This study addresses a significant gap by developing a model specifically for AI lead generation in micro-home-service businesses—an ignored segment. It incorporates behavioral, technological, and performance factors to explain how small service businesses successfully adopt AI.Practical Implications Practitioners and vendors can use this model to identify adoption barriers and design user-friendly AI systems that meet the specific needs of small service businesses.Social Implications Increased AI adoption by small businesses contributes to stronger local economies, job security for the self-employed, and better access to digital markets.

Abstract: Understanding how moral and emotional language operates in paid social advertising is essential for evaluating persuasion and its ethical contours. We provide a descriptive map of Moral Foundations Theory (MFT) language in Meta ad copy (Facebook/Instagram) drawn from seven global beverage brands across eight English-speaking markets. Using the moralstrength toolkit, we implement a two-channel pipeline that combines an unsupervised semantic estimator (SIMON) with supervised classifiers, enforce a strict cross-channel consensus rule, and add a non-overriding purity diagnostic to reduce attribute-based false positives. The corpus comprises 758 text units, of which only 25 ads (3.3%) exhibit strong consensus, indicating that much of the copy is either non-moral or linguistically ambiguous. Within this high-consensus subset, the distribution of moral cues varies systematically by brand and category, with loyalty, fairness, and purity emerging as the most prominent frames. A valence pass (VADER) shows that moralized copy tends toward a negative valence that may still yield a constructive overall tone when advertisers follow a crisis–resolution structure in which high-intensity moral cues set the stakes while surrounding copy positions the brand as the solution. We caution that text-only models undercapture multimodal signaling and that platform policies and algorithmic recombination shape which moral cues appear in copy. Overall, the study demonstrates both the promise and the limits of current text-based MFT estimators for advertising: they support transparent, reproducible mapping of moral rhetoric, but future progress requires multimodal, domain-sensitive pipelines, policy-aware sampling, and (where available) impression/spend weighting to contextualize descriptive labels.

Abstract: Within the framework of brand communication, several companies choose to use bistable logos. These types of logos fall within the mechanisms inherent to bistable perception, where the interpretation of the two possible percepts involved may depend on the areas being observed or on prior instructions given to the observer to search for a particular shape within the ambiguous image. Perceptual factors related to the stimulus and the areas of eye fixation are called bottom-up aspects. The information exogenous to the bistable stimulus that determines perception is called top-down modulation. In order to determine whether certain bottom-up perceptual modulation areas for the Toblerone bistable logo are related to the search for each percept previously modulated by a written instruction, an experimental task was carried out with 34 participants using a Tobii T-120 eye-tracker device. Seven bottom-up modulation clusters were analyzed for ocular responses manifested in two different top-down modulation conditions. The results show that for each of the percepts, some areas correspond to the textual information offered as a top-down modulator. It is concluded that for the perception of the Toblerone® logo, some areas are related to each percept and that unimodal top-down modulation mechanisms operate in certain areas, while others can be assumed to be parts of the logo that contribute to the recognition of the two percepts involved.

Abstract: The growing integration of immersive technologies in the tourism sector raises questions about their real impact on the visitor experience. This study investigates whether mixed reality effectively influences the tourism experience, seeking to understand the mechanisms through which tourism content and emerging technologies shape tourists' perceptions. A quantitative approach was adopted through the application of a questionnaire. The data were analyzed using the PLS-SEM (Partial Least Squares Structural Equation Modeling) method, allowing us to test direct and indirect relationships between the constructs Tourism Content, Adoption of Mixed Reality, and Tourist Experience. The analysis revealed positive and statistically significant direct effects. Tourism content strongly influences the adoption of mixed reality (β=0.725; p<0.001). Moderate impacts of the adoption of mixed reality (β=0.375; p<0.001) and tourism content (β=0.392; p=0.001) on the tourist experience were found. The indirect effect mediated by the adoption of mixed reality proved to be significant (β=0.272; p=0.001), with an VAF (Variance Accounted For) of 41%. Mixed reality plays a complementary partial mediating role in the relationship between tourism content and visitor experience, confirming its relevance in the contemporary tourism experience.

Abstract: This study investigates how cause-related marketing (CRM) contributes to sustainable relationship management in the food and beverage (F&B) industry by integrating perceived CRM, psychological distance, relationship quality, and customer involvement. It explores how socially responsible initiatives enhance customer trust and loyalty while addressing evolving consumer expectations for ethical and sustainable practices. Based on 400 valid responses from chain and independent restaurants in Taipei, structural equation modeling was used to test the proposed framework. The results indicate that CRM significantly improves relationship quality, with customer involvement amplifying this relationship. Moreover, shorter psychological distance further strengthens the effects of CRM on relationship outcomes. The findings highlight CRM as an effective strategic tool for promoting sustainable customer engagement, reinforcing brand authenticity, and achieving long-term competitive advantage in the hospitality and tourism sectors.

Abstract: Museums contribute significantly to the cultural and economic development of destinations, yet university museums remain underexplored, especially in developing economies. This study examines how Experimenter Attractiveness (EA), Attitude toward the Museum (ATM), and Self-Concept Connection (SCC) influence Visit Experience (VE) and Loyalty by Location (LL). Using data from 1,400 visitors to university museums in Puebla, Mexico, Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to test the proposed relationships. All hypotheses were supported, with the strongest effects observed from EA to SCC and from SCC to VE, indicating that attractive and engaging experiences strengthen visitors’ self-connection and lead to greater satis-faction during the overall museum visit. The validated model demonstrates consistent relationships among constructs and contributes to understanding how attractiveness and self-connection shape behavioral intentions in the university museum context. These findings highlight the importance of experiential marketing and emotional engagement strategies, while future research should explore additional attitudinal and technological factors that enhance visitor loyalty.

Abstract: Fertile women represent a socially and medically significant patient group, yet little research has examined their rehospitalization behavior and financial burden in clinical settings. This study develops predictive and explainable artificial intelligence (AI) to forecast retention and medical costs among reproductive-age orthopedic patients. Electronic health records of 83 women (aged 15–49) at a major university hospital in Korea were analyzed. Six machine learning models were tested, and model performance was assessed using accuracy and the area under the curve (AUC). Shapley Additive Explanations (SHAP) were applied to interpret predictors of rehospitalization. Additional analyses explored determinants of patients’ total and uncovered medical costs. Random forest outperformed other models in predicting rehospitalization (AUC 0.92 vs. 0.73 for logistic regression). Key predictors included major disease, systolic blood pressure, platelet count, age, and treatment costs. Random forest also yielded lower error rates than linear regression in forecasting patients’ financial burden (RMSE/IQR for total cost: 1.05 vs. 1.14). Several factors—such as blood pressure, pulse, and hematocrit—were influential for both retention and costs. Predictive and explainable AI can support medical centers in anticipating rehospitalization and financial barriers for fertile women. By integrating medical and socioeconomic determinants, hospitals may design strategies that enhance patient retention while addressing broader societal priorities in women’s health.

Abstract: Electric motor vehicle adoption in Indonesia is proliferating. This study integrates norm activation theory and goal-directed behavior to explain electric motor vehicle choice. The study aims to analyze the implementation of norm activation theory and goal-directed behavior in understanding consumers choice of an electric motor vehicle. This research employs a descriptive approach to analyzing the research model. A questionnaire was distributed to 340 respondents who have driven motor vehicles, ensuring diverse perspectives, and it was analyzed using a structural model analysis. The results of this study demonstrated that goal-directed behavior and norm activation theory could effectively explain consumer decision-making. The findings showed that norma activation and goal-directed behavior theories could explain consumer choice. However, subjective norms and perceived behavioral control did not influence the desire for consumer choice.

Abstract: The transition to sustainable food systems requires communication strategies that resonate with consumers’ values, not only technological innovation. This study examines how values-centric communication shapes German consumers’ responses to alternative proteins, focusing on insect-based snacks. A desk-based synthesis of 28 studies, guided by Schwartz’s Theory of Basic Human Values, identified Tradition and Security as dominant drivers of food choice and yielded five communication requirements: cultural familiarity, emotional safety, simplicity and clarity, trust and credibility, and routine integration. These were translated into communication guidelines and short on-pack claims, which were applied to a refined packaging prototype. An exploratory focus group (N = 7) then compared reactions to the original versus the refined packaging, analysed using McGuire’s Communication–Persuasion Matrix. Findings indicate that familiar formats, reassuring tone, clear visual hierarchy, and salient trust cues promoted movement from immediate aversion toward consideration, whereas information overload, claim–image incongruence, value-incongruent brand naming, and delayed recognition of insect content hindered acceptance. The study contributes an integrative analytic lens combining Schwartz’s value theory with McGuire’s model and provides a set of testable guidelines for value-aligned food communication. Given the exploratory design (small student sample, fixed presentation order, bundled manipulations, and hypothetical intentions), results should be interpreted cautiously; future research should employ counterbalanced factorial designs with behavioural outcomes.

Abstract: Despite the increasing number of social media users and the advantages linked to agility in other areas, the implementation of agility within a social media framework remains unexamined. A quantitative method was utilized. Data was collected from 420 Social Networking Site users in Turkey, utilizing the convenience sampling method. SmartPLS was used for data analysis. The results show that perceived social media agility has a positive impact on customer-based brand equity, customer engagement, and customer motivation. Customer engagement and customer motivation were found to impact customer-based brand equity significantly. Furthermore, customer motivation has no significant impact on customer engagement. Change-seeking has a positive influence on customer engagement and customer motivation. Customer engagement and customer motivation were found to significantly mediate the link between perceived social media agility and customer-based brand equity. The study provides an exhaustive framework that incorporates social media agility, customer motivation, and engagement, thereby enhancing theoretical models of brand equity and customer behaviour. Managers must prioritize the development of flexible social media strategies that can swiftly adapt to changes and trends, cultivating a dynamic and participatory online presence. This study advances digital marketing literature by integrating the investigated constructs into a unified conceptual framework.

Abstract: In response to the increasing global emphasis on sustainability, electric vehicles (EVs) have emerged as a promising alternative vehicles. Grounded in the Value-Attitude-Behviour (VAB) model and the Theory of Planned Behaviour (TPB), this study investigates Malaysian consumers’ intention to choose EVs as their preferred mode of transportation. Specifically, the study explores the relationships between price, maintenance cost, infrastructure readiness, consumer attitudes, and purchase intention. Moreover, it examines the mediating role of consumer attitude in the relationships between price, maintenance cost, and infrastructure readiness with the intention to purchase EVs. Data were collected from 252 respondents in Malaysia, using a proportionate stratified sampling method. Out of the seven hypotheses tested, six were supported. The findings reveal that maintenance cost, infrastructure readiness, and attitude have significant positive relationships with consumers’ intention to purchase EVs. The results further indicate that consumer attitude mediates the relationship between price, maintenance cost, and infrastructure readiness with the intention to purchase EVs. Theoretically, this study contributes to the existing body of knowledge by developing a framework that integrates value-based antecedents with attitudinal and behavioral outcomes. Practically, the findings provide valuable insights for marketers and policymakers to formulate effective strategies and policies that can accelerate EV adoption.

Abstract: Product launches typically consist of two stages: an introductory stage and a maturity stage, with the latter characterized by higher market demand. This prompts retailers to increase prices during the maturity stage in order to maximize profits. However, exceeding the fair price threshold for consumers can trigger a sense of unfairness, leading to purchase aversion and a decrease in mature demand. At this time, if retailers consider consumer fairness concerns in advance and adopt the price decrease strategy, consumers will take the first-stage price as a reference point, which will instead have a psychologically positive effect and increase demand. In order to assess the impact of these two strategies on supply chain profits, we have developed models for price increase and decrease strategies that take into consideration consumer fairness concerns. Subsequently, we compare the variations in profit among manufacturers, retailers, and the supply chain in centralized and decentralized scenarios. The research findings suggest that in centralized decision-making, retailer faces a threshold in choosing between implementing the price increase or decrease strategy. Conversely, in decentralized decision-making, retailer consistently adopts the price increase strategy. Furthermore, in centralized decision-making, consumer fairness concerns lead to reduced overall profit for the supply chain. However, it is paradoxical that under certain parameter constraints of decentralized decision-making, such consumers’ presence can enhance the supply chain’s overall profit.

Abstract: The growth of peer-to-peer accommodation platforms has transformed the tourism and hospitality industry by introducing decentralized, host-driven pricing systems. However, many Airbnb hosts rely on intuition or limited platform recommendations to set nightly rates, often resulting in inconsistent pricing strategies. This study develops and evaluates a machine-learning model for predicting Airbnb prices using publicly available data from Inside Airbnb for Seattle, Washington. The analysis integrates listing, review, and calendar data to identify key determinants of nightly rates. Following extensive data cleaning and feature engineering, three predictive models were tested: Linear Regression, Ridge Regression, and Random Forest Regression. The Random Forest model achieved the best performance, with an R² of 0.726 and a mean absolute error (MAE) of approximately $51 per night. Cross-validation and multi-seed testing confirmed model stability and reproducibility. Feature-importance analysis revealed that property capacity and amenity richness were the strongest predictors of price, while neighborhood tier and host activity contributed moderately. These findings reinforce hedonic pricing theory by demonstrating that tangible property characteristics explain most pricing variation in peer-to-peer rentals. The study contributes a reproducible and interpretable framework for short-term rental analytics, offering practical guidance for hosts, policymakers, and researchers seeking to understand data-driven pricing in the sharing economy.

Abstract: This study investigates how Moroccan users experience and interpret digital content that seems tailored to their personal profiles. While many participants recognize the relevance of such content, their willingness to engage depends less on accuracy and more on whether they feel respected and in control. Based on 629 survey responses and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings indicate that perceived control is the most influential factor in building trust, which in turn strongly predicts engagement. Conversely, when content feels intrusive or when users have concerns about how their data is managed, trust declines—even if the targeting appears accurate. These results imply that people do not simply react to what they receive but also to the manner in which it is delivered and explained. In a rapidly digitizing environment like Morocco, where awareness of data rights remains limited, trust and transparency emerge as essential foundations for meaningful digital interaction. The study provides practical insights for marketers and platforms aiming to design targeting strategies that are not only effective but also ethically responsible and aligned with users’ expectations.

Abstract: Data-driven marketing analytics has advanced targeting and optimization, yet its underlying infrastructure now functions as a complex sociotechnical system with overlooked ecological costs. This study conceptualizes programmatic advertising through a systems lens. It introduces the Ecological Delivery Paradox, a structural incongruity where environmentally friendly advertising messages are transmitted via energy-intensive delivery pipelines. Using an interpretivist–abductive design, we conducted 38 in-depth interviews with consumers and professionals, which were analyzed using reflexive thematic analysis in MAXQDA. Results show that awareness of hidden delivery costs emerges through a concretization threshold and crystallizes into metaphors such as “clean message, dirty conduit,” which trigger differentiated cognitive–affective pathways. These pathways shape trust trajectories across four profiles: cliff erosion, slow seep, suspended risk, and resilient cores. System-level moderators, including rationalization buffers, efficiency beliefs, and the visibility of low-data alternatives, determine outcomes. The findings extend marketing systems theory by reframing greenwashing as message–infrastructure misalignment and by integrating delivery congruence into advertising trust models. We propose a data-driven control architecture that aligns predictive analytics with ecological proportionality through mechanisms such as lightweight creatives, carbon-aware bidding coefficients, frequency–data quotas, and ad-level transparency labels. This systemic approach advances legitimacy, audience trust, and sustainability as joint objectives in programmatic advertising.

Abstract: Determination of the right price is vital for the success of newly developed food products. This study examined the market prices and their determinants for five ready-to-cook catfish products: Panko Breaded Standard Strips (PBSS), Panko Breaded Standard Fillet (PBSF), Panko Breaded Delacata Fillet (PBDF), Sriracha Marinated Delacata Fillet (SMDF), and Sesame-Ginger Marinated Delacata Fillet (SGMDF). Market prices were derived using Vickrey's second-price auction, where the second-highest bid represents the market price. We analyzed experimental auction data from 121 consumers with a logit model to estimate the probability of offering the market price based on product sensory attributes, socio-demographic characteristics of the participants, and competition level (panel size). Consumers' willingness-to-pay (WTP) was elicited in two rounds: before tasting (visual evaluation) and after tasting (organoleptic evaluation) the products. Breaded products received higher market prices than marinated products, with PBDF ranked highest. Sensory traits, especially taste, along with income, education, and grocery shopping involvement, significantly influenced the market price formation. Increased competition elevated the market prices. Both product features and consumer characteristics strongly affect market price outcomes, and experimental auctions provide a robust tool for understanding consumer behavior toward newly developed food products.

Abstract: This paper investigates how multi-vendor loyalty programs influence customer behavior, specifically within the context of an airline's program and its various partners. It examines cross-buying activities between flying (the core service) and partners across different categories, such as other airlines, car hire, hotels, and less complementary services like credit cards and fuel vendors. Utilizing a generalized additive model (GAM) analysis on data from the Voyager loyalty program, the research tests hypotheses related to the impact of partner category fit, bonus awards, and membership duration on core service usage and cross-buying. The findings reveal complex and varied relationships between partner categories and core service engagement, highlighting that less complementary partners exhibit a strong positive and nearly linear relationship with core service usage. The study also explores the effect of bonus miles and membership duration on customer loyalty and purchasing patterns across the different partner types.

Abstract: This study investigates the perception-to-purchase journey by examining how consumer artificial intelligence (AI) literacy influences the effectiveness of AI-generated sponsored vlogs, an emerging digital marketing format. Using survey data from 413 consumers and structural equation modeling, we develop and test the AI Literacy Perception-Decision Model (ALPDM). Results show that AI literacy affects information adoption through three pathways: emotional value, information usefulness, and source credibility. Multi-group analysis further indicates that AI literacy directly influences purchase intention for experiential services but requires full mediation through information adoption for tangible products. The ALPDM framework advances marketing theory by establishing the complete chain—AI literacy → perceived value → information adoption → purchase intention—demonstrating how technological competence evolves from a cost barrier into a cognitive resource that shifts source credibility evaluation from peripheral to central processing. For practitioners, the findings suggest differentiated strategies: Marketers of experiential services should emphasize anthropomorphic elements, whereas marketers of tangible products should prioritize technological transparency to foster consumer trust.