Analysis of Omnichannel Consumer Behavior: Purchase Intention on Omnichannel Restaurants in Indonesia

The advancement of the internet and emerging technology has altered the world of industry through sectors. Additional networks also emerged, alter-ing buying preferences and purchasing behaviour. The omnichannel approach is a modern innovative method of marketing strategy in which, with the use of advanced technologies across networks, it is possible to integrate various channels for shopping to provide customers with a specific and comprehensive shopping experience. There is a disconnect in customers' omnichannel attitudes and businesses' ability to execute an omnichannel approach. The aim of this paper is to define and understand the factors that affect omnichannel consumers' actions during the shopping phase, specifically their adoption and usage of technologies. An initial model was created to explain omnichannel consumer behavior using the variables used in UTAUT2 and other previous research on omnichannel consumer behavior. Structural Equation Modelling was used to test the model on a sample of 495 customers of omnichannel restaurants (SEM). The findings suggest that the following factors significantly affect purchasing intention in an omnichannel context: habit, encouraging circumstances, personal innovativeness, hedonic drive,


Introduction
Recent technological advancements have allowed the digitization of numerous areas of industry. Starting in the retail industry and has been implemented in many, digitalization has presented new opportunities and challenges to businesses across industries. One of the challenges is the increasing complexity it made in selling to the customers. Consumer behavior has shifted dramatically as a result of advancements in mobile platforms and social networking, as well as the convergence of these emerging channels into online and offline retailing. Consumers are in-creasingly active in using alternate channels in buying products or services, requesting infor-mation, and asking about usage or availability (Neslin et al, 2006). When customers switch to dif-ferent platforms, a multichannel approach under which silo channels are planned and handled independently of one another is inconsistent and rife with inconsistencies (Saghiri et al., 2017). In an omnichannel setting, channels are used concurrently throughout the quest and purchasing period, rendering it very difficult for retailers to retain control (Verhoef et al, 2015).
Omnichannel commerce may be the third generation of e-commerce, in which several networks are utilized consistently (Juaneda-Ayensa et al., 2016). A study by EConsultancy (2019) found that most of the browsing online by Indonesian consumers is done on a smartphone, while the purchase decision is made in different channels such as desktop or even offline. This behavior is a defining characteristic of omnichannel consumers. The report also found that in Indonesia, 322 even though the marketers (90%) are aware of the need for real-time marketing through wellintegrated marketing technology and data, only half (54%) have integrated platforms in their respective companies. Most of the marketers that have integrated platforms also stated that their platform's capabilities are still unsatisfactory (45%). A significant number of the marketers in Indonesia also don't have an integrated platform for their marketing needs (29%). This statistic shows a gap between the practice of omnichannel strategy in Indonesia and the consumer behavior of Indonesian consumers, which are already omnichannel focused. Thus, an omnichannel strategy where integration and synergic management of different channels are the focus is ur-gently needed to explain the increasing complexity of consumer behavior in Indonesia.
This essay aims to advance theoretical awareness of the technological and behavioral antecedents of omnichannel consumers' product and service buying processes. We carried out this research in the restaurant industry context because of two reasons. The first being there are very few papers addressing the omnichannel strategy in the restaurant context and the second being the restaurant context is especially relevant in Indonesia, where online food delivery services are prominent with Go-Food and Grab-Food are one of the main reasons consumer behavior in the restaurant industry is changing (Mordor Intelligence, 2019). Additionally, this article proposes a modern technology paradigm focused on UTAUT2 (Venkatesh et al., 2012), which is expanded to include new dimensions-personal innovativeness, perceived security danger, and perceived compatibility-and is tailored to the omnichannel background.
Our study has significant analytical and managerial ramifications, as identifying the drivers of omnichannel consumer behavior enables businesses to develop unique omnichannel management techniques to improve customer loyalty through an optimized shopping experience (Verhoef et al, 2015;Juaneda-Ayensa et al, 2016;Kazancoglu & Aydin, 2018).

Literature Review Omnichannel retailing context
The concept of omnichannel retailing is seen as a progression from multichannel retailing (table 1). Although multichannel implies a division between channels, consumers in an omnichannel setting should be able to seamlessly migrate between networks, whether online or offline, within single purchase. A customer can search for products or services in the morning through their smartphone, compare prices in the afternoon through their PC, and purchase the product after work in the physical store. Consumers who shop omnichannel are a global phenomenon (Schlager and Maas, 2013), and they expect a frictionless and optimized purchasing experience along their purchase route (Juaneda-Ayensa et al., 2016). Due to the holistic management of networks, consumers communicate with the business rather than the site (Lazaris et al., 2015).
An omnichannel strategy's primary characteristic is that it is customer-centric and places a premium on the interaction between channels and consumers (Verhoef et al., 2015). Thus, the omnichannel strategy broadens the channel selection while still taking into consideration customer-brand-retail channel interactions (Neslin et al, 2014). This is because users, regardless of how they interact, expect a consistent, seamless, and interactive experience. Omnishoppers no longer enter the channel; rather, they are constantly present in it or several channels at the same time, owing to technological advancements and increased versatility (Juaneda-Ayensa et al, 2016). These consumers are always on the lookout for opportunities to use their smartphones to conduct searches, evaluate brands, and find better options to maximize the advantages provided by each channel (Yurova et al., 2017). It is critical to initiate research into omnichannel consumer behavior (Neslin et al., 2014;Verhoef et al., 2015).

Consumer behavior in an omnichannel context
Previous research has shed light on what makes an omnichannel buying experience and the impact it has on behavioral intentions. One of the key drivers of an omnichannel strategy is technology, as the growing adoption of new innovations in retail has shifted customer expectations and desires (Schlager & Maas, 2013). Omnichannel consumers want a cohesive and optimized experience regardless of the medium they use; they switch effortlessly between networks, whether offline or online, based on their tastes (Piotrowicz & Cuthberson, 2014). Technology is critical in online retailing.
As a result, recent research on omnichannel user behavior has focused on technology. Channel convergence is a distinguishing characteristic of the omnichannel shopping experience (Hure et al., 2017;Kazancoglu & Aydin, 2018;Shi et al., 2020). The integration of channels is the primary explanation for the sophistication of retail patterns (Hure et al., 2017). Additionally, omnichannel buyers feel they have a greater understanding of the investment they have purchased than the salespeople do. They believe they have a greater degree of leverage over the sales encounter and are therefore more demanding (Rippe et al., 2015). Increased complexity of shopping behaviors, interaction in between channels as a brand experience, and an expected and consistent, and seamless shopping experience are the main characteristics of omnichannel experience (Hure et al.,

Multichannel Strategy
Omnichannel Strategy Perceived interaction with the brand.

Customers
No possibility of triggering interaction.
Can trigger full interaction.
Use channels in parallel. Use channels simultaneously.

Retailers
No possibility of controlling integration of all channels.
Control full integration of all channels.

Sales People
Do not adapt selling behavior.
Adapt selling behavior tailored to customer needs and knowledge.
2017). Shi et al. (2020) have also found that connectivity between channels, flexibility to switch between them and consistent experience is what made an omnichannel shopping experience.
Despite an increase in research on information and communication technology (ICT) in mul-tichannel settings, it is important to continue exploring omnichannel consumer conduct, as previous research has shown mixed results (Neslin et al., 2014;Verhoef et al., 2015;Juaneda-Ayensa et al., 2016;Hure et al., 2017). It is important to determine how consumers' views about technology influence their buying choices in novel situations (Escobar-Rodriguez & Carvajal-Trujilo, 2014).

Unified theory of technology acceptance and use in an omnichannel environment: Model and hypothesis
The approach of our study is based on Venkatesh and colleagues' extension of the Unified Theory of Acceptance and Usage of Technology (UTAUT2) model (2012). This model is used to attempt to define the variables that affect technology acceptance and use across a customer's omnichannel buying journey. Following a study of the literature, we chose UTAUT2 as the primary catalyst of omnichannel strategy because it explains market penetration and the use of technologies (Venkatesh et al., 2012). Additionally, this model has been used in previous research on the omnichannel environment (Juaneda-Ayensa et al., 2016;Susanto et al., 2019). Furthermore, Kazancoglu and Aydin (2018) discovered that UTAUT2 is the most powerful predictor of omnichannel purchasing intention. This theory informs our understanding of omnichannel consumers' attitudes toward technology and the aspects in which those attitudes influence their purchasing intentions in the shopping context (Juaneda-Ayensa et al., 2016;Susanto et al., 2019). According to UTAUT2, seven factors influence a consumer's choice to use information and communication technology: success objectives, effort perceptions, social effects, enticing circumstances, hedonic incentives, price worth, and habit. Previously published studies on omnichannel consumer behavior ignored facilitating criteria and price point, owing to the belief that omnichannel is freely available (Juaneda-Ayensa et al., 2016;Susanto et al., 2019). However, a recent report de-termined that these two variables are important in the context of omnichannel retailing, necessi-tating their inclusion in this study (Kazancolgu & Aydin, 2018).
As Venkatesh et al. (2012) indicate, UTAUT2's applicability should be checked through multiple domains and with additional variables, especially in the sense of customer behavior. As a consequence of previous studies, we used the variables personal innovativeness (Juaneda-Ayensa et al., 2016), perceived security danger (Kazancoglu & Aydin, 2018;Shi et al., 2020), and perceived compatibility (Shi et al., 2020) to get a deeper understanding of the degree to which the model's.
The term "performance anticipation" refers to the degree to which several outlets may be used simultaneously and/or technology assists customers when buying goods, in this case, food and beverages (Venkatesh et al., 2012). It has been shown that the anticipation of success is a strong indicator of behavioral intent (Juaneda-Ayensa et al., 2016;Susanto et al., 2019). As a consequence, we made the following hypotheses: H1: Planned success has a favorable impact on omnichannel buying intention.
Effort expectancy refers to the degree of convenience with which shoppers communicate with multiple touchpoints and platforms during the shopping journey (Juaneda-Ayensa et al., 2016). This factor is significant in both voluntary and mandatory usage contexts (Venkatesh et al., 2012) and it positively affects purchase intention. Thus, the following hypothesis was pro-posed: H2: Planned commitment has a favorable impact on omnichannel buying intention 1 st ICEMAC 2020 325 The extent to which consumers feel they can use omnichannel as a buying tool is referred to as social influence (family, near friends). This aspect has been interpreted as a direct predictor of behavioral purpose in previous customer behavior research using the theory of rational intervention (TRA) and theory of expected behavior (TPB) as subjective norms (Ajzen, 1980). Subjective standard or social impact is largely characterized as the way individuals believe others would see them as a consequence of their usage of a particular technology (Venkatesh et al., 2012), and it has a positive effect on purchasing intention. As a result, the following theory is proposed: H3: Social impact has a positive effect on the intention to buy through omnichannel The word "habit" applies to the degree to which individuals perform an activity without think-ing about it (Venkatesh et al., 2012). This definition was added to the UTAUT2 as a new structure derived from the previous UTAUT (Venkatesh et al., 2003) and is an indicator of technology us-age in some previous studies. When a human repeatedly engages in a particular behavior, the ac-tivity is likely to become automatic (Jasperson et al., 2005;Limayem et al, 2007). As a result, the following theory has been advanced: H4: Habit positively affects omnichannel purchase intention Though utilitarian motivation was included as part of the success expectancy component in the UTAUT model (Venkatesh et al., 2003), hedonic motivation was included as a separate construct in UTAUT2. The UTAUT2 model (Venkatesh et al., 2012) incorporates hedonic incentive, which has been found to play a significant role in assessing technological adoption and use (Brown & Venkatesh, 2005). Shopping appeal may be either hedonistic or utilitarian. Hedonic motives are characterized by adjectives such as pleasurable and enjoyable, whereas utilitarian motives are logical and task-oriented (Babin et al., 1994). Numerous studies conducted in the res-taurant industry have discovered that individuals order food, especially via online channels, for the enjoyment and fun associated with the practice (Alagoz & Hekimoglu, 2012;Yeo et al., 2017;Alalwan, 2020). Additionally, hedonic drive has been identified as a significant indicator of online food distribution applications in Indonesia (Prabowo & Nugroho, 2019). As a result, the following theory was advanced: H5: Hedonic motivation positively affects omnichannel purchase intention Facilitating conditions are described as the services and assistance available to customers to en-gage in conduct (Venkatesh et al., 2003). In the omnichannel sense, it can be described as if a channel encourages omnichannel shopping, thus improving the integration and seamlessness of the shopping journey (Kazancoglu & Aydin, 2018;Shi et al., 2020). As omnichannel customers desire an optimized and streamlined shopping experience that allows for fast channel switching, channel-related products and services can provide customers with versatility and choices in a variety of different areas (Shi et al., 2020). For instance, payment mechanism choices should be as diverse as possible to accommodate complexity (Shi et al., 2020), because if there is a flaw with the payment structure, it may be argued that it impedes the shopping journey (Kazancoglu & Aydin, 2018). Another example is product supply, where if there is an issue (whether it is a connection issue, a mistake, or inconsistencies), it may be said to impede the shopping trip (Kazancoglu & Aydin, 2018). As a result, the following theory was advanced: H6: Facilitating condition positively affects omnichannel purchase intention 326 Price value should be seen as a critical aspect, as it serves as the distinguishing distinction between a customer and an organizational environment. Price value can be described as the cognitive trade-off between the application's perceived advantages and the monetary expense of utilizing it (Venkatesh et al., 2012). It can be interpreted in the omnichannel sense as to whether shopping through the omnichannel provides monetary gain and value to the customer (Yeo et al., 2017). As a result, the following theory was advanced: H7: Price value positively affects purchase purpose through all platforms.

External variables used in UTAUT2 extension
Consumers have the opportunity to embrace or utilize emerging technologies or innovations as they come into touch with them. Previous research has shown that multichannel users have the tenacity to discover and use innovative platform alternatives (Rogers, 1995;Konus et al., 2008). Personal innovativeness is a measure of a person's willingness to experiment with new or different products and to seek out new experiences as a result (Midgley & Dowling, 1978). Market innovativeness is a predictor of ICT acceptance and purchasing intention in previous studies (Citrin et al., 2000), and has also been found to be a predictor in an omnichannel sense (Juaneda-Ayensa et al, 2016;Susanto et al., 2019). As a result, the following theory was advanced:

H8: Personal innovativeness positively affects omnichannel purchase intention
The term "perceived compatibility" applies to the extent to which an experience is perceived to be associated with the user's existing principles, interests, behaviors, and current and previous interactions (Aljabri & So-hail, 2012). When a buyer switches from one platform to another, their perceived compatibility with the new channel is critical in deciding their buying plan (Amaro & Duarte, 2015). When in the omnichannel sense, it is essential to test the compatibility of consumers' prior familiarity with particular shopping platforms (Shi et al., 2020). As a result, the following theory is advanced: H9: Perceived compatibility positively affects omnichannel purchase intention Danger perception has been identified as a highly significant element in the omnichannel buying experience (Kazancoglu & Aydin, 2018). Perceived danger can be characterized as the consumer perception that omnichannel shopping is risky in terms of protection (Shi et al., 2020). Additionally, Kazancoglu and Aydin (2018) discovered that customers view omnichannel as dangerous due to neither the likelihood of the mechanism collapsing nor financial risks (price inconsistencies). As a result, our research hypothesizes that if shoppers consider less of these dangers, they would see omnichannel shopping as more beneficial than detrimental. As such, the following theory was proposed: H10: Perceived risk negatively affects omnichannel purchase intention. 327

Research strategy
This study is a quantitative and conclusive study that aims to analyze the antecedents of technology adoption on omnichannel purchase intention. We designed an online questionnaire focused on omnichannel restaurant consumers and was administered online through Instagram, Twitter, and Facebook, to name a handful. An omnichannel customer is described for this study as a shopper who has made at least one transaction from an omnichannel restaurant through at least two distinct channels or touchpoints. In all, 495 respondents fit the criteria set by our defini-tion of omnichannel consumers, who have purchased at least using two channels in the last eight months prior to the collection of the data (October 2020).
To carry out our study, we defined omnichannel restaurants as restaurants that have both online and offline presence, and they must also be integrated into one, big system. Secondly, the restaurants must have an online delivery platform (in this case, Go-Food and Grab-Food), be-cause both are two of the most popular food delivery channels in Indonesia. We defined those restaurants as restaurants under the management of ISMAYA group and Mitra Adiperkasa (MAP) Group in Indonesia for several reasons. First and foremost, the restaurants under both groups are very well known in Indonesia, such as Starbucks Coffee, Burger King, Domino's Pizza, The Peo-ple's Café, Sushi Groove, etc. The second reason is being ISMAYA and MAP both have website and apps that shows information about the restaurants under them, allowing them to communicate information online directly to consumers. Third, both have membership programs that allow consumers to gain benefits from shopping in both restaurants. Both also have integrating features such as MAP Voucher (the ability to pay using this voucher in any of MAP's restaurants) and IS-MAYA's points and rewards able to be used in their restaurants.
The questionnaire was divided into two parts. The first section questioned respondents about their shopping experience at their most often visited omnichannel restaurants (table 2, attached below on appendix I, page 12). The respondents are made to read the definition and some examples of the omnichannel shopping experience, and They were then asked to score their degree of agreement with each object on a five-point Likert scale ranging from 1 to 5, (strongly disagree) (strongly agree). The second part of the questionnaire is used to ask for socio-demographic information, such as age, gender, education, monthly income, and frequency of shopping in a month (table 3). 328  Less than 1.000.000 20% 1.000.000-5.000.000 40,2% 5.001.000-10.000.000 23,8% 10.001.000-20.000.000 9,7% More than 20.000.000 6,3%

Frequency of Shopping (Monthly)
1 or 2 times 37% 3 to 5 times 41,8% 6 to 9 times 11,5% More than 10 times 9,7% Due to the uniqueness of the application area, the measurement scales were translated and modified to the Indonesian language, followed by a wording test to ensure that no misspellings or misunderstandings existed during the measurements of ten participants. To evaluate the results, we used IBM SPSS Statistics 25 to conduct exploratory factor analysis (EFA) on 30 respondents as a pre-test and then used LISREL 8.54 to conduct covariance-based structural equation model-ing (CB-SEM) of latent variables. We performed a confirmatory factor analysis (CFA) and evalu-ated the measurements in the measurement model prior to evaluating the hypothesis in the structural model using CB-SEM. A measurement model is used to determine the scale's validity and reliability in this analysis. As such, the composite reliability (CR) and Cronbach alpha's values reflect the reliability of the scales used in this study. A construct is said to be reliable if its CR val-ue is greater than 0,7 and its Cronbach alpha value is greater than 0,6. The loading factor of the calculation model and the KMO test, both of which had a minimum value of 0,5, also affirm the constructs' validity (Hair et al., 2006).

Measurement model
We launched an investigation confirmatory factor analysis (CFA) on the products and made some changes. Both products were checked to have a minimum standard loading factor of greater than 0.5. The item PI3 had a value lower than 0,5. We thus decided to exclude the item from the model to improve the model's validity (Hair et al., 2006).
It was also verified that all constructs had a value of CR> 0,7 and Cronbach alpha>0,6 which means that the reliability of the constructs was confirmed. The validity of the constructs was also confirmed, with all the constructs having a value of KMO test above 0,5. Furthermore, all items had a loading factor value greater than 0,5, which makes the measurements valid (table 4, attached below on appendix II, page 13).

Structural model
In the structural model, the significance level of the hypothesized relationships can be said to be significant with the value of t-value>1,645 or >-1,645, as the hypotheses are one-tailed. CB-SEM was performed by using LISREL 8.54. The structural model explains the intention to pur-chase in the omnichannel context well, with an R^2 value of 78% (table 5, attached below on Ap-pendix III,  page 14). This result validated the proposed model's predictive potential (Hair et al., 2006).
The significance, sign, and magnitude of the path coefficients are shown in table 5. From ten hypotheses, six were supported as their t-value are greater than>1,645 or >-1,645. The hypothesis that was supported was, by order of magnitude, are habit, facilitating conditions, personal innovativeness, hedonic motivation, performance expectancy, and social influence. The other four hypotheses, effort expectancy, price value, perceived compatibility, and perceived risk are not supported as their relationships were not significant.
The increasing complexity of consumer shopping behavior in the digital era has given birth to omnichannel retailing (Hure et al., 2017;Shi et al., 2020). Omnichannel consumers are a global phenomenon (Schlaager & Maas, 2013) and they expect seamless and integrated experience in their shopping journey. The omnichannel strategy can be defined as customer management where throughout the customer relationship, the shopper interacts with a brand through differ-ent devices and channels (such as a physical store, online channel, mobile channel, social media) thus making all touchpoints of the said brand must be integrated to provide a seamless and com-plete shopping experience (Juaneda-Ayensa et al., 2016). Thus it is important to investigate the field of omnichannel consumer behavior, as there are still mixed results in the previous studies (Neslin et al., 2014;Verhoef et al., 2015;Juaneda-Ayensa et al., 2016;Hure et al., 2017) 330 The best indicator of omnichannel buying intention was found to be a habit. This indicates that omnichannel consumers in Indonesia are very used to switching between different channels and shopping with them. This is following previous studies in the omnichannel context (Kazan-coglu and Aydin, 2018;Sun et al., 2020). Companies must take note that the omnichannel strategy is especially important as omnichannel consumers are everywhere and have already formed a habit. This factor will become more important in the coming years as more retailers adopt true omnichannel strategies (Juaneda-Ayensa et al., 2016).
This study has found facilitating conditions as an important factor for the adoption of technology in the omnichannel context, following Kazancoglu and Aydin (2018). This shows that the facilitating conditions when consumers are using the channel are very important, such as product availability, good connection to the internet. Technical problems such as an error in the payment system, the mismatch between product availability should be avoided for consumers to have a seamless and integrated shopping experience (Kazancoglu & Aydin, 2018;Park & Kim, 2020;Shi et al., 2020).
Personal innovativeness is a predictor of omnichannel purchase intention. This confirms and strenghthens the results of previous studies (Juaneda-Ayensa et al., 2016;Susanto et al., 2019). This result implies that individuals who are more innovative regarding ICT will have a stronger intention to purchase using omnichannel. Omnichannel consumers seek out new technology, and in turn new touchpoints and channels, to experiment with it and try it among their friends and families. As such, companies should try to introduce new features or re-introduce existing features of their channels and touchpoints to attract this kind of customer.
Following previous research, performance expectancy is found to be a significant factor in omnichannel purchase intention which has been confirmed in much previous literature (Ven-katesh et al., 2012;Verhoef et al., 2015;Juaneda-Ayensa et al., 2016;Kazancoglu & Aydin, 2018;Jo & Lee, 2019;Susanto et al., 2019). This also suggests that consumers will continue to buy using in the omnichannel environment when they perceive usefulness in doing so. While contrary to pre-vious findings, effort expectancy was not found to affect purchase intention. This could be at-tributable to the advance of ICT technology that has stabilized apps implementation, to the de-gree that consumers perceive little difficulty in using them .
Social influence was also found to affect omnichannel purchase intention. This is following previous theories such as TRA and TPB that normative factors affect behavioral intentions (Ajzen, 1991). Previous studies in the same context have also found similar results (Alagoz and Hekimoglu, 2012;Lee et al., 2019;Susanto et al., 2019). This implies that when consumers buy products, especially in the food and beverage industries, they are very affected by their friends and families. Companies should use this information to formulate strategies where it's easier or cheaper to buy in bulks in certain channels and touchpoints.
Hedonic motivation was also found to affect purchase intentions. This is also in accordance to previous studies (Alagoz & Hekimoglu, 2012;Venkatesh et al., 2012;Juaneda-Ayensa et al., 2016;Susanto et al., 2019). This also means that there are pleasure, fun, and enjoyment to be found in shopping using omnichannel. This could be attributed to the shopping experience of omnichannel consumers themselves, where they expect a seamless and holistic experience (Juaneda-Ayensa et al., 2016). This means that hedonic and utilitarian factors are part of the journey (Melero et al., 2015).
Price value perceived compatibility, and perceived risk are found to not affect purchase intention. In the case of price value, consumers do not perceive a price benefit because there are no differences in material benefits in between using different channels . Perceived compatibility was also found not significantly affect purchase intention. This also means that consumers shop using omnichannel regardless of their beliefs, values, and shopping preferences (Shi et al., 2020). Finally, the perceived risk did not influence omnichannel purchase intention. This means that consumers buy in the omnichannel context regardless of the need for security or risks 331 involved. This could also mean that the possibility of buying in an omnichannel context off-sets the risks perceived by consumers. Companies could introduce scenarios of touchpoints where consumers perceive the need for security in which retailers can use new technologies to manage consumers directly in the physical stores (Juaneda-Ayensa et al., 2016).

Conclusion
The primary goal of this study was to ascertain the factors that affect technology acceptance and use in the omnichannel context, as well as their impact on buying intention among omni-channel consumers in Indonesia, using Venkatesh et alUTAUT2's model (2012). The study's find-ings indicate that the UTAUT2 model is insufficient for predicting omnichannel shopping behav-ior and should be expanded to include additional variables. Kazancoglu and Aydin (2018) used two variables in this study: facilitating conditions and price value, as these two variables were omitted from the UTAUT2 model in an omnichannel sense. According to Kazancoglu and Aydin, enabling conditions are a critical factor in the implementation of technologies in an omnichannel setting (2018). While previous research (Juaneda-Ayensa et al., 2016;Susanto et al., 2019) omit-ted the element, this study demonstrates that promoting conditions should be investigated fur-ther as a factor influencing technology adoption and usage in an omnichannel setting. Additional-ly, this research makes a significant contribution by examining omnichannel shopping activity in the restaurant setting, an area where very few studies have been conducted, and none in Indone-sia. As such, the context provided in this study could serve as a foundation for potential studies on omnichannel behaviors.
The findings also have real consequences for Indonesian omnichannel managers, since it has been shown previously that shoppers have developed an affinity for omnichannel shopping and retailers must integrate the technique as quickly as possible. Omnichannel is one of the most effective management and marketing techniques for enhancing a critical aspect of their company, namely their consumers' streamlined and holistic shopping experience. Managers must be cognizant of the process by which such strategies are developed since they are responsible for thoroughly defining and analyzing the technology that suits their market approach and how the technology is adopted by their consumers (Juaneda-Ayensa et al., 2016). Restaurants, in particular, where normative factors such as personal inventiveness and hedonic drive have a beneficial effect on purchasing intention, must be diligent in incorporating novel and imaginative contact points and pursuing word-of-mouth marketing.
This study has certain limitations because our data is gathered in Indonesia and is limited to ISMAYA and MAP restaurant patronage. This can restrict the scope of results. Second, the survey included an overwhelming plurality of women (73,1 percent) between the ages of 17 and 25. (67,7 percent). Because this study used online questionnaires, which are more common among younger people, and older people are less comfortable with online testing. Additionally, the use of online questionnaires can result in selection bias. As a result, prospective studies could use a vari-ety of data collection techniques to minimize those prejudices. Additionally, our study suggests other avenues for potential research, such as examining the emerging presence of technology in particular networks, such as offline or physical shop channels. Additionally, sociodemographic considerations are not included in this study. Additionally, future studies could examine the im-portance of such variables in supplementing the current model. This analysis aims to investigate the current omnichannel customer behavior phenomena, as technology is driving the transfor-mation of retailing's future. As such, an integrated and holistic shopping experience is critical to riding the current wave of e-commerce successfully.