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Source publication
Purpose
The purpose of this paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems.
Design/methodology/approach
This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy lit...
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Citizenship education has been an explicit part of the universal education system in contemporary China. Using data from an original nationwide survey conducted in 2018, we test the hypothesis that the longer the intensity of exposure to citizenship education, the more citizens are influenced by a state-led conception of citizenship characterized b...
Citations
... The rapid rise of Peer-to-Peer markets in the sharing economy provides a new way to consume goods and services (Wirtz et al., 2019). These services used software platforms as an intermediary to facilitate sharing of their resources between private individuals (Allen, 2015). ...
With the rapid growth of the food delivery industry, there is an urgent need to manage software effectively for sharing economy applications. One way to evaluate the effectiveness of these applications is by examining user concerns and feedback. We propose to use a Bi-LSTM-CNN model in a pipeline for automatic classification of the user concerns. The performances of other machine learning and deep learning models were studied and compared. The results showed that the proposed Bi-LSTM-CNN model achieved the highest accuracy score of 84.6%, outperforming the single deep learning models and the traditional machine learning models. Moreover, due to the imbalance nature of the collected data, the impact of data over-sampling technique for data imbalance problem was also evaluated. Interestingly, the interplays between the complex representation induced by the proposed Bi-LSTM-CNN model render the selected oversampling scheme e.g., SMOTE, unnecessary for our setting.
... Duggan et al. (2020) categorized gig work into three main types: (1) capital platform work, such as Airbnb and Etsy; (2) crowd work, such as Amazon Mechanical Turk and Fiverr; and (3) APP work, such as Uber and Lyft. The boom in gig work has been a powerful driver of the global service economy, providing more employment opportunities for workers (Beverungen et al., 2021;Kozinets, 2022;Wirtz et al., 2019). More recently, gig workers have become large, diverse, and unique frontline service providers (Davidson et al., 2023;Gleim et al., 2019). ...
Abstract
Purpose
With the rapid growth of the gig economy worldwide, gig workers’ perceived algorithmic control has been proven to have a crucial impact on the service performance, well-being and mental health of gig workers. However, the literature suggests that gig workers’ perceived algorithmic control may be a double-edged sword. The purpose of this research is to explore how the perceived algorithmic control of gig workers can accelerate thriving at work.
Design/methodology/approach
Based on the model of proactive motivation and work design literature, a three-wave survey was employed, yielding 281 completed responses. The structural equation modeling method was used to test the theoretical hypothesis.
Findings
The results indicate that gig workers’ perceived algorithmic control has positive and indirect effects on thriving at work through the mediating role of job crafting. In addition, job autonomy can moderate the mediated relationship; specifically, when job autonomy is high, this mediated relationship will be stronger.
Practical implications
The health and well-being of gig workers is a concern around the world. The findings provide insights for service platform enterprises and gig workers.
Originality/value
Perceived algorithmic control is critical to mental health and positive work experiences during a gig worker’s service process. However, the current literature focuses more on the negative aspects of algorithmic control. This paper provides a comprehensive research agenda for how to accelerate thriving at work for gig workers.
... Sharing economy (SE) is a diverse and evolving domain that encompasses several models, including collaborative consumption (CC), gig economy, on-demand economy, access economy, and peer-to-peer economy (P2P) (Akhmedova et al., 2020). Business models within the SE vary as a function of: (1) the nature of the assets (capacity constrained vs unconstrained); (2) the nature of the interaction (access vs transfer of ownership), and (3) ownership of the assets (peer-provided vs platform provided) (Akhmedova et al., 2020;Wirtz et al., 2019). According to this classification, car-sharing platforms represent a subtype of SE based on access to the capacity constrained physical resource (car), which can either be platform (B2C model) or peer provided (P2P model). ...
... Second, the sharing economy business model is still evolving (Faraji et al., 2024), and the role of interaction among actors in the extended ecosystem of actors (users, platform, peerproviders, manufacturers, users' friends and family members, nonusers, government, society at large, sustainability) is growing, especially in P2P sharing economy models. Even B2C models often involve significant customer-customer co-creation (e.g., filling the tank and cleaning the car for the next user) (Wirtz et al., 2019). ...
Enabled by digital platforms, shared mobility holds the promise of alleviating the number of pressing social and environmental problems associated with mobility. However, shared models are neither sustainable nor circular by default, creating a need to carefully manage these platforms’ value proposition. This study analyses 17,386 reviews of 16 mobility providers (Zipcar, Turo, Car2Go, Getaround, Sharenow etc.) collected from six different online review containers (Playstore, Appstore, Trustpilot, Yelp!, etc.) over a 7-year period. Structural Topic Modelling (STM), Mean Topic Prevalence (MTP), regression analysis and perceptual mapping were applied. As a result, 14 service-related topics were extracted from the corpus and grouped into six service quality dimensions, identifying the weight of the service dimensions and their evolution over the years. Further analysis examined differences between free-floating and station-based platforms. A key finding was the recent growth in importance of sharing experiences – that is, quality interaction among peers for station-based platforms, by which loyalty can be strengthened through adopting business models that facilitate more cooperative and collaborative forms of exchange. For free-floating models, the role of the (functional) quality of the application was found to be key.
... Chatbots enable banks to provide fast, consistent service by addressing simple inquiries like balance checks and transaction history, and they offer customers 24/7 access (Cameron, 2019). With this technology, companies can create a smoother customer journey and deliver better value, aligned with research that shows digitalization can enhance the effectiveness and efficiency of customer service (Wirtz, So, Mody, Liu, & Chun, 2019). ...
... This data-centric approach supports a shift from reactive to proactive strategies, where companies anticipate customer needs and respond to market demands faster than traditional models allow. For example, Amazon uses real-time data to recommend products, optimize inventory, and tailor customer experiences, demonstrating how data analytics are central to its customer-centric business model (Wirtz et al., 2019). ...
Digital transformation has become a key driver of change across nearly every industry, including marketing. Modern marketing is shifting from the Marketing 3.0 approach, which focuses on building emotional connections and human values, to Marketing 5.0, where technology plays a crucial role in enhancing consumer experience and well-being in digital spaces. This paper examines this transformation, covering aspects such as product digitalization, customer relations, and new technology-based business models. The study also explores the role of human resources and workplace culture in supporting digital transformation, supplemented with real-world examples from global companies. These findings aim to provide insights for academics and practitioners to understand the importance of human-centered technology in modern marketing.
... The boundaries of sharing economies are blurred as a result of the tension between the money-making spirit of certain initiatives and the community-based approach of others (Gerwe and Silva, 2020;Khalek and Chakraborty, 2023). While there have been several definitions and terms for this phenomenon, including the gig economy, the peerto-peer economy, platform capitalism, access-based consumption, collaborative consumption, the collaborative economy, and crowd-based capitalism (Bardhi and Eckhardt, 2012;Benoit et al., 2017;Davis, 2016;Duggan et al., 2020;Eckhardt et al., 2019;Fehrer et al., 2018;Gerwe and Silva, 2020;Howcroft and Bergvall-K� areborn, 2019;Laurell and Sandstr€ om, 2017;Mair and Reischauer, 2017;Sundararajan, 2016;Sutherland and Jarrahi, 2018;Wirtz et al., 2019), this study is based on the conceptualization of the sharing economy proposed by Gerwe and Silva (2020), which is one of the most recent, comprehensive and inclusive thus far. That is, the sharing economy can be regarded as "a socioeconomic system that allows peers to grant temporary access to their underutilized physical and human assets through online platforms" (p. ...
Purpose
Advances in information technology and the increasing digitalization of the general public have favored the growth of the sharing economy. The sharing economy is based on transactions of idle resources between individuals to satisfy cogent needs. Notwithstanding the great interest in this emerging phenomenon, it is still not clear which factors are driving the shift in consumer consumption behavior from the traditional economy toward this new economic model. Grounded in self-determination theory, we contend that what is needed is a holistic approach that considers the three elements involved in sharing economy transactions, namely (1) consumer motivations, (2) web-based platforms and (3) types of assets exchanged.
Design/methodology/approach
To conduct our study, we used the Flash Eurobarometer 467 database titled “The Use of the Collaborative Economy,” collected by the European Union with Flash Eurobarometer datasets and openly available to the public. Consequently, our study aims to provide results based on a large-scale quantitative analysis involving a large number of individuals and multiple sectors.
Findings
Our findings provide empirical evidence of the positive effects of the shift in consumption behavior toward the sharing economy brought about by (1) consumers’ intrinsic motivations, (2) the quality of the platform and (3) the human asset-based categories of products offered.
Originality/value
This research seeks to advance understanding of the factors that facilitate the adoption of the sharing economy, and we provide managers and policymakers with suggestions regarding the factors they may leverage to further favor the spread of this economic model.
... Despite the growing interest in AI for marketing, research on how AI specifically contributes to personalizing customer experiences and increasing engagement remains fragmented. Prior studies have explored AI's role in marketing automation (Wirtz et al., 2019), customer relationship management (CRM) systems (Lemon & Verhoef, 2016), and consumer behavior prediction (Vassileva, 2017), but a comprehensive understanding of its application in creating personalized marketing experiences is still lacking. This research gap highlights the need for a focused investigation into how AI technologies can be harnessed to create more tailored interactions with customers . ...
... In addition, several studies show that AI's potential is not limited to one specific industry. Wirtz, So, and Mody (2019) demonstrate that AI has transformative potential even in peer-to-peer platforms, enhancing customer engagement through automated and personalized services. Chen, Chiang, and Storey (2021) further explore how big data analytics combined with AI can anticipate customer needs, driving personalized offers that boost engagement. ...
... This is a critical area for future research, as consumers today interact with brands through multiple channels, and a disjointed experience can lead to customer frustration and disengagement. Wirtz, So, and Mody (2019) demonstrate how AI can enhance customer engagement in peer-to-peer platforms, yet the same principles need to be applied more broadly across omnichannel environments to ensure consistency in personalized marketing. ...
This study explores the integration of artificial intelligence (AI) in data- driven marketing to personalize customer experiences and enhance engagement. Using a qualitative research approach through literature review and library research, this paper examines various AI-driven marketing strategies and their impact on customer interactions. The analysis reveals that AI technologies, such as machine learning, predictive analytics, and natural language processing, have revolutionized marketing by enabling personalized content, product recommendations, and dynamic customer segmentation. These advancements allow marketers to analyze vast amounts of customer data, identifying behavioral patterns and preferences with precision. The findings also emphasize the role of AI in enhancing customer engagement through automated communication tools, such as chatbots and personalized email campaigns, which increase customer retention and satisfaction. Despite the benefits, the study highlights the challenges associated with AI adoption, including data privacy concerns and the need for specialized skills to manage AI-driven tools effectively. By synthesizing current research, this paper contributes to understanding how AI can be leveraged to create more personalized and engaging customer experiences, ultimately driving business success. The research suggests that organizations must balance innovation with ethical considerations to fully harness the potential of AI in marketing.
... The following six key characteristics can be found by referring to the various SE practices in different industries: the temporary access to and ownership of resources [11], the existence of a digital platform that facilitates the sharing process [12], resource access is provided through an economic interaction [13], the digital platform strengthens the roles of other parties [11], two types of resource that share in SE practices: physical resources (vehicles and accommodation) and digitalised resources, namely, work and money [14,15], two types of ownership of resources: digital platform facilitates the sharing but does not own the resources, and resources are shared by the business that owns them [14,16]. ...
The sharing economy (SE) is a nascent phenomenon representing a socio-economic process to optimise underutilised resources through digital platforms. This process facilitates the shared consumption of resources to maximise resource utilisation while supporting the circularity of resources. However, the successful operation of SE practices is hindered by the lack of identification of effective strategies for enhancing the SE implications, which are essential to comprehending SE practices and developing more sophisticated applications. Therefore, this research aims to provide the first insights into the strategies that enhance SE practices across diverse industries and identify knowledge gaps and future research directions. A systematic literature review (SLR) was conducted by selecting articles published in the 2014–2023 period in Scopus and Web of Science databases. Selected articles were subjected to descriptive and NVivo 14-supported thematic analyses. The descriptive analysis showed that, despite considering articles published in the last 10 years, all relevant articles were published in the last 5 years. Developed and developing countries showed almost equal contributions, while China was recognised as the country with the highest number of publications. Accommodation and transportation sectors were reported as the sectors with the highest number of publications. A cross-analysis was conducted to recognise the varying utilisation of different strategies across diverse industries and sectors. Ten different categories were identified through the thematic analysis that enhance SE practices: economic; environmental; geographic; governance; health, safety, and security; marketing; people; product/services; research, training, education; and technology-related strategies. Each category was discussed along with its relevant strategies, resulting in identifying a total of 84 strategies. These strategies were then presented alongside the responsible parties tasked with their implementation. The study contributes to the SE literature by providing an SLR for contemporary strategies utilised to enhance SE practices, specifically focusing on elucidating the most appropriate categorisation of these strategies. Moreover, this comprehensive SLR provides the first insights into the effective strategies that enhance SE practices across diverse industries.
... It can be argued that the AIDUA model overlooks social-emotional and relational dimensions that significantly affect consumer engagement with chatbots. Stock and Merkle (2018) contend that customers' response to humanized entities extends beyond perceived functionality to include social-emotional elements (cited in Wirtz et al., 2019). This view is supported by Heerink et al. (2010) and van Doorn et al. (2017), who stress additional factors such as perceived humanness and social presence. ...
Purpose
While prior research has examined customer acceptance of humanized chatbots, the mechanisms through which they influence customer value creation remain unclear. This study aims to investigate the emerging concept of Perceived Humanization (PH), examining how hedonic motivation, social influence and anthropomorphism influence value creation through the serial mediation of PH and trust. The moderating roles of rapport and social presence are also explored.
Design/methodology/approach
Based on data from an online survey involving 257 respondents, this study employs Partial Least Squares Structural Equation Modeling utilizing SmartPLS3 software.
Findings
Hedonic motivation leads to value creation via two routes: PH and affective trust; and PH and cognitive trust. Social influence and anthropomorphism also positively impact value creation through similar pathways. Rapport moderates the impact of social influence on PH, while social presence moderates the relationship between PH and both affective and cognitive trust. A cross-cultural analysis of China, India and New Zealand highlights varying cultural dimensions influencing PH and its effects on value creation.
Practical implications
For practitioners in the tourism industry, the findings highlight the strategic importance of enhancing PH in chatbot interactions. By understanding and optimizing these elements, businesses can significantly improve their customer value-creation process.
Originality/value
This study contributes to the service marketing literature by generating a comprehensive framework for the comprehension and application of PH. Its cross-cultural perspective provides rich insights, offering valuable information for service marketers aiming to thrive in the dynamic and competitive tourism industry.
... Research has shown that users and potential users of the sharing economy need to place considerable trust in both the person and the platform with which they are dealing [86]. Embedding trust in a platform and its brand takes time, making it harder for competitors to copy and reducing competitive intensity [211]. Trust toward sharing economy platforms is an antecedent of continuance intention, indicating its pivotal role in sustaining user participation [206]. ...
The sharing economy offers potential economic, social, and environmental benefits, yet participation is not universal. Our research, based on a survey of 873 individuals across three generations in the Czech Republic, investigates the barriers that limit engagement and how these vary across different generations. We examine supply and demand side barriers, identifying four key components on each side (Principal Component Analysis). On the supply side, these include user uncertainty, personal uncertainty, uncertainty from lack of information, and uncertainty from the platform. On the demand side, we identified user uncertainty, personal uncertainty, uncertainty from perceived value, and uncertainty from operational risks. Our findings reveal significant generational differences concerning these barriers. On the supply side, the first three components show statistically significant differences between generations. On the demand side, we found substantial differences for the second and fourth components. The implications of our research suggest that peer-to-peer platforms could benefit from employing generational marketing strategies to address these barriers, thereby increasing their e-commerce volume.
... Platform facilitate interactions among multiple entities, including clients and businesses (Wichmann et al., 2022). They facilitate information exchange, product and service sales, and social interaction (Bonina et al., 2021;Wirtz et al., 2019). Enterprises can generate a favorable emotional response from customers, for whom empowerment is synonymous with liberation and independence, by allowing customers to have a crucial role in selecting products and services (Auh et al., 2019). ...
... Even though CE is a comparatively new concept, it is a well-developed concept in the marketing literature (Lemon & Verhoef, 2016;. In addition, the customer-firm relationship and CE models are further diversified by the emergence of new technologies, including augmented and artificial reality and online social media (Steinhoff et al., 2019;Wirtz et al., 2019). CE on sharing platform is a proxy for their experiences and behavior (e.g., happiness, trust, value-in-use, brand loyalty, word-of-mouth, or price perception (Alfalih, 2022;Winell et al., 2023). ...
This article aims to investigate the effect of customer empowerment on their engagement on sharing platform in the retailing sector via the mediating role of service innovation and customer trust. This study utilized a quantitative design emphasizing mature theory research. The research sample consisted of 457 customers of sharing platform for the retailing sector, using a partial least square-structural equation model (PLS-SEM) for hypothesis testing. The result reveals that customer empowerment positively and significantly affects customer engagement directly and via service innovation as a mediating mechanism on the sharing platform. However, trust in the platform does not mediate this relationship. It is advisable to retailers on sharing platform to create a leading position in this market by enhancing customer engagement and promoting service innovation via customer empowerment. This paper develops a conceptual framework for customer engagement on sharing platform in the sharing economy via service innovation based on giving empowerment to customers.
