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This study explores clothing disposal in a developing economy. It focuses on how consumers dispose of clothing and what motives influence them to use a specific disposal method.

Semi-structured interviews, a qualitative research method, were conducted with a purposive sample of 27 participants from diverse demographic backgrounds within the developing economy of Ethiopia. The interviews were coded and analysed using thematic analysis to identify categories and themes.

The findings reveal various clothing disposal methods, such as bartering, donating, gifting, repurposing and reusing, and discarding. Different motives drive consumers to use these methods, including economic benefits, altruism, and convenience.

The study bridges an important knowledge gap in literature mainly on three aspects, as highlighted by previous research. Theoretically, in addition to proposing a different perspective of bartering as a disposal method, the study investigates the motives behind clothing disposal methods from diverse consumer groups and proposes a conceptual framework to illustrate the link between clothing disposal methods and motives. Methodologically, the study addresses the call for a more inclusive and diverse sample, considering gender and varied socio-economic groups. Contextually, while previous research has focused on developed economies, this study explains clothing disposal methods and motives from a developing economy context, specifically Ethiopia.

This study aims to explore how perceived anthropomorphism, perceived warmth, and customer–artificial intelligence (AI) assisted exchange (CAIX) of service robots affect customers’ satisfaction via digital marketing innovation.

A customer satisfaction model was formulated based on the perspective of parasocial relationships and hybrid intelligence; 236 completed questionnaires were returned by partial least squares structural equation modeling analysis.

This study demonstrates that perceived anthropomorphism, perceived warmth and CAIX's impact on digital marketing innovation were supported, and customer satisfaction impacted the continued intention to use service robots.

Restaurants that leverage service robots differentiate themselves from competitors by offering innovative and technologically advanced dining experiences. Integrating AI capabilities sets these restaurants apart and attracts tech-savvy customers who value convenience and efficiency.

Artificial intelligence (AI) is shaping the future of the marketing world. This study is the first to examine the effect of AI marketing efforts, brand experience (BE) and brand preference (BP) in light of the stimulus-organism-response (SOR) model.

The data collected from 398 participants by the questionnaire method were analyzed by SEM (structural equation modeling) using Smart PLS 4.0 and IBM SPSS 26 programs.

We find that four SOR elements of AI marketing efforts (information, interactivity, accessibility and personalization) positively impact bank customer BE, BP and repurchase intention (RPI). Further, we find that BE plays a mediator role in the relationship between AI marketing efforts, RPI and BP.

The findings of the study have significant implications for the bank marketing literature and the banking industry, given the limited evidence to date regarding AI marketing efforts and bank–customer relationships. Moreover, the study makes important contributions to the AI marketing and brand literature and helps banks increase customer experience with artificial intelligence activities and create long-term relationships with customers.

Delving into the captivating landscape of entrepreneurship education, digital literacy and digital transformation, this study aims to investigate their interplay with entrepreneurial propensity and the moderating effect of school support among Jordanian school students aged 15–17. Anchored in the theory of planned behavior (TPB) and the human capital theory (HCT), this work illuminates the intricate web of influences that shape students’ entrepreneurial mindset.

The study adopts a quantitative approach and uses a “paper-and-pencil" translated questionnaire to collect data from a convenience sample of (n = 650) school students in Jordan. Covariance-based structural equation modeling (CB-SEM) through Statistical Package for the Social Sciences (SPSS) Analysis of Moment Structures (AMOS) v28 was utilized to scrutinize the variables' direct, mediating and moderated mediated impact.

The three structural models revealed that (1) entrepreneurship education has a positive effect on students’ entrepreneurial propensity; (2) digital literacy and digital transformation mediate the interaction between entrepreneurship education and entrepreneurial propensity; (3) school support moderates the mediating effect of digital literacy, and digital transformation, on the link between entrepreneurship education and entrepreneurial propensity.

The results offer actionable enlightenments for educators and policymakers in Jordan to tailor entrepreneurship education, digital literacy and support frameworks, effectively nurturing students' entrepreneurial mindset and aspirations.

This study contributes to understanding the complex dynamics between education, technology and entrepreneurship. Our modest contribution links the findings to a real-world case of two 15-year-old students in Jordan who were inspired by the book “Rich Dad Poor Dad” to start their own business.

This paper aims to (1) unveil how artificial intelligence (AI) can be implemented in growth-hacking strategies; and (2) identify the challenges and enabling factors associated with AI’s implementation in these strategies.

The empirical study is based on two distinct groups of analysis units. Firstly, it involves 11 companies (identified as F1 to F11 in Table 1) that employ growth-hacking principles and use AI to support their decision-making and operations. Secondly, interviews were conducted with four businesses and entrepreneurs providing consultancy services in growth and digital strategies. This approach allowed us to gain a broader view of the phenomenon. Data analysis was performed using the Gioia methodology.

The study firstly uncovers the principal benefits and applications of AI in growth hacking, such as enhanced data analysis and user behaviour insights, sales augmentation, traffic and revenue forecasting, campaign development and optimization, and customer service enhancement through chatbots. Secondly, it reveals the challenges and catalysts in AI-driven growth hacking, highlighting the crucial roles of experimentation, creativity and data collection.

This research represents the inaugural scientific investigation into AI’s role in growth-hacking strategies. It uncovers both the challenges and facilitators of AI implementation in this domain. Practically, it offers detailed insights into the operationalization of AI across various phases and aspects of growth hacking, including product-market fit, user acquisition, virality and retention.