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Yazdan Shirmohammadi, Fatemeh Safa,
Volume 11, Issue 4 (1-2025)
Volume 11, Issue 4 (1-2025)
Abstract
Tourism is recognized as one of the most dynamic and rapidly growing economic sectors in recent decades, acting as a major driver of economic development, employment generation, and cultural exchange worldwide (Cristó & Sharpley, 2019). Within this broader industry, tourism start-ups play a central role in developing innovative products and services, enhancing destination attractiveness, and increasing stakeholder engagement. The performance of such start-ups, especially in urban tourism ecosystems such as Tehran, is increasingly dependent on their ability to leverage Information and Communication Technologies (ICT), foster knowledge integration, and innovate in both products and services. ICT has emerged as a key enabler of competitiveness in knowledge-intensive and service-oriented industries. It facilitates the acquisition and dissemination of knowledge across organizational boundaries, allowing firms to accelerate internal learning, adopt open innovation practices, and improve overall performance (Harif et al., 2022). Moreover, in the context of start-ups, where agility, adaptability, and resource constraints are often interwoven, strategic application of ICT becomes not just an operational necessity, but a performance catalyst.
Methods and Materoal
The present study employed a descriptive-correlational methodology based on structural equation modeling (SEM) using the SmartPLS 3 software. A total of 280 managers and employees from tourism start-ups based in Tehran were selected through convenience sampling. Standardized questionnaires were used to measure the constructs of interest, including ICT (Azam, 2015), open innovation (Hamed et al., 2018), knowledge integration, knowledge management (Iqbal et al., 2023), service innovation (Hu, 2009), marketing strategy (Koksal & Ozgul, 2007), and firm performance. Validity and reliability of the constructs were confirmed through Cronbach's alpha, composite reliability, Average Variance Extracted (AVE) and discriminant validity measures. Items with factor loadings below 0.4 were removed to ensure model parsimony. The GoF (Goodness-of-Fit) index was computed and interpreted based on Kline's (2010) thresholds to ensure robustness of the overall model.
Resultss and Discussion
The results reveal that ICT significantly influences three critical mediating variables: external knowledge integration (β = 0.60, t = 18.0), open innovation (β = 0.75, t = 26.55), and knowledge management (β = 0.512, t = 7.17). These findings support prior studies that conceptualize ICT not only as a data processing tool but also as a vehicle for organizational learning and innovation (Scuotto et al., 2017; Bhatt & Grover, 2005).Moreover, the integration of external knowledge has a direct and significant effect on knowledge management (β = 0.40, t = 8.59), underscoring the importance of external inputs in shaping internal learning systems and innovation capacity (Liao & Marsillac, 2015). In contrast, the direct relationship between open innovation and knowledge management was not statistically significant (t = 0.18), suggesting that open innovation may be more effective when coupled with internal absorptive capabilities or organizational culture conducive to knowledge utilization.Knowledge management, as a central construct in this model, demonstrated strong effects on both service innovation (β = 0.70, t = 24.96) and organizational performance (β = 0.389, t = 3.87). This aligns with the existing literature that highlights the strategic role of knowledge systems in enabling innovation and competitive advantage (Darroch, 2005; Harif et al., 2022). Furthermore, service innovation itself has a modest yet significant impact on performance (β = 0.17, t = 2.66), echoing previous studies that link new service development to firm-level outcomes (Aas & Pedersen, 2010; Cheng & Huizingh, 2014).Surprisingly, the direct effect of marketing strategy on performance was not significant (t = 1.62), which contradicts the results of some earlier studies (Kitsios & Kamariotou, 2016). However, a significant moderating effect of marketing strategy was found on the relationship between service innovation and performance (t = 3.10, β = 0.138), indicating that when strategically aligned with innovation initiatives, marketing strategies can enhance the impact of innovation efforts.The structural model exhibited strong explanatory power, with R² values of 0.658 for knowledge management, 0.494 for service innovation, and 0.429 for performance. The global GoF value of 0.638 exceeded the threshold for strong model fit (Kline, 2010), confirming the robustness of the conceptual framework.
Conclusion
This study offers multiple contributions to both academic theory and managerial practice. First, it empirically validates the critical role of ICT as a driver of performance in tourism start-ups, particularly through its impact on knowledge integration and innovation mechanisms. Second, it emphasizes the importance of effective knowledge management systems as a bridge between external knowledge inputs and internal innovation outcomes. Third, it suggests that while marketing strategy may not directly influence performance, it plays a valuable role as a moderator when combined with service innovation.The implications for practitioners are clear: tourism start-ups should invest in ICT infrastructure and training not merely for operational efficiency but as strategic assets for learning and innovation. Knowledge integration systems, such as customer databases, supplier collaboration platforms, and staff training modules, should be prioritized. In addition, marketing strategies should be designed to amplify the value created through service innovation.Given the limited geographic focus of the study, future research should replicate this model in other cities and cultural contexts. Mixed-method approaches incorporating qualitative insights could also enrich the findings. Moreover, examining the role of individual characteristics such as entrepreneurial orientation, digital literacy, or organizational culture may shed further light on the boundary conditions of these relationships.
Methods and Materoal
The present study employed a descriptive-correlational methodology based on structural equation modeling (SEM) using the SmartPLS 3 software. A total of 280 managers and employees from tourism start-ups based in Tehran were selected through convenience sampling. Standardized questionnaires were used to measure the constructs of interest, including ICT (Azam, 2015), open innovation (Hamed et al., 2018), knowledge integration, knowledge management (Iqbal et al., 2023), service innovation (Hu, 2009), marketing strategy (Koksal & Ozgul, 2007), and firm performance. Validity and reliability of the constructs were confirmed through Cronbach's alpha, composite reliability, Average Variance Extracted (AVE) and discriminant validity measures. Items with factor loadings below 0.4 were removed to ensure model parsimony. The GoF (Goodness-of-Fit) index was computed and interpreted based on Kline's (2010) thresholds to ensure robustness of the overall model.
Resultss and Discussion
The results reveal that ICT significantly influences three critical mediating variables: external knowledge integration (β = 0.60, t = 18.0), open innovation (β = 0.75, t = 26.55), and knowledge management (β = 0.512, t = 7.17). These findings support prior studies that conceptualize ICT not only as a data processing tool but also as a vehicle for organizational learning and innovation (Scuotto et al., 2017; Bhatt & Grover, 2005).Moreover, the integration of external knowledge has a direct and significant effect on knowledge management (β = 0.40, t = 8.59), underscoring the importance of external inputs in shaping internal learning systems and innovation capacity (Liao & Marsillac, 2015). In contrast, the direct relationship between open innovation and knowledge management was not statistically significant (t = 0.18), suggesting that open innovation may be more effective when coupled with internal absorptive capabilities or organizational culture conducive to knowledge utilization.Knowledge management, as a central construct in this model, demonstrated strong effects on both service innovation (β = 0.70, t = 24.96) and organizational performance (β = 0.389, t = 3.87). This aligns with the existing literature that highlights the strategic role of knowledge systems in enabling innovation and competitive advantage (Darroch, 2005; Harif et al., 2022). Furthermore, service innovation itself has a modest yet significant impact on performance (β = 0.17, t = 2.66), echoing previous studies that link new service development to firm-level outcomes (Aas & Pedersen, 2010; Cheng & Huizingh, 2014).Surprisingly, the direct effect of marketing strategy on performance was not significant (t = 1.62), which contradicts the results of some earlier studies (Kitsios & Kamariotou, 2016). However, a significant moderating effect of marketing strategy was found on the relationship between service innovation and performance (t = 3.10, β = 0.138), indicating that when strategically aligned with innovation initiatives, marketing strategies can enhance the impact of innovation efforts.The structural model exhibited strong explanatory power, with R² values of 0.658 for knowledge management, 0.494 for service innovation, and 0.429 for performance. The global GoF value of 0.638 exceeded the threshold for strong model fit (Kline, 2010), confirming the robustness of the conceptual framework.
Conclusion
This study offers multiple contributions to both academic theory and managerial practice. First, it empirically validates the critical role of ICT as a driver of performance in tourism start-ups, particularly through its impact on knowledge integration and innovation mechanisms. Second, it emphasizes the importance of effective knowledge management systems as a bridge between external knowledge inputs and internal innovation outcomes. Third, it suggests that while marketing strategy may not directly influence performance, it plays a valuable role as a moderator when combined with service innovation.The implications for practitioners are clear: tourism start-ups should invest in ICT infrastructure and training not merely for operational efficiency but as strategic assets for learning and innovation. Knowledge integration systems, such as customer databases, supplier collaboration platforms, and staff training modules, should be prioritized. In addition, marketing strategies should be designed to amplify the value created through service innovation.Given the limited geographic focus of the study, future research should replicate this model in other cities and cultural contexts. Mixed-method approaches incorporating qualitative insights could also enrich the findings. Moreover, examining the role of individual characteristics such as entrepreneurial orientation, digital literacy, or organizational culture may shed further light on the boundary conditions of these relationships.
Soheila Shirezhian, Seyed Mehdi Mirmehdi,
Volume 12, Issue 1 (5-2025)
Volume 12, Issue 1 (5-2025)
Abstract
Introduction
In recent years, advancements in technology, particularly in artificial intelligence, have significantly transformed how customers interact with businesses. One of the most prominent manifestations of this transformation is the emergence of chatbots as intelligent digital agents in marketing and customer service. Chatbots are AI-powered programs capable of responding to user inquiries through text or voice interactions, playing a crucial role in enhancing the efficiency of customer-organization communication. These tools enable companies to provide 24/7 services, reduce response times, increase customer loyalty, and save human resources. Unlike human agents, chatbots are unaffected by factors such as fatigue or holidays, ensuring constant availability. However, traditional customer service channels like email, websites, or phone calls remain popular among some customers.
In the retail sector, chatbots facilitate effective customer-brand interactions by offering convenience, flexibility, and easy access. They streamline the online shopping process by providing quick responses and guiding users, creating a seamless and satisfying experience while addressing the impersonal nature of e-commerce. Recent advancements in natural language processing have enabled chatbots to perform complex tasks, such as analyzing customer preferences and delivering personalized responses. These capabilities, combined with the widespread use of messaging platforms, have driven the growth of the chatbot industry. Nevertheless, concerns like data security and privacy pose significant barriers to widespread adoption, requiring careful consideration from system designers. This study, grounded in the Technology Acceptance Model, examines factors such as trust, personal innovativeness, ease of use, social influence, and hedonic motivation to understand the reasons behind users’ acceptance or rejection of chatbots.
Methods and Materoal
This study adopts a quantitative approach with an applied objective, utilizing a descriptive-survey design. The target population consists of Iranian users with experience using AI-based chatbots in online customer service platforms, such as websites, apps, or messaging services. Inclusion criteria required participants to have used at least one service-oriented chatbot and to be familiar with digital tools. Exclusion criteria included incomplete questionnaires, lack of actual chatbot experience, or use of chatbots for non-customer-service purposes (e.g., entertainment or language learning). To enhance accuracy and minimize bias, the influence of the chatbot’s application domain (e.g., retail, banking, education, or healthcare) was analyzed using variance analysis and control of contextual variables.
Data were collected through three primary methods: documentary studies, electronic resources, and field research. The data collection tool was a questionnaire based on a 5-point Likert scale (ranging from “strongly disagree” to “strongly agree”), measuring variables such as trust, hedonic motivation, social influence, personal innovativeness, perceived usefulness, ease of use, attitude, and intention to use. The questionnaire was designed based on standardized scales from prior research, and its content validity was confirmed by experts.
Resultss and Discussion
The findings indicate that trust, personal innovativeness, and ease of use significantly influence the perceived usefulness of chatbots. Trust enhances perceived usefulness by providing accurate and prompt responses. Personal innovativeness strengthens this perception by aligning chatbots with users’ needs, while ease of use, by simplifying interactions, positively affects both perceived usefulness and users’ attitudes. Both perceived usefulness and positive attitudes directly increase the intention to use chatbots. However, social influence and hedonic motivation did not show a significant impact on perceived usefulness, possibly due to customers’ preference for traditional channels or the functional focus of chatbots over entertainment.
Conclusion
This study reveals that trust, personal innovativeness, and ease of use are critical drivers of chatbot adoption. Trust, fostered through reliable and swift responses, enhances the perception of chatbots’ usefulness. Personal innovativeness aligns chatbot functionalities with users’ creative needs, further boosting this perception. Ease of use simplifies interactions, fostering positive attitudes and increasing the intention to use chatbots. The lack of significant impact from social influence may stem from customers’ preference for traditional channels like email or phone calls. Similarly, hedonic motivation’s limited effect could be attributed to the service-oriented nature of chatbots, which prioritizes efficiency over enjoyment.
Chatbots, by automating routine tasks, offering predictive analytics, and enhancing customer experiences, serve as innovative tools in digital services. However, challenges such as data security and privacy concerns remain barriers to broader adoption. Designing user-friendly and trustworthy chatbots can enhance their acceptance and improve the digital customer experience. This study recommends further research on non-users and environmental factors that may hinder the impact of social influence and hedonic motivation to better understand adoption barriers.
In recent years, advancements in technology, particularly in artificial intelligence, have significantly transformed how customers interact with businesses. One of the most prominent manifestations of this transformation is the emergence of chatbots as intelligent digital agents in marketing and customer service. Chatbots are AI-powered programs capable of responding to user inquiries through text or voice interactions, playing a crucial role in enhancing the efficiency of customer-organization communication. These tools enable companies to provide 24/7 services, reduce response times, increase customer loyalty, and save human resources. Unlike human agents, chatbots are unaffected by factors such as fatigue or holidays, ensuring constant availability. However, traditional customer service channels like email, websites, or phone calls remain popular among some customers.
In the retail sector, chatbots facilitate effective customer-brand interactions by offering convenience, flexibility, and easy access. They streamline the online shopping process by providing quick responses and guiding users, creating a seamless and satisfying experience while addressing the impersonal nature of e-commerce. Recent advancements in natural language processing have enabled chatbots to perform complex tasks, such as analyzing customer preferences and delivering personalized responses. These capabilities, combined with the widespread use of messaging platforms, have driven the growth of the chatbot industry. Nevertheless, concerns like data security and privacy pose significant barriers to widespread adoption, requiring careful consideration from system designers. This study, grounded in the Technology Acceptance Model, examines factors such as trust, personal innovativeness, ease of use, social influence, and hedonic motivation to understand the reasons behind users’ acceptance or rejection of chatbots.
Methods and Materoal
This study adopts a quantitative approach with an applied objective, utilizing a descriptive-survey design. The target population consists of Iranian users with experience using AI-based chatbots in online customer service platforms, such as websites, apps, or messaging services. Inclusion criteria required participants to have used at least one service-oriented chatbot and to be familiar with digital tools. Exclusion criteria included incomplete questionnaires, lack of actual chatbot experience, or use of chatbots for non-customer-service purposes (e.g., entertainment or language learning). To enhance accuracy and minimize bias, the influence of the chatbot’s application domain (e.g., retail, banking, education, or healthcare) was analyzed using variance analysis and control of contextual variables.
Data were collected through three primary methods: documentary studies, electronic resources, and field research. The data collection tool was a questionnaire based on a 5-point Likert scale (ranging from “strongly disagree” to “strongly agree”), measuring variables such as trust, hedonic motivation, social influence, personal innovativeness, perceived usefulness, ease of use, attitude, and intention to use. The questionnaire was designed based on standardized scales from prior research, and its content validity was confirmed by experts.
Resultss and Discussion
The findings indicate that trust, personal innovativeness, and ease of use significantly influence the perceived usefulness of chatbots. Trust enhances perceived usefulness by providing accurate and prompt responses. Personal innovativeness strengthens this perception by aligning chatbots with users’ needs, while ease of use, by simplifying interactions, positively affects both perceived usefulness and users’ attitudes. Both perceived usefulness and positive attitudes directly increase the intention to use chatbots. However, social influence and hedonic motivation did not show a significant impact on perceived usefulness, possibly due to customers’ preference for traditional channels or the functional focus of chatbots over entertainment.
Conclusion
This study reveals that trust, personal innovativeness, and ease of use are critical drivers of chatbot adoption. Trust, fostered through reliable and swift responses, enhances the perception of chatbots’ usefulness. Personal innovativeness aligns chatbot functionalities with users’ creative needs, further boosting this perception. Ease of use simplifies interactions, fostering positive attitudes and increasing the intention to use chatbots. The lack of significant impact from social influence may stem from customers’ preference for traditional channels like email or phone calls. Similarly, hedonic motivation’s limited effect could be attributed to the service-oriented nature of chatbots, which prioritizes efficiency over enjoyment.
Chatbots, by automating routine tasks, offering predictive analytics, and enhancing customer experiences, serve as innovative tools in digital services. However, challenges such as data security and privacy concerns remain barriers to broader adoption. Designing user-friendly and trustworthy chatbots can enhance their acceptance and improve the digital customer experience. This study recommends further research on non-users and environmental factors that may hinder the impact of social influence and hedonic motivation to better understand adoption barriers.
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