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Article

Analysis of Factors Influencing Digital Transformation of Tourism Villages: Evidence from Bogor, Indonesia

by
Isbandriyati Mutmainah
1,
Iis Anisa Yulia
1,
Foni Agus Setiawan
2,
Aditya Sugih Setiawan
3,
Immas Nurhayati
4,
Bambang Hengky Rainanto
5,
Sri Harini
6 and
Endri Endri
7,*
1
Department of Management, Universitas Nusa Bangsa, Bogor 16166, Indonesia
2
Department of Informatics Engineering, Universitas Ibn Khaldun, Bogor 16162, Indonesia
3
Department of Tourism, Sekolah Tinggi Pariwisata, Bogor 16113, Indonesia
4
Department of Management, Universitas Ibn Khaldun, Bogor 16162, Indonesia
5
Department of Tourism, Institut Bisnis dan Informatika Kesatuan, Bogor 16123, Indonesia
6
Departement of Management, Universitas Djuanda, Bogor 16720, Indonesia
7
Faculty of Economics and Business, Universitas Mercu Buana, Jakarta 11650, Indonesia
*
Author to whom correspondence should be addressed.
Tour. Hosp. 2025, 6(2), 57; https://doi.org/10.3390/tourhosp6020057
Submission received: 19 January 2025 / Revised: 25 March 2025 / Accepted: 26 March 2025 / Published: 28 March 2025
(This article belongs to the Special Issue Data-Driven Insights in Tourism and Hospitality: Smart Technologies and Data Science)
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Versions Notes

Abstract

:
This study examines the main determinants influencing the commitment of tourism village managers and business stakeholders to implement digital transformation. It will test the impact of perceived benefits, attitudes towards change, consumer behavior change, and the technological context on the intentions and commitments of tourism village managers and enterprises in Bogor Regency, Indonesia. The Causal Step multiple linear regression analysis examined 146 respondents selected through saturated sampling. The findings indicated that attitudes towards change, consumer behavior change, and the technological context significantly influenced the commitment to implement a digital transformation, mediated by the intention to implement digital transformation. The intention to implement digital transformation became a perfect part of the technological context of the commitment to implement digital transformation. It became a partial mediator of the influence of digital attitudes towards change and consumer behavior change on the commitment to implement transformation. Perceived benefits only directly affected the commitment to implement digital transformation. This research has at least two novelties, conceptual and contextual novelties. Conceptual novelty is studied in digital transformation, focusing on tourism villages. The contextual novelty is that the findings offer a more thorough understanding of the conditions and stages of technological transformation embraced by stakeholders and managers of tourism villages.

1. Introduction

The tourism sector’s competitiveness has markedly intensified in the 21st century (Ruiz-Lacaci et al., 2024), with the digital revolution as a pivotal element in its success (Saura & Bennett, 2019). The tourism sector in Indonesia possesses significant economic potential, contingent upon effective management. According to the data from the Ministry of Tourism and Creative Economy of the Republic of Indonesia, foreign exchange earnings from tourism amounted to 10.46 billion USD by September 2023, with tourism contributing 3.8% to GDP. Domestic tourist movements totaled 688.78 million, while foreign tourist visits reached 9.5 million. Nevertheless, not all tourist destinations have fostered economic growth; thus, it is imperative to establish a sustainable tourism development model to ensure that the tourism sector’s contributions are not limited to specific destinations. Sustainable tourism aims to develop tourist attractions while preserving natural, social, economic, and cultural resources for both present and future generations (De Bruyn et al., 2023).
One tourist destination that has the potential to develop is tourist villages, whereby in 2024, there will be more than 4000 tourist villages spread throughout Indonesia. Tourism villages are products produced from the inherent potential of the village, encompassing natural resources, societal attributes, and cultural elements, collectively serving as an identity with tourist appeal (Rina & Siswati, 2023). Tourism villages are typically developed in rural areas that retain distinctive characteristics, including the village’s uniqueness, pristine natural resources, traditions, and culture, which collectively define the identity of a tourism village (Gao & Wu, 2017; Kelfaoui et al., 2021; Ginting et al., 2024). Tourism villages encounter challenges in development due to inadequate management of promotional information and limited access to information technology, resulting in their inability to attract tourists (Amrullah et al., 2023) sustainably. Human resources that can convey digital knowledge and strategies are essential for digital transformation (Satar et al., 2025). The deficiencies in media and literacy skills among managers, particularly in marketing, are believed to contribute to the inadequate influx of both domestic and international tourists to tourism villages (Priyambodo & Artianingsih, 2022; Kusumastuti et al., 2024; Hailuddin et al., 2022).
Developing a sustainable tourism model in tourism villages through digital transformation is a viable solution to address the problems faced by tourism villages, as well as an effective alternative strategy for their development. Digital transformation demonstrates a company’s capacity to use digital technology to develop new business models that significantly enhance business outcomes and generate new corporate value propositions and organizational identities (Verhoef et al., 2021; Wessel et al., 2021; Huang et al., 2023; Ricardianto et al., 2023). Digital transformation encompasses not merely adopting new technology but also entails profound alterations in business strategy, operational processes, and how companies engage with customers. Through digital technology, tourism villages can enhance the tourist experience, optimize operational efficiency, and expand their market reach. Digital marketing transformation is an important aspect of digital transformation in promoting various tourist attractions. It economizes time and resources within the tourism sector and delivers pertinent information to visitors before, during, and after excursions (Ginting et al., 2024). According to the We Are Social report, 213 million individuals will be connected to the Internet in 2023, with 167 million active social media users in Indonesia. These data indicate that digital marketing can be the primary gateway for visitors to obtain information about tourist attractions in diverse regions through the Internet.
The escalating competition in the digital era necessitates dedication from enterprises to evolve and innovate through digital transformation. Nevertheless, not all enterprises perceive digital transformation as a straightforward endeavor. Numerous studies have examined and confirmed the factors affecting the decision to utilize digital media in business, typically internal and external (Alsaad et al., 2014; Samosir et al., 2023). Numerous studies on digital transformation have been conducted (e.g., Ramdansyah & Taufik, 2017; Chandra & Kumar, 2018; Hayati & Andrawina, 2019; Govinnage & Sachitra, 2019; Sin & Sin, 2020; Al-Tit, 2020; Tripathi & Singh, 2024; Nurhayati et al., 2023). However, there remains a paucity of research on digital transformation within the tourism sector, particularly concerning tourism villages. This study aims to address this gap by identifying the determinants of the intention and commitment of managers and stakeholders in tourism villages to implement digital transformation internally and externally. Internal factors frequently regarded as significant include perceived benefits and attitudes towards change, whereas external factors encompass consumer behavior change and the technological context.
SMEs, particularly those in tourism villages, decide to undertake digital transformation based on their perception of its benefits. Many studies examining the correlation between perceived benefits and digital adoption have proliferated, demonstrating a significant influence on digital adoption (Govinnage & Sachitra, 2019). Conversely, numerous studies indicated that perceived benefits exerted no influence (e.g., Kleijnen et al., 2007; Ko et al., 2009). A contributing factor to the inconsistent findings is that current studies frequently examine only a limited number of driving factors, neglecting other variables and concentrating on direct effects while overlooking potential indirect effects mediated by other factors (Hubert et al., 2017).
Anticipatory and adaptive abilities are essential for individuals and organizations to deal with rapid change (Van Den Heuvel et al., 2017). The attitudes and behaviors of individuals within the organization can influence success or failure in anticipating and adapting to change. According to multiple prior studies, the execution of inadequate change management results in the failure of transformation initiatives (Dietz et al., 2013). The efficacy of organizational change in anticipating and adapting to transformations is significantly contingent upon the perspectives of management and employees regarding the change. Similarly, attitude toward change is crucial for effectively adapting to change in digital transformation. An attitude towards technological change can enhance organizational initiative and commitment to digital transformation.
Consumer behavior is dynamic (Singh et al., 2024), and digitalization drives alterations in consumer behavior. The swift advancement in digitalization has led consumers to increasingly depend on digital platforms for information retrieval, purchasing, and brand interaction. This alteration compels enterprises to adjust to evolving consumer behavior to maintain market relevance. Significant technological advancements in the present century have transformed lifestyles, work patterns, business management, and consumer behavior. Recent research indicates that technological advancements influence consumer purchasing behavior shifts (Pancic et al., 2023). Efficient lifestyle decisions render e-commerce, online retail, social media, and other digital platforms the mediums for searching, selecting, and transacting (Hubert et al., 2017; Manes & Tchetchik, 2018; Singh et al., 2024). Recently, there has been a rise in academic studies that thoroughly discuss digital consumer behavior (Kamkankaew et al., 2022; Rogova & Matta, 2023).
Technological changes, including the advent of big data, artificial intelligence (AI), the Internet of Things (IoT), and cloud computing, present new opportunities for enterprises. Enterprises can integrate these technologies into their business strategies to maintain competitiveness. The efficacy of the digital adoption process is contingent upon the degree of technological proficiency attained (Emini & Merovci, 2021). In the digital marketing era, which has expanded significantly due to technological advancements, adaptation to technology is essential for all companies, including those in the SME scale (Hayati & Andrawina, 2019; Pranitasari et al., 2024).
Benteng Tourism Village is one of the developing tourist villages in Indonesia. It focuses on edu-agrotourism and features cultural, religious, artificial, culinary, educational, and nature tourism destinations. Benteng Tourism Village primarily encounters issues related to inadequate management of tourist villages, particularly concerning the utilization of digital media and restricted digital literacy among managers and enterprises. This constraint has compelled managers and enterprises of tourism villages to rely solely on traditional marketing, resulting in restricted market reach and a lack of awareness regarding the uniqueness, advantages, and potential of Benteng tourism villages among the broader public. Research on sustainable tourism remains insufficient and is deemed essential, as Indonesia possesses significant tourism potential, with each region prioritizing tourism as a critical sector (Jumiati et al., 2024).
For this reason, this study aims to identify the factors determining the success of digital transformation in tourism villages by examining the internal and external factors that drive the intention and commitment of entrepreneurs and managers to adopt digital marketing applications. This research is significant at both the academic and managerial levels. This research is significant at the academic level for enhancing the limited references on digital transformation in tourism villages, including identifying its determinants. For managers of tourist destinations, this study serves as a crucial resource for decision-making regarding the digitalization of the tourism sector. The structure of this paper includes an introduction explaining the background and identification of problems faced by Benteng tourism village in digital transformation, a literature review explaining previous research that is the basis for the research, the research methods used, the results of the discussion, conclusions, and recommendations. Research limitations are part of the conclusion to provide recommendations for future work-related agendas.

2. Literature Review

Accelerated technological advancements necessitate that companies undergo technological transformation to remain competitive in the market (Mutmainah et al., 2020; Omol, 2024; Owoseni, 2023). According to the dynamic capability theory (H. Zhang & Zhang, 2023), digital transformation constitutes an ability to swiftly adapt to external environmental changes, enhancing the organization’s efficiency and competitiveness. Digital transformation is crucial to business development success (Rupeika-Apoga et al., 2022; Harsasi et al., 2023).
Despite the extensive research and discourse surrounding digital transformation, scholars present various definitions, each reflecting distinct concerns (Xiao et al., 2022). Digital transformation, as defined by Palos-Sánchez et al. (2023) and Gurcan et al. (2023), is characterized as an organizational adaptation to environmental changes, particularly within the technological change, encompassing modifications in processes, methodologies, and workplace culture through automation to enhance efficiency. Digital transformation is a process in which organizations leverage cutting-edge digital technologies, including information technology, communication, and analytics, to enhance management models, develop new operational systems, and bolster performance and sustainable competitiveness (Nambisan, 2017; Yoo et al., 2024). Digital transformation consists of two perspectives: the technical perspective, which emphasizes the role of information technology in business processes, and the value perspective, which emphasizes that changes in thinking about digital transformation change the value proposition and operating model in utilizing technology (Ji & Li, 2022). Digital transformation is a multifaceted process influenced by numerous factors that dictate its success (G. Zhang et al., 2023). Numerous studies indicate that the critical determinants of successful digital transformation encompass perceived benefits (Ritz et al., 2019), attitudes toward change (Van Den Heuvel et al., 2017), market pressures from both consumers and competitors (G. Zhang et al., 2023; Wong et al., 2020; Oubrahim & Sefiani, 2023), and technological factors (Ghobakhloo et al., 2022; Hayati & Andrawina, 2019; Emini & Merovci, 2021).
One of the digital transformations that is proliferating is digital marketing transformation. Digital marketing leverages digital technologies, including the Internet, mobile devices, social media, and search engines, to promote products or services (Yasmin et al., 2015; Yoon, 2024). Numerous media channels, such as email marketing, affiliate marketing, display advertising, pay-per-click, mobile marketing, social media marketing, SMS marketing, SEO, search engine marketing, and content marketing, are extensively utilized by various companies (Yasmin et al., 2015).
Numerous internal and external factors affect the efficacy of digital marketing transformation. Variations in business types and scales will yield disparities in predominant factors. In small businesses, characterized by certain limitations, the primary internal factor motivating stakeholders to engage in digital marketing transformation is the presence of solid intentions. The intention to use denotes the preparedness and motivation of individuals or organizations to employ a particular resource (Tsai, 2012; Alam et al., 2020); specifically concerning using digital technology in this study. A greater intention among business actors to implement digital marketing transformation correlates with increased commitment. Research indicates that the perception of benefits significantly influences individual intentions and commitments to utilize digital applications, serving as an effective predictor (Abu-Silake et al., 2024). The strong motivation of business entities to implement digital transformation depends on their perception of the benefits of digital marketing transformation and their attitude toward change.
Perceived benefit denotes an individual’s assessment of the advantages of utilizing a specific technology, influencing attitudes toward adopting new technology (Jou et al., 2024). Perceived benefit is the recognition of advantages or enhancements arising from current business transaction execution methods through digital media (Agwu & Murray, 2015). Enhanced perceptions of the advantages of digital marketing transformation will elevate the willingness of business entities to implement such transformations. Jou et al. (2024) assert that perceived benefit directly influences the intention to use. Consumer behavior examines the socio-cultural and psychological factors that affect the individual processes of researching, purchasing, and utilizing products and services (Madhavan & Chandrasekar, 2015). Digital consumer behavior pertains to the consumer’s process of researching, purchasing, and utilizing products through digital services across diverse platforms (Güngör & Çadırcı, 2022), including the Internet, mobile devices, social media, and other digital channels (Wagner et al., 2020; Rogova & Matta, 2023).
Attitude is a response to a stimulus frequently referenced in the management literature to characterize employee responses to organizational changes (Pan et al., 2021). An organization’s efficacy in implementing changes depends on its members’ support and attitude toward change. The conceptualization of Attitude Toward Change (ATC) has been a protracted debate within research. It focuses on whether it should be framed in negative or positive terms, with various concepts articulated positively and negatively (Van Den Heuvel et al., 2017). This study defines ATC as the readiness to change (Van Den Heuvel et al., 2017).
Additionally, Pan et al. (2021) define ATC as the capacity to adapt to rapid change. The attitudes towards change in this study indicate a favorable disposition among business actors to adapt to advancements in information technology. A more favorable attitude of business actors towards technological changes correlates with an increased interest in implementing digital marketing transformation. The attitude towards change plays a significant role (Govinnage & Sachitra, 2019). Organizations with a favorable disposition towards change, particularly technological advancements, will typically find it easier to embrace change and execute digital transformation. The disposition towards change significantly impacts the intention and commitment to implement digital transformation. The disposition of individuals or organizations can either foster or hinder success in adapting to change (Van Den Heuvel et al., 2017). An optimistic disposition towards change arises when organizational members recognize the advantages of change, possess confidence in their capacity to embrace new technologies, and comprehend how digital transformation can enhance the organization’s efficiency and competitiveness, thereby motivating them to allocate resources, time, and energy to ensure the success of the digital transformation.
The evolution of digital technology has transformed consumer behavior (Kalashnikova et al., 2023; Güngör & Çadırcı, 2022). Consumer behavior changes denote conduct modifications as a reaction to digitalization (Verhoef et al., 2021). The advancement in information technology, resulting from the industrial revolution, has transformed transactional mindsets and behaviors from traditional to digital (Efendioglu, 2024). Advancements in information technology have transformed consumer information retrieval and transaction processes, emphasizing efficiency and convenience. The accessibility of search engines, social media, e-commerce, online retail, mobile banking, and digital wallets provides a new experience for conducting transactions that are more efficient, informative, and precise (Verhoef et al., 2021). This transition will prompt companies to embrace digital transformation. If companies are hesitant to adapt, their business practices become less appealing to consumers, who may transition to competitors that align with their preferences. The implementation of digital transformation in business is essential for survival.
The evolution of digital platforms provides numerous new ways for companies to engage with current and prospective consumers (Rogova & Matta, 2023). In the realm of digital transformation, technology assumes a pivotal role (Cheng et al., 2023). Technology is an instrument that enables organizations to advance more effectively through task automation (Gurcan et al., 2023). Technology is the primary consideration when an organization plans or undertakes digital transformation. Digital technology supports the success of transformation, which helps substantially change the digitalization of corporate business (G. Zhang et al., 2023). Using technology through digitalization can increase efficiency and improve productivity for business actors, add value for customers, and create new business models (Tagscherer & Carbon, 2023). Organizations with elevated levels of digital technology utilization possess the groundwork to excel in digital transformation.
The subsequent research model illustrates the role of internal factors, specifically the perception of benefits and attitude towards change, as well as external factors, specifically consumer behavior changes and the technological context, in encouraging the intention and commitment of business actors to implement digital marketing transformation in this study.
The research model in Figure 1 indicates that commitment to digital transformation is affected by perceived benefits, attitudes toward changes, consumer behavior changes, and technology both directly and indirectly through the mediation of the intention to implement digital transformation. Testing the relationship between variables using the following hypotheses:
H1: 
Perceived benefits positively influence the commitment to implement digital transformation.
H2: 
Attitude towards change positively influences the commitment to implement digital transformation.
H3: 
Consumer behavior changes positively influence the commitment to implement digital transformation.
H4: 
Technological changes positively influence the commitment to implement digital transformation.
H5: 
Perceived benefits positively influence the intention to implement digital transformation.
H6: 
Attitude towards changes, positive intention, and commitment to implementing digital transformation.
H7: 
Consumer behavior changes positively in the commitment to implement digital transformation.
H8: 
Technological changes positively influence the intention to implement digital transformation.
H9: 
Intention to implement digital transformation positively influences the commitment to implement digital transformation.

3. Materials and Methods

This study examines the influence of perceived benefits, attitudes toward change, consumer behavior change, and technology on the commitment to implement digital transformation to implement digital transformation as a mediating variable. Perceived benefit refers to an individual’s assessment of the advantages of utilizing a specific technology, influencing attitudes toward adopting new technology (Jou et al., 2024; Güngör & Çadırcı, 2022), attitude toward change refers to the readiness to change digitalization (Van Den Heuvel et al., 2017; Cavalcanti et al., 2022), consumer behavior change refers to conduct modifications as a reaction to digitalization (Verhoef et al., 2021), technological context refers to respondents’ perceptions of how technology development plays a role in their business (Yoon, 2024), digital transformation intention refers to the preparedness and motivation of respondents to employ a particular resource (Tsai, 2012; Boateng et al., 2016). Digital transformation commitment refers to the respondent’s commitment to digital transformation in their business (Cavalcanti et al., 2022; Cardoso et al., 2024). These variables in this study were measured using 38 indicators, as shown in Table 1 below:
In response to these objectives, this study utilizes primary data obtained by distributing questionnaires to respondents. Due to the relatively small population size, which is 146 people, the sampling method is saturated. Thus, all members of the population become samples. With three models, the Causal Step multiple regression method tests the impact of independent variables on dependent variables through mediating variables. Model 1 examines the impact of perceived benefits, attitudes towards change, consumer behavior changes, and technological context on the commitment to digital transformation. Model 2 examines the impact of perceived benefits, attitude towards change, consumer behavior change, and technological context on the intention to implement digital transformation. In contrast, Model 3 examines the impact of perceived benefits, attitudes towards change, consumer behavior change, technological context, and digital transformation intention on the commitment to implement digital transformation.
The rule of thumb of this method posits that variable M is stated as a mediating variable between the impact of variable X on variable Y if (i) the independent variables have a significant effect on the dependent variable, (ii) the independent variables have a significant effect on the mediating variable, and (iii) both the independent variable and the mediating variable significantly affect the dependent variable. Variable M is a perfect mediator if, after entering M into the regression model, the effect of variable X on Y becomes insignificant. Variable M is classified as a partial mediation variable if, upon its inclusion in the regression equation model, the effect of variable X on Y remains significant while the regression coefficient diminishes. A classical assumption test is used to verify that the regression model is unbiased and efficient. Regression model estimation using SPSS version 25.

4. Results

4.1. Respondents’ Demographic Information

The survey encompassed all tourism village managers and enterprises in the Benteng Tourism Village area, totaling 146 individuals. Questionnaires were disseminated by selected and trained enumerators. Table 2 presents a general description of the respondents.
The data collected from 146 participants showed that 51.27% were female and 48.63% were male. The majority of participants are in the 40–49 age group (36.30%), followed by the 50–59 age group (31.51%), the 30–39 age group (14.38%), under 30 years old (10.27%) and over 59 years old (7.53%). Regarding educational attainment, 55.48% of the participants held a high school degree, 19.86% held a college degree, 17.12% held a primary school degree, and only 0.68% held a master’s degree.

4.2. Validity and Reliability Test

Reliability testing using Cronbach’s Alpha Coefficient, where Bach et al. (2024) stated that a variable is reliable if Cronbach’s Alpha coefficient exceeds 0.7. Conversely, Mutmainah et al. (2020) stated that a variable is reliable if Cronbach’s Alpha coefficient is more significant than 0.6. Validity testing compares the Corrected Item-Total Correlation coefficient with the r table at degrees of freedom (n−k), where n represents the amount of data. At the same time, k is the number of indicators for each variable. An indicator is considered valid if the Corrected Item-Total Correlation coefficient exceeds the r-table coefficient for the relevant degrees of freedom and vice versa. Table 3 presents the results of the reliability and validity tests.
Based on Table 3, the reliability test results indicate that all research variables have a Cronbach’s Alpha coefficient exceeding its critical value. Therefore, the perceived benefit, attitude towards change, consumer behavior change, technological context, intention to use digital transformation and commitment to using digital transformation variables are stated as reliable. The validity test results of the research instrument indicate that all indicators for the variables of perceived benefit, attitude towards change, consumer behavior change, technological context, digital transformation intention to use, and digital transformation commitment to use have a correlation coefficient exceeding the critical value at α = 0.05, thereby confirming the validity of all research variable instruments.

4.3. Multiple Regression Analysis with Causal Step Model

Model 1. The influence of perceived benefits (PB), attitude toward change (ATC), consumer behavior change (CBC), and technological context (TC) on digital transformation commitment (DTC).
DTC = β0 + β1PB + β2ATC + β3 CBC + β4TC + ɛ
The statistical test results for Model 1, as seen in Table 4, indicate that perceived benefits, attitudes toward changes, consumer behavior changes, and technological context significantly influence the commitment to implement digital transformation. Based on the significance level of the coefficients of the variables of perceived benefits, attitudes towards change, changes in consumer behavior, and technological context, the value is less than 0.05.
Model 2. The influence of perceived benefit (PB), attitude towards change (ATC), consumer behavior change (CBC), and technological context (TC) on digital transformation intention (DTI) is stated in the following model:
DTI = β0 + β1PB + β2ATC + β3 CBC + β4TC + ɛ
The statistical test results for Model 2, as seen in Table 5, indicate that the variables of attitude toward change, consumer behavior changes, and technological context significantly influence the intention to implement a digital transformation. Based on the significance level of the coefficient of the variables of attitude towards change, changes in consumer behavior, and technological context, the value is less than 0.05. In contrast, the variable of perceived benefits does not significantly affect this intention. The significance level of the perceived benefits variable coefficient is more than 0.05.
Model 3. The influence of perceived benefit (PB), attitude towards change (ATC), consumer behavior change (CBC), technological context (TC), and intentions to implement digital transformation (DTI) on the commitment to implement digital transformation (DTC).
DTC = β0 + β1PB + β2ATC + β3 CBC + β4TC + DTI5 + ɛ
The statistical test result of Model 3, as seen in Table 6, indicates that the variables of perceived benefits, attitudes towards change, consumer behavior changes, technological context, and intentions to implement digital transformation significantly influence the commitment to implement a digital transformation. Based on the significance level of the coefficient of perceived benefits, attitudes towards change, consumer behavior changes, and intentions to implement digital transformation, the value is less than 0.05. In contrast, the technological context variable does not significantly affect the commitment to implement digital transformation. Based on the significance level of the technological context coefficient, which is more than 0.05.

4.4. Test of Classical Assumptions

4.4.1. Normality Test

The central limit theorem states that deviation from normality is not a significant problem when the sample size is 100 or more (Mishra et al., 2019); however, a normality test can still provide more meaningful conclusions. The normality test uses Shapiro–Wilk to assess the normality of the data. The Shapiro–Wilk normality test was selected due to the limited data volume, as (Mishra et al., 2019) indicated that normality tests like Kolmogorov–Smirnov necessitate a larger dataset. The hypothesis posits that if the significance value exceeds 0.05, one may infer that the data originate from a normally distributed population. Table 7 demonstrates that perceived benefit, attitudes towards change, consumer behavior changes, technological context, intention to implement digital transformation, and commitment to implement digital transformation variables possess Shapiro–Wilk statistical values with a significance greater than 0.05, allowing for the conclusion that the data in this study originate from a normally distributed population.

4.4.2. Multicollinierity Test

The multicollinearity test aims to ensure a correlation between independent variables. Multicollinearity testing identifies each independent variable’s Variance Inflation Factor (VIF) and tolerance values. When the tolerance value exceeds 0.10, and the VIF is below 10, it indicates the absence of multicollinearity symptoms. The multicollinearity test results, as seen from Table 8, indicate that perceived benefit, attitudes towards change, consumer behavior changes, technological context, and intention to implement digital transformation variables have VIF values below 10 and tolerance values above 0.10, thus concluding that the constructed model does not indicate multicollinearity.

4.4.3. Heteroscedasticity Test

The heteroscedasticity test identifies inequality of variance in the residuals of a regression model for a single observation. This study employs the Glejser test for heteroscedasticity, assessing its significance based on whether the result exceeds 0.05. If the significance exceeds 0.05, the regression model will be free from heteroscedasticity. The study’s results, as seen in Table 9, indicated that the significance values of all independent variables exceeded 0.05, thus concluding that the model did not indicate heteroscedasticity.

5. Discussion

This study aims to analyze the influence of perceived benefits, attitudes toward change, consumer behavior changes, and technology on a commitment to digital transformation to use digital transformation as a mediating variable. The summary of the test results using the Causal Step model obtained the following model:
The findings of the Causal Step test, as seen from Table 10, indicate that the variables of perceived benefits, attitudes towards change, consumer behavior changes, technological changes, and intentions have a positive and significant influence on the commitment to implement digital transformation. The findings of this study align with the research outcomes from (Qiu et al., 2023; Wang & Dong, 2023; Pinyanitikorn et al., 2024; Li, 2024; Paramita & Hidayat, 2023; Hammood et al., 2023; Sutticherchart & Rakthin, 2023; Schönherr et al., 2023; Paun et al., 2024; Chen et al., 2024; Mansur et al., 2021). As respondents perceive more significant benefits from digital transformation, their attitudes towards change become increasingly optimistic, leading to modifications in consumer behavior that enhance commitment to implementing digital transformation. The intention to implement digital transformation can mediate the impact of attitudes towards change, consumer behavior changes, and technological changes on the commitment to implement digital transformation. The intention variable becomes a perfect mediator for the impact of technological change on the commitment to implement digital transformation, whereas the attitudes towards change and consumer behavior changes, the intention variable acts as a partial mediator. The study’s results indicate that the variable of intention to implement digital transformation does not mediate the impact of perceived benefits on the commitment to implement digital transformation. In other words, perceived benefits directly influence the commitment to implement digital transformation without mediation by the intention to implement it. The study reveals that perceived benefits do not significantly influence the intention to implement digital transformation, contrasting with prior research that suggests a correlation between perceived benefits and the intention to implement digital transformation. Managers and enterprises in tourism villages may not have experienced the benefits of digital transformation, rendering it an ineffective motivator for pursuing such changes.

5.1. The Influence of Intention to Implement Digital Transformation on Commitment to Implement Digital Transformation

The study’s findings indicate that the intention to implement digital transformation significantly influences commitment to implement digital transformation. This shows that the greater the intention of village tourism managers and business actors to implement digital transformation, the greater their commitment. Individuals respond to various conditions, objects, contexts, and emerging technologies (Boateng et al., 2016) to predict outcomes through specific actions. While intention does not currently reflect actual behavior, firm intention can predict actual actions (Thoumrungroje & Suprawan, 2024). As stated by (Sartono et al., 2024), the intention is the willingness and readiness of individuals or organizations to implement. The intensity of this hope fosters a dedication among them to fulfill their intentions. The advancement in technology, which village tourism managers and enterprises cannot ignore, compels them to adapt their work and business practices to leverage these technological transformations. The findings of this study align with the research conducted by (Qiu et al., 2023; Wang & Dong, 2023; Pinyanitikorn et al., 2024).

5.2. The Influence of Perceived Benefit on Commitment to Implement Digital Transformation

Among the enterprises in the Benteng Tourism Village area, merely 48.63% have utilized limited online applications to market their products through WhatsApp Group, and they perceive the benefits. Thus, these perceived benefits encourage their interest in utilizing additional digital media. The study’s findings indicate that perceived benefit significantly influences commitment to implement digital transformation. This indicates that the more substantial the benefits of digital transformation recognized by tourism managers and enterprises, the greater the commitment to implement digital transformation. The benefits they perceive are associated with digital transformation, enhancing transactions, improving operational efficiency, and augmenting sales and profits. This positive perception of digital transformation encourages the implementation of digital transformation. The findings of this study align with the research conducted by (Hubert et al., 2017; Paramita & Hidayat, 2023; Hammood et al., 2023; Sutticherchart & Rakthin, 2023).

5.3. The Influence of Attitude Towards Change on Commitment to Implement Digital Transformation

The study’s findings indicate that attitude towards change significantly influences commitment to implement digital transformation. A positive attitude among managers and enterprises towards environmental change correlates with a heightened commitment to implement digital transformation. Attitude towards change refers to the evaluation and reaction to environmental alterations encountered. The attitudes towards change influence the commitment of tourism village managers and business stakeholders in the tourism village to implement digital transformation. The attitudes towards change indicate their responsiveness to technological advancements, including their readiness to adapt to new technologies. Attitude towards change is observed through their acceptance and mindset. If tourism village managers and business stakeholders have positive attitudes towards change, they will be more open to adopting technology and even investing in digital transformation; conversely, resistance to technological change will impede the commitment to digital transformation. Ahmed et al. (2024) state that a positive attitude toward digital transformation significantly influences their success in digital transformation, while a resistant attitude toward change can thwart digital transformation efforts. Managers and business stakeholders with a growth mindset will perceive digital transformation as an opportunity to enhance efficiency and competitiveness. In contrast, those with a fixed mindset typically resist change due to their comfort with established practices. A growth mindset is essential for effective technology utilization (Thomas et al., 2024; Bogdány et al., 2024). The findings of this study align with the research conducted by Schönherr et al. (2023) and Liu (2024).

5.4. The Influence of Consumer Behavior Change on Commitment to Implement Digital Transformation

The study’s results indicate that changes in consumer behavior significantly impact commitment to implementing digital transformation. Consumer behavior change encourages business stakeholders to enhance their commitment to digital transformation. Consumer behavior change is challenging and compels business entities to intensify their commitment to digital transformation. Technological advancements, particularly information technology, have transformed consumer behavior in communication, interaction, task completion to fulfill needs, and service utilization. Technological advancements encourage increased consumer demand for rapid, personalized, and flexible services, prompting businesses to adapt to maintain consumer loyalty. Consumer satisfaction enhances loyalty (Tarnanidis, 2024). As a result, companies that fail to provide a fast digital experience are turning consumers away, including in the tourism sector. This compels business entities to invest in technology and commit to digitalization. If not executed, their business will be forsaken by consumers and fail to compete with rivals—in this case, with other tourist destinations that have undergone digital transformation. Changes in consumer behavior towards pervasive digitalization will enhance the commitment of enterprises to implement digital transformation. The findings of this study align with the research conducted by Chen et al. (2024).

6. Conclusions, Implications and Limitations

6.1. Conclusions

This study utilizes primary data from Benteng Tourism Village managers and enterprises in that tourism village to examine the key driving factors influencing their commitment to implement digital transformation. It examines the impact of perceived benefits, attitudes towards change, consumer behavior change, and technology on the intention and commitment to implement digital transformation. This study’s results offer detailed insights into how perceived benefits, attitudes towards change, and consumer behavior change, directly and indirectly, affect the commitment to implement digital transformation through the mediation of the intention to implement digital transformation. The results of statistical tests on the hypotheses show that perceived benefits directly influence the commitment to implement digital transformation. Attitudes towards changes in consumer behavior in the context of technology influence commitment to implementing digital transformation directly and indirectly, with the mediation of intentions to implement a digital transformation, where consumer behavior changes have the most significant impact. The intention to implement digital transformation became a perfect mediator of the technological context of the commitment to implement a digital transformation. It became a partial mediator of the influence of digital attitudes towards change and consumer behavior change on the commitment to implement transformation.

6.2. Theoretical Implications

This study provides at least two theoretical implications. First, research on digital transformation in tourism villages provides a different viewpoint on the main factors driving the intention and commitment of managers and enterprises around tourism villages to implement digital transformation, predominantly involving managers of tourism villages and small and medium-sized enterprises (SMEs). Second, the identification of variables as the main drivers of intention and commitment to implement digital transformation in the case of tourist villages. As is known, SMEs also have characteristics that are their weaknesses, such as the inability to respond quickly to market changes caused by perceptions of benefits, technological changes, attitudes towards change, and the ability to respond to changes in consumer behavior and technology. The study’s results indicate that perceptions of benefits, attitudes towards changes, changes in consumer behavior, and technology that influence commitment to digital transformation will enrich research on the determinants of digital transformation in tourist villages or SMEs at the business level.

6.3. Managerial Implications

The study results show that perceived benefits, attitudes towards change, consumer behavior change, and technological context significantly influence intention and commitment to implement digital transformation. These results provide a signal regarding strategies that need to be carried out by tourism village stakeholders such as the government, businesses, and universities to determine steps to develop tourism villages through digital transformation collaboratively.
To improve the perceived benefits of tourism village managers and businesses, the government collaborates with universities and businesses to conduct socialization and training for tourism village managers and businesses on how digitalization can increase the number of visits, income, and operational efficiency. Best practices of tourism villages that have successfully carried out digital transformation serve as references to motivate and increase trust. Direct assistance, such as internet subsidies, digital tools, or access to online tourism marketplaces, must be provided, considering that most tourism managers and business actors are MSMEs.
To build a positive attitude towards change in tourism village managers and businesses, the government collaborates with universities and businesses to conduct education and a mindset change campaign to increase understanding that digitalization is not only a demand of the times but also an opportunity to increase competitiveness. Involving community leaders, communities, and local leaders as agents of change is important such that the message of digital transformation is more readily accepted.
To improve the responsiveness of tourism village managers and businesses to consumer behavior changes oriented towards the use of information technology in searching for information and meeting their needs, the government collaborates with universities and businesses to build a digital ecosystem, such as a national tourism marketplace, to help tourism villages reach tourists more efficiently by providing a digital platform for promotion. The government, universities, and businesses must help tourism village managers and businesses build partnerships with influencers and digital media to increase tourism village exposure and attract digital-savvy tourists.
To improve the accessibility of tourism village managers and businesses to technological developments, the government collaborates with universities, and business needs to help strengthen digital infrastructure, such as guaranteeing stable and affordable internet access in tourism villages through public Wi-Fi procurement programs and better telecommunications networks. The government collaborates with universities and businesses to facilitate the development of applications and Tourism Management Systems that make it easier for tourism villages to manage visits, reservations, and marketing online, followed by training for tourism village managers and businesses in technology utilization, such as social media management, tourist data analysis, and digital payment systems.

6.4. Limitations and Lines for Future Research

This study has inherent limitations. Without reducing the quality of the results and insights, some limitations are as follows:
This study utilizes a limited dataset comprising 146, representing the population of the Benteng tourist village managers and enterprises in the Benteng tourist village area. The restricted data quantity constrains the application of more advanced analysis methods that require more data. Further research is suggested using more analytical units and data to strengthen the findings.
Due to the heterogeneous characteristics of tourist villages in Indonesia and globally, including variations in geography, tourism services, demographic traits, and local culture, the model derived from this study cannot be generalized to all tourist villages; it is only relevant to tourist villages exhibiting similarities to Benteng tourist village. Further research can modify the independent variables based on the characteristics of the tourist village or destination that is the object of research. Mixed method research is essential for further research by obtaining more qualitative data to provide a more comprehensive understanding of the digital transformation of tourism villages.

Author Contributions

Conceptualization, I.M. and I.A.Y.; methodology, F.A.S. and E.E.; software, I.A.Y. and B.H.R.; validation, S.H., I.N. and A.S.S.; formal analysis, I.M. and E.E.; investigation, F.A.S. and E.E.; resources, B.H.R. and A.S.S.; data curation, I.N., S.H. and F.A.S.; writing—original draft preparation, I.M. and I.A.Y.; writing—review and editing, S.H. and E.E.; visualization, F.A.S.; supervision, I.M., I.N. and B.H.R.; project administration, I.A.Y. and A.S.S.; funding acquisition, I.M., S.H. and I.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Directorate of Research, Technology and Community Service, Directorate General of Higher Education, Research and Technology, Ministry of Education, Culture, Research and Technology of the Republic of Indonesia. Following Master Contract Number 059/E5/PG.02.00/PL.BATCH.2/2024 for competitive research grant funding for the KATALIS scheme.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the scientific research ethical committee, The Research Ethics Committee of the Institute for research and Community Service (LPPM) (004/LPPM-UNB/S-Ket/IX/2024; 2 September 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Tourismhosp 06 00057 g001
Figure 1. Conceptual Model of Digital Transformation Commitment.
Figure 1. Conceptual Model of Digital Transformation Commitment.
Tourismhosp 06 00057 g001
Table 1. Conceptual model’s variables.
Table 1. Conceptual model’s variables.
VariablesAuthorsNumber of IndicatorsScale
Perceived Benefits (PB)Jou et al. (2024); Güngör and Çadırcı (2022)4From 1 (strongly disagree) to 5
(strongly agree)
Attitude Toward Change (ATC)Van Den Heuvel et al. (2017); Cavalcanti et al. (2022)4From 1 (strongly disagree) to 5
(strongly agree)
Consumer Behavior Change (CBC)Verhoef et al. (2021)4From 1 (strongly disagree) to 5
(strongly agree)
Technology Context (TC)Yoon (2024)3From 1 (strongly disagree) to 5
(strongly agree)
Intention to Implement Digital Transformation (DTI)Tsai (2012); Boateng et al. (2016)3From 1 (strongly disagree) to 5
(strongly agree)
Commitment to Implement Digital Transformation (DTC)Cavalcanti et al. (2022); Cardoso et al. (2024)18From 1 (strongly disagree) to 5
(strongly agree)
Table 2. Respondents’ demographic information.
Table 2. Respondents’ demographic information.
Demographic CategoryParticipants%
GenderMale7148.63
Female7551.37
Age<301510.27
30–392114.38
40–495336.30
50–594631.51
>59117.53
EducationPrimary School Degree106.85
Secondary School Degree2517.12
High School Degree8155.48
College Degree2919.86
Master’s Degree10.68
Table 3. Reliability and Validity Test Result.
Table 3. Reliability and Validity Test Result.
Variables and IndicatorsCorrected Item-Total Correlationr TableConclusionCronbach’s AlphaConclusion
Perceived Benefit0.890Reliable
 X1. DT makes it easier to conduct transactions0.7020.159Valid
 X2. DT makes business operations more efficient0.7760.159Valid
 X3. DT will increase sales0.7860.159Valid
 X4. DT will increase the profit0.7790.159Valid
Attitude toward Change0.816Reliable
 X5. The belief is that every change will benefit the business.0.7340.159Valid
 X6. The belief is that every change must be followed.0.5090.159Valid
 X7. Adapting to change is beneficial.0.8120.159Valid
Consumer Behavior Change0.877Reliable
 X8. Consumers are starting to prefer looking for information regarding the goods or services they need through digital media.0.5860.159Valid
 X9. Consumers are starting to prefer making purchasing transactions through digital applications0.8070.159Valid
 X10. Consumers are starting to prefer making payment transactions through digital applications0.7670.159Valid
 X11. Consumers believe transacting via digital applications is more practical than conventional methods.0.7880.159Valid
Technological Context0.850Reliable
 X12. The development of information technology will create new opportunities for innovation.0.6260.159Valid
 X13. The accessibility of various digital media facilitates the acquisition of suitable digital transaction applications for business entities.0.7920.159Valid
 X14. The availability of many online guides for utilizing digital applications can assist business entities in operating these tools.0.7460.159Valid
Digital Transformation Intention to Use0.962Reliable
 Y1. Having an interest in utilizing digital applications for business0.9060.159Valid
 Y2. Having a desire to try utilizing digital applications for business0.9400.159Valid
 Y3. Having plans to utilize digital applications for business if the opportunity arises0.9120.159Valid
Digital Transformation Commitment to Use0.947Reliable
 Y4. The belief is that DT implementation is essential.0.7290.176Valid
 Y5. Believe that implementing DT will improve business operations0.6680.176Valid
 Y6. Implementing DT can enhance business performance.0.7240.176Valid
 Y7. Implementing DT can ensure business continuity.0.7100.176Valid
 Y8. Developing a plan to utilize digital technology for my business0.6580.176Valid
 Y9. Preparing financial resources to carry out DT.0.6370.176Valid
 Y10. Preparing human resources to carry out DT.0.6870.176Valid
 Y11. Preparing adequate facilities to support DT.0.7270.176Valid
 Y12. Preparing adequate infrastructure to support DT.0.7010.176Valid
 Y13. Having the awareness that DT requires adequate knowledge of digital technology is important.0.7270.176Valid
 Y14. DT requires adequate capabilities to utilize various digital applications.0.7160.176Valid
 Y15. Being aware that DT requires adequate technical capabilities to solve problems using digital technology applications.0.6830.176Valid
 Y16. Being aware that DT requires adequate skills to share digital technology knowledge with others.0.6690.176Valid
 Y17. Having confidence that DT will increase opportunities to create new businesses.0.7100.176Valid
 Y18. Having confidence that DT will strengthen relationships with partners.0.7050.176Valid
 Y19. Have confidence that DT will increase competitiveness.0.5950.176Valid
 Y20. Having confidence that DT will lead customers to discover your business.0.6670.176Valid
 Y21. Having confidence that DT will make service quality to customers better0.7520.176Valid
Table 4. Statistical Test Results on Model 1.
Table 4. Statistical Test Results on Model 1.
HypothesisUnstandardized CoefficientsStandardized CoefficientstSig.Interpretation
BStd. ErrorBeta
(Constant)12.4843.928 3.1780.002Significant
H1PB → DTC0.8670.2920.2272.9650.004Significant
H2ATC → DTC1.2090.2980.2574.0550.000Significant
H3CBC → DTC1.2280.3200.3153.8390.000Significant
H4TC → DTC1.0330.4280.1822.4170.017Significant
F(5.146) = 67.557
Prob > F = 0.000
R Square = 0.657
Adjusted R Square = 0.647
Table 5. Statistical Test Results on Model 2.
Table 5. Statistical Test Results on Model 2.
HypothesisUnstandardized CoefficientsStandardized CoefficientstSig.Interpretation
BStd. ErrorBeta
(Constant)1.8690.905 2.0660.041Significant
H5PB → DTI0.0620.0670.0850.9180.360Not Significant
H6ATC → DTI0.1660.0690.1862.4220.017Significant
H7CBC → DTI0.2420.0740.3273.2890.001Significant
H8TC → DTI0.2680.0980.2472.7170.007Significant
F(5.146) = 34.873
Prob > F = 0.000
R Square = 0.497
Adjusted R Square = 0.483
Table 6. Statistical Test Results on Model 3.
Table 6. Statistical Test Results on Model 3.
HypothesisUnstandardized CoefficientsStandardized CoefficientstSig.Interpretation
BStd. ErrorBeta
(Constant)10.7733.912 2.7540.007Significant
H1PB → DTI0.8100.2880.2122.8160.006Significant
H2ATC → DTI1.0570.2980.2243.5400.001Significant
H3CBC → DTI1.0060.3260.2583.0900.002Significant
H4TC → DTI0.7890.4300.1391.8330.069Not Significant
H9DTI → DTC0.9150.3590.1742.5510.012Significant
F(6.146) = 57.457
Prob > F = 0.000
R Square = 0.672
Adjusted R Square = 0.661
Table 7. Normality Test Results.
Table 7. Normality Test Results.
VariablesShapiro–Wilk
StatisticdfSig.
PerceivBenefit0.9841460.082
AttitudeToChange0.9841460.085
ConsBehavChange0.9831460.074
TechChange0.9871460.188
DTIntention0.9821460.053
DTCommitment0.9861460.150
Table 8. Multicollinearity Test Results.
Table 8. Multicollinearity Test Results.
ModelCollinearity Statistics
ToleranceVIF
1(Constant)
Percv.Benefit0.4132.420
AttitudeToChange0.5831.715
ConsBehavChange0.3352.989
Tech.Change0.4102.441
DTIntention0.5031.989
Table 9. Heteroscedasticity Test Results.
Table 9. Heteroscedasticity Test Results.
ModelUnstandardized CoefficientsStandardized CoefficientstSig.
BStd. ErrorBeta
1(Constant)10.8332.801 3.8670.000
Percv.Benefit0.3580.2060.2191.7360.085
AttitudeToChange−0.4210.214−0.209−1.9700.051
ConsBehavChange−0.0340.233−0.021−0.1480.883
Tech.Change−0.5100.308−0.210−1.6540.100
DTIntention−0.1550.257−0.069−0.6050.546
Dependent Variable: Abs_RES.
Table 10. Summary of Causal Step Model Testing Results.
Table 10. Summary of Causal Step Model Testing Results.
Independent VariablesModel 1Model 2Model 3
βSig.βSig.βSig.
Perceived Benefit0.8670.0040.0620.3600.8100.006
Attitude toward Change1.2090.0000.1660.0171.0570.001
ConsBehavChange1.2280.0000.2420.0011.0060.002
Technological Context1.0330.0170.2680.0070.7890.069
Digital Transformation Intention 0.9150.012
Dependent VariablesDigital Transformation CommitmentDigital Transformation IntentionDigital Transformation Commitment
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MDPI and ACS Style

Mutmainah, I.; Yulia, I.A.; Setiawan, F.A.; Setiawan, A.S.; Nurhayati, I.; Rainanto, B.H.; Harini, S.; Endri, E. Analysis of Factors Influencing Digital Transformation of Tourism Villages: Evidence from Bogor, Indonesia. Tour. Hosp. 2025, 6, 57. https://doi.org/10.3390/tourhosp6020057

AMA Style

Mutmainah I, Yulia IA, Setiawan FA, Setiawan AS, Nurhayati I, Rainanto BH, Harini S, Endri E. Analysis of Factors Influencing Digital Transformation of Tourism Villages: Evidence from Bogor, Indonesia. Tourism and Hospitality. 2025; 6(2):57. https://doi.org/10.3390/tourhosp6020057

Chicago/Turabian Style

Mutmainah, Isbandriyati, Iis Anisa Yulia, Foni Agus Setiawan, Aditya Sugih Setiawan, Immas Nurhayati, Bambang Hengky Rainanto, Sri Harini, and Endri Endri. 2025. "Analysis of Factors Influencing Digital Transformation of Tourism Villages: Evidence from Bogor, Indonesia" Tourism and Hospitality 6, no. 2: 57. https://doi.org/10.3390/tourhosp6020057

APA Style

Mutmainah, I., Yulia, I. A., Setiawan, F. A., Setiawan, A. S., Nurhayati, I., Rainanto, B. H., Harini, S., & Endri, E. (2025). Analysis of Factors Influencing Digital Transformation of Tourism Villages: Evidence from Bogor, Indonesia. Tourism and Hospitality, 6(2), 57. https://doi.org/10.3390/tourhosp6020057

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