Determinants of Omnichannel Shopping Intention for Sporting Goods

Abstract

Omnichannel retailing is a revolutionary business strategy of recent years which allows customers to engage with retailers using multiple channels and touchpoints to make their shopping experiences better. The main purpose of the current research is to identify the determinants of omnichannel shopping intention for sporting goods. This research applied the UTAUT2 model to study the determinants of omnichannel shopping intention of 406 Indian respondents and tested the relationship using the structural equation model. Responses were collected from December 2021 to January 2022. The empirical result of the research shows the influence of performance expectancy, social influence, effort expectancy, hedonic motivation, habit, and perceived value on omnichannel shopping intention for sporting goods. Among the seven constructs, performance expectancy emerged as the major contributor, followed by hedonic motivation, habit, perceived value, effort expectancy, and social influence of the omnichannel shopping intention. This paper also presented the analysis of the moderating effect of gender and found that performance expectancy, habit, perceived compatibility, and hedonic motivation have significantly different effects on omnichannel shopping intention. These findings provide several important implications for both researchers and sporting goods retailers in developing marketing strategies.

1. Introduction

Retailing has undergone drastic changes in the last 25 years due to continuous developments of digital technologies along with rapid changes in consumer behaviors [1]. The COVID-19 pandemic had a big impact on the retail industry and consumers’ online and offline shopping experiences. As a result of the drastic increase seen in recent years in sales conducted through online retail channels, traditional retailers relying on brick-and-mortar channels faced a big challenge [2]. This has led traditional retailers to change their business model by introducing multiple channels to manage the changing customer lifestyles and their relationship with them [3,4]. Rapid advancements in technology have provided customers with multiple options for interacting with retailers [5]. Research in retail shopping shows a good number of customers use multiple touch points and channels during their shopping journeys like online or offline stores, mobile phone apps, social media platforms, and other online platforms [6]. The consumer may search for details related to the product in one channel and make a purchase in another channel which may cause a loss of customers to a retailer [4]. Omnichannel retailing emerged as a new integrated shopping approach that allows the customer to use multiple channels simultaneously to provide a good customer experience [7]. Research studies show that omnichannel customers spend more time than customers who use only one channel [8,9], while omnichannel stores indicate higher rates of incremental store visits [10]. One of the major issues in Omnichannel retailing is the increase in carbon emissions due to home delivery, particularly the “last-mile” transportation. It became necessary for omnichannel retailers to focus on delivering personal, engaging, and environmentally sustainable future.

The COVID-19 pandemic had a big impact on the global sporting goods industry. Online shopping for sporting goods witnessed a six-time increase after the COVID-19 pandemic, leading to brick-and-mortar retailers to make adjustments in their business models [11]. The way consumers discover, explore, purchase, and engage with traditional sporting goods retailers has changed due to the use of various devices, such as desktops, laptops, mobile phones, and tablets, in their shopping process [12]. Many sports retailers in India, like Decathlon, Adidas, and Puma, realized the need and adopted an omnichannel approach to ensure that they meet the changing consumer behaviors. The pandemic has made rapid changes in omnichannel shopping behavior [13]. Sporting goods refer to sporting equipment’s and apparel required for participation in sports like footwear, sports apparel, fitness equipment, athleisure, and goods used to prevent sports-related injuries. [12]. An increasing number of Indians are becoming health-conscious and fitness-focused due to a large number of cases of lifestyle diseases like atherosclerosis, stroke, diabetes, and heart diseases [12]. As a result, there is a rise in the number of gyms and fitness centers, which in turn is boosting the sales of sports and fitness goods in the country [12]. Industry report predicts that the Indian sports and fitness goods market is expected to touch US$ 6.054 million by 2024 [14]. India is currently ranked as the fourth-largest retail market in the world. Indian retail market is estimated to grow from US$0.793 trillion in 2020 to 1.5 trillion by 2030 [15]. It is predicted that India’s e-commerce sector will grow by 84% by the year 2024 [15].

Omnichannel retailing is characterized by consumers switching between channels like in-store, retailers’ websites, retailers’ mobile apps, and e-commerce websites [16]. Research on consumer behavior in omnichannel shopping has attracted great attention in recent times and was listed as top priorities research topic by Marketing Science Institute (MSI) through 2020–2022 [17]. The focus of most of the previous studies relating to omnichannel retailing is on retailers’ perspective that includes supply channel management and channel integration. However, studies relating to the understanding of consumer behavior are very few [18,19]. A fewer number of research studies were conducted to study the factors that influence consumers to buy from omnichannel retailers [20]. Most of the studies related to omnichannel consumer intention were conducted in mature and well-penetrated markets like the US, UK, China, and some European countries, and research conducted in emerging economies like India has been inadequate [5,21,22]. Most previous studies conducted relate to fashion goods, but less research has paid attention to the omnichannel consumer buying behavior of sporting goods [23]. The main purpose of this research is to identify the determinants of omnichannel shopping intention for sporting goods based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to resolve the gap in the literature.

The current research findings will contribute to the omnichannel literature as it focuses on identifying the determinants of intention to shop in an omnichannel retail store. This research intends to improve the theoretical understanding of the determinants influencing the consumer’s omnichannel shopping intention and empirically test it. This paper enables retailers to understand the consumer’s expectations from omnichannel shopping, which may help the delivery of a superior shopping experience. The basic objective is to provide insight to managers in getting an understanding of the role of compatibility and perceived risk in the adoption of omnichannel shopping. This article is organized as follows: the next section reviews the existing literature on consumer behavioral intention toward omnichannel retailing. The research framework and hypothesis of the research are presented in Section 3. Research methodology is the subject matter of Section 4. Section 5 presents the empirical findings of data analysis. Section 6 presents the discussion, and Section 7 provides the conclusion, theoretical contribution, managerial contribution, and the limitation of the research work with direction for future research.

2. Literature Review

2.1. Omnichannel Retailing and Shopping Methods

The rapid growth of technological advancement has led to the emergence of new channels that include retailers’ websites, mobile apps, social networking sites, and kiosks, empowering consumers with more information and choices than ever before [24]. Growth in the number of online stores and changes seen in consumer behavior have compelled retailers to introduce multiple channels for interaction with customers [25]. Channels used by retailers are either digital (e.g., websites, mobile apps, social media) or physical stores that interact with consumers during their shopping journey [26]. Every product purchase is viewed as a customer journey, starting with product discovery and leading to trial, purchase, delivery, or pick-up [27]. Omnichannel consumers use multiple channels and touchpoints of the retailers in their buying process [8]. Research shows that of the 30 possible multichannel journeys, the most common channel used for product discovery is online, and the physical store is the preferred channel for trial and purchase [27]. Research shows many consumers checks two or more channels before buying a product or service [28,29], and channel preferences vary by age, purchase category, and stage in the shopping journey [27]. An industry survey indicates that the consumer preference is to shop with retailers who operate both online and in a physical store [30]. A study by Verhoef et al. confirms that customers gather information in one channel before making their purchase in another [4].

Research findings reveal that multichannel retailing leads to cannibalization and free-riding behavior [31], which can be reduced by integrating the channels which encourage customers to purchase products in the same retail store. Omnichannel retailing is the practice by which a variety of channels and their touchpoints are seamlessly integrated and leveraged to influence customer purchase decisions [32,33,34]. An omnichannel retail store is fully integrated by sharing customer details, inventory data, and pricing across all channels to provide good customer experience. Omnichannel shopping is quickly becoming a new norm in retailing but not equally applied in all retailers. This retailing strategy was initially applied by the apparel and travel industries [34] and later adopted by other industries. Omnichannel shoppers are consumers who use more than one channel during their shopping journey [29]. Research shopping behavior is defined as searching for information relating to a product on one channel and then purchasing the product in another channel [18]. Types of onichannel shopping methods is presented in

Table 1. Major types of omnichannel shoppers.

Type of Omnichannel Shopping Methods	Description
Webrooming	Searching online for information before purchasing at a brick-and-mortar store.
Showrooming	Checking the product in a store and purchasing product online.
Buy online, pick-up in-store (BOPIS)	Buying online and then picking up at a store or kiosk.
Buy online while in store	Purchasing a product online while in the retailer’s store
Buy in-store, home delivery (BIHD)	Scanning a product in-store to find a better deal online.

. For example, omnichannel shoppers might research a product online (mobile app or website) but buy the product in a physical store. This type of shopping behavior is referred to as webrooming. Checking the product in a physical store and purchasing the product online is referred to as showrooming. Two other popular methods of omnichannel shopping are buying online and picking up the product in a physical store or buying online. The research report of “ivend retail” reveals that 90% of shoppers combine digital and physical channels on the path to purchase [35]

Omnichannel retailing is based on information technology platforms as consumers check the product using various online and offline channels. Technology is the key to delivering a unified experience across all channels and providing a good shopping experience [36]. Omnichannel shoppers value in-store technology or mobile apps when they shop in a physical store as it makes the shopping process convenient [35]. They also use personal devices like mobile devices and desktops for interaction while shopping in-store and outside the shop. Research shows that half of online shoppers globally have used their mobile phones when they research a product inside the physical store, and 27% have purchased a product from their mobile phones while inside the physical store [37]. Research conducted by Mimoun et al. reveals the positive influence of in-store retail technology on performance and effort expectancy [38]. These findings clearly show that technology is driving new shopping behavior.

2.2. Omnichannel Consumer Behavior and Shopping Intention

A growing number of customers, during their shopping journey, use multiple channels, which are referred to as omnichannel shoppers [39]. Juaneda-Syensa et al. [29] defined an omnichannel consumer as one who shops for a product or service using more than one channel. Cortiñas et al. [40] defined omnichannel customers are those who have visited two or more channels of the store but made a purchase in any one channel [40]. Cortiñas et al. refer to monochannel customers as those visiting one channel (i.e., mobile, website, or physical) and making their purchase in the same channel they have visited [40]. Earlier studies attempted to profile the shopper’s inclination toward the omnichannel retail store. Wang et al.’s research show the influence of channel characteristics on the channel attitudes of shoppers using multiple channels [41]. Verhoef et al. research findings found research shoppers gather information in one channel but make purchases in another channel [4]. Few researchers have made a study of customer purchase intention to buy sporting goods in both offline (e.g., Eunju et al. [42]) and online (e.g., Chiu et al. [23]) retail stores. Research findings of Lee [43] indicate customers show a higher preference for brick-and-mortar shopping than online stores when purchasing sporting equipment. Research finding from a survey conducted in Europe shows that 73% of the respondent are shopping for sportswear in physical stores, and 27% are shopping online [44]. Research on consumers’ perspective of omnichannel retailing remains limited and sporadic [19,45]. Chiu, Kim, and Won’s research findings indicate that attitude, subjective norm, and emotion have a positive effect on consumers’ intention to purchase sporting goods online using a model of goal-directed behavior (MGB) [23]. Kang used the Engel–Kollat–Blackwell (EKB) Model to study omnichannel intention to buy [9] (Table 2).

The concept of omnichannel retailing has attracted increasing interest in both research and practice [1]. Several theoretical models have been applied to get an understanding of the factors that influence consumer behavior. Despite the high number of research using technology adoption models available, it is important to continue the research in the field of omnichannel consumer behavior to determine the consumers intention to use in the new context [29]. Gunawan, Losaura, and Ahmad studied the factors determining the intention to buy fashion products from the omnichannel store in Jakarta using UTAUT 2 model [46]. The research finding shows that performance expectancy, effort expectancy, social influence, facilitating conduction, hedonic motivation, habit, personal innovativeness, and perceived security partially influence the intention to use omnichannel stores. Kim et al. studied the antecedents of a specific omnichannel shopping behavior using the UTATUT2 model. Their research finding shows the effect of hedonic motivation and social influence on behavioral intentions in omnichannel choice [20]. Ketzenberg and Akturk analyzed 49 million online and in-store transactions of an American retail chain and found that an omnichannel strategy like the BOPIS service led to 4.7% of online sales [47]. Their research also found that the main reason consumers used BOPIS was due to the lack of shipping costs.

Table 2. Review of Omnichannel Shopping Intention Studies.

Authors (Year)	Theories/ Model	Independent Variables	Dependent Variable	Type of Retail Store	Country
Juaneda-Ayensa, Mosquera, and Murillo (2016) [29]	UTAUT2	PE, EE, SI, Habit, HM, Personal Innovativeness, Perceived Security	Omnichannel Shopping Intention	Fashion Retailer	Spain
Chiu, Kim, and Won (2018) [23]	MGB	Attitude, Subjective Norm, Emotion	Purchasing Sporting Goods Online	Sporting Goods	Korea
Xu and Jackson (2018) [48]	TPB	Perceived Behavioral Control, Risk, and Price Advantage	Channel Selection Intention	General	US and UK
Fuente (2019) [31]	UTAUT2	PE, EE, SI, HH, HM, PV, FC, PI, and PS	Omnichannel Purchase Intention	Fashion Retailer	Spain
Silva, Martins, and Sousa (2019) [49]	TAM	Risk, Cost, Compatibility, Usefulness, Ease of Use.	Future Use Intention and Actual Use		Portugal
Kang (2019) [9]	EKB	Perceived Value Personality	Omnichannel Shopping Intention	Fashion Retailer	US
Truong (2020) [18]	-	Showrooming, Webrooming, Perceived Compatibility, Perceived Risk	Omnichannel Shopping Intention	Fashion Retailer	Vietnam
Kim, Han, Jang, and Shin (2020) [20]	UTAUT2	PE, EE, SI, HH, HM	Intention to Buy-online-pick-up-in-store	Department Stores and Fashion Retailers	Korea

Note: performance expectancy (PE), effort expectancy (EE), social influence (SI), habit (HH), hedonic motivation (HM), facilitating condition (FC), perceived value (PV), perceived compatibility (PR), and perceived risk (PR).

Table 2. Review of Omnichannel Shopping Intention Studies.

Authors (Year)	Theories/ Model	Independent Variables	Dependent Variable	Type of Retail Store	Country
Juaneda-Ayensa, Mosquera, and Murillo (2016) [29]	UTAUT2	PE, EE, SI, Habit, HM, Personal Innovativeness, Perceived Security	Omnichannel Shopping Intention	Fashion Retailer	Spain
Chiu, Kim, and Won (2018) [23]	MGB	Attitude, Subjective Norm, Emotion	Purchasing Sporting Goods Online	Sporting Goods	Korea
Xu and Jackson (2018) [48]	TPB	Perceived Behavioral Control, Risk, and Price Advantage	Channel Selection Intention	General	US and UK
Fuente (2019) [31]	UTAUT2	PE, EE, SI, HH, HM, PV, FC, PI, and PS	Omnichannel Purchase Intention	Fashion Retailer	Spain
Silva, Martins, and Sousa (2019) [49]	TAM	Risk, Cost, Compatibility, Usefulness, Ease of Use.	Future Use Intention and Actual Use		Portugal
Kang (2019) [9]	EKB	Perceived Value Personality	Omnichannel Shopping Intention	Fashion Retailer	US
Truong (2020) [18]	-	Showrooming, Webrooming, Perceived Compatibility, Perceived Risk	Omnichannel Shopping Intention	Fashion Retailer	Vietnam
Kim, Han, Jang, and Shin (2020) [20]	UTAUT2	PE, EE, SI, HH, HM	Intention to Buy-online-pick-up-in-store	Department Stores and Fashion Retailers	Korea

Note: performance expectancy (PE), effort expectancy (EE), social influence (SI), habit (HH), hedonic motivation (HM), facilitating condition (FC), perceived value (PV), perceived compatibility (PR), and perceived risk (PR).

3. Research Framework and Hypotheses

The research framework for this study was developed to explore the factors that drive the adoption of omnichannel shopping intention, illustrated in Figure 1. The research framework adopted for this current research is based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model of Venkatesh et al. [50]. Omnichannel shopping intention behavior is based on consumer capability of using several technologies [49], and the UTAUT2 model will be an appropriate model as it is widely applied to study the drivers of technology acceptance and use during their shopping journey. The Unified Theory of Acceptance and Use of Technology (UTAUT) proposed by Venkatesh et al. has been used in getting an understanding of acceptance of information technology and user behavior. UTAUT was developed by integrating core elements of eight models, i.e., the Theory of Reasoned Action (TRA), Social Cognitive Theory (SCT), Technology Acceptance Model (TAM), Motivational Model (MM), Theory of Planned Behavior (TPB), Combined TAM-TPB, Model of PC Utilization (MPCU), and Innovation Diffusion Theory (IDT) [51]. UTAUT2 model has provided an explanation for 77 percent of the variance in behavioral intention to use technology and 52 percent of the variance in technology use [50]. Based on the UTAUT2 model, the factors that drive the consumers’ intention to use omnichannel retail stores are affected by seven factors: performance expectancy (PE), effort expectancy (EE), social influence (SI), habit (HH), hedonic motivation (HM), facilitating condition (FC), and perceived value (PV). According to Venkatesh et al., the UTAUT2 model needs the addition of other relevant factors when it is applied to different contexts [52].

3.1. Performance Expectancy

Performance expectancy is defined as the level of benefit that consumers receive from the use of certain services or technology for the achievement of related tasks [52]. In the context of omnichannel shopping, performance expectancy is related to the consumer’s perception of one’s ability to use multiple channels during one’s single shopping trip can help in the successful accomplishment of shopping tasks [53]. Previous research found performance expectancy as one of the major factors influencing the behavioral intention of mobile commerce [54]. Few research studies on omnichannel shopping intention also show that performance expectancy is the most important factor that affects the intention to buy in omnichannel fashion retail stores [20,29,46]. Based on the previous studies, the following hypothesis is proposed:

H1: 

Performance expectancy positively influences omnichannel shopping intention.

3.2. Effort Expectancy

In the omnichannel retailing environment, consumers select different channels, which include online, mobile, and in-store channels. Effort expectancy for omnichannel retailing is how customers believe that shopping through retailers’ multiple channels can help them complete shopping easily and efficiently. Effort expectancy can be defined as the level of ease associated with consumers’ use of different channels during their shopping process for sporting goods [52]. This was found to be a significant factor in attitudes toward RFID-enabled retail services [55]. Effort expectancy was found to be the key determinant of omnichannel shopping intention [29]. Based on the previous studies, the following hypothesis is proposed:

H2: 

Effort expectancy positively influences omnichannel shopping intention.

3.3. Social Influence

Social influence is the extent to which consumers perceive that people who are important to consumers believe it is good to buy sporting goods using different channels [31]. Venkatesh et al. defined social influence as others’ view of the individual using new technology [52]. Chimborazo-Azogue et al.’s research reveals that social influence has an effect on mobile showrooming intention [56]. Based on the previous studies, the following hypothesis is proposed:

H3: 

Social influence positively influences omnichannel shopping intention.

3.4. Facilitating Conditions

The perception related to the resources and support available for the performance of a behavior is defined as the facilitating condition [52]. In the context of omnichannel retailing, users need to have certain resources, such as internet connection speed, and skills, such as using a desktop, smartphone, and in-store technology. Previous research findings show that facilitating conditions strongly influence the acceptance of some specific technology-based retail services to buy online pick-up in-store (BOPIS). Based on the earlier studies, the following hypothesis is proposed:

H4: 

Facilitating Conditions positively influence omnichannel shopping intention.

3.5. Hedonic Motivation

Hedonic motivation is a pleasant feeling caused by using technology products, services, and applications [52]. Previous research findings show the importance of hedonic motivation during a customer’s shopping journey [57]. Researchers have identified hedonic motivation as the strongest predictor of behavioral intention to use offline, online, and mobile [58] shopping channels. Kim et al.’s research reveals that hedonic motivation affects the intention to buy online pick-up in-store services [20]. Based on the previous studies, the following hypothesis is proposed:

H5: 

Hedonic motivation positively influences omnichannel shopping intention.

3.6. Habit

Habit is defined as the extent to which people tend to perform repetitive behavior as a result of learning [52,59]. Venkatesh et al. have suggested that experience does provide habits of varying degrees for a user’s intention to use technology. Habit was found to be an antecedent of behavioral intention in previous studies [60]. Based on the previous studies, the following hypothesis is proposed:

H6: 

Habit positively influences omnichannel shopping intention.

3.7. Perceived Value

Prior studies indicate perceived value as one of the major constructs in influencing users’ intention to buy in various contexts [32,59]. Many researchers have conducted empirical validation of the influence of perceived value on users’ intention to buy in various commercial contexts, such as shopping malls [61], online shopping [62], mobile shopping [63], and omnichannel shopping [8,9]. Ju-Young’s research finding reveals that consumers who perceive greater value in checking various channels during their purchase process show a higher intention to shop using omnichannel methods [9]. Based on the previous studies, the following hypothesis is proposed:

H7: 

Perceived value positively influences omnichannel shopping intention.

3.8. Moderating Effect of Gender

Venkatesh et al., in their research paper, have confirmed gender having moderating effects on the UTAUT2 construct’s effect on consumers’ behavioral intention [52]. The relationship between gender and shopping intention is widely studied by researchers, but very few studies have been conducted on omnichannel shopping intention. Mosquera et al.’s [36] study conducted in Spain shows that in-store technology was the strongest predictor of purchase intention for men. Based on the previous studies, the following hypothesis is proposed.

H8: 

Gender moderates the relations among all constructs of the proposed model and omnichannel shopping intention for sporting goods.

4. Research Methodology

4.1. Participants and Data Collection Procedure

The objective of this research is to identify the determinants of omnichannel shopping intention for sporting goods in India. Email invitation was sent randomly to selected sample of 3000 consumers. This study is about purchasing sporting goods, so at the beginning of the questionnaire, filtering questions were added to ensure that respondents spent minimum of 1 or more hours on sporting activities per week. A total of 423 survey responses were obtained. After elimination of incomplete responses, a total of 406 valid responses were used for further analysis. All the 406 respondents spent at least one hour on sporting activities per week. Detailed information related to the study participants is presented in

Table 3. Characteristics of respondents.

Demographic Variable	Category	Responses	Percentage
Gender	Male	187	46.1
	Female	219	53.9
Age	Under 18 years	2	0.5
	18–25 years	289	71.2
	26–33 years	72	17.7
	34–41 years	32	7.9
	42 and Above years	11	2.7
Occupation	Student	262	64.5
	Employee	106	26.1
	Self Employed	31	7.6
	Homemaker	2	0.5
	Unemployed	5	1.2
Omnichannel Retail Store Buying Frequency	Never	50	12.3
	Rarely	107	26.4
	Sometimes	151	37.2
	Often	24	5.9
	Always	74	18.2

Source: Authors Calculation.

. The online survey was collected during the period from December 2021 to January 2022.

4.2. Instrument

The survey instrument of this study aims at the measurement of the different variables related to omnichannel shopping intention and the details of the respondents. All measures were adopted from the previous studies concerning the UTAUT2 model. All items used in this study have used a five-point Likert scale that ranges from 1—strongly disagree to 5—strongly agree and was developed based on the previous literature. The details of the measures adopted for this study are presented in Appendix A.

4.3. Data Analysis Tools and Techniques

Structural equation modeling (SEM) was conducted using IBM SPSS 28.0 and IBM Amos 28.0 for the identification of the determinants of omnichannel shopping intention. SEM is a multivariate technique that allows simultaneous estimation of multiple equations, regression analysis, and path model analysis [64]. It is composed of the measurement model and the structural mode. The measurement model measures the laten variables or composite variables, while the structural model tests all the hypotheses based on the path analysis [65]. The hypotheses were tested with a sample of 406 using SPSS 28.0 and Amos 28.0. The moderating effects of gender were analyzed using multigroup analysis, as gender is a categorical variable.

5. Data Analysis

5.1. Respondent’s Profile

The data was collected using an online questionnaire. The research collected data from various sources online sources to improve the representativeness of the sample. Out of 406 collected responses, 46.1% were male, whereas 53.9% were female. The survey respondents were dominated by the 18–25 age group, which made up 71.2% of the total sample size. 87.7% of respondents had purchased sporting goods in an omnichannel retail store. In addition, 24.1% mentioned that they often or always bought sporting goods in the omnichannel approach. Details relating to the respondent’s profile are presented in Table 3.

5.2. Measurement Model

The preliminary data analysis was conducted to check for any missing data, data accuracy, presence of outliers, and normality. Out of 423 collected responses from an online survey, 17 questionnaires were eliminated due to incomplete responses. The response of the missing value was removed, and 406 usable responses were selected for further analysis. The descriptive analysis findings showed no missing values, outliers, or invalid values. Skewness values of the survey items ranged from 0.099 to −0.588, which lies within the 1.00 cut-off suggested by Kline [65]. The kurtosis statistics of the survey items ranged from 0.682 and −683, which was found to be lower than suggested cut-off suggested by Byrne [66]. Harman’s single factor test was also conducted, and the result showed that the total variance value for a single factor accounted for 39.97, i.e., less than 50%. Thus, the study did not violate the common method bias.

Internal consistency, convergent validity, and discriminant validity of the model were examined using IBM Amos 28. Confirmatory factor analysis was conducted using structural equation modeling that used the maximum likelihood estimation method [64,66]. The value for convergent validity was estimated based on the standardized loading of each item variable on their respective latent construct variable [67]. Data analysis showed factor loadings of all the items were found to be higher than the acceptable level > 0.70 and have t-value > 1.96 [64] except for one item indicating acceptable level reliability. Item FC4 was removed based on the acceptable level of the factor loading, and AVE improved slightly to 0.529. The model confirmed that the indicators converge with the assigned factors. The internal consistency of the items was checked using Cronbach’s alpha, with all the items showing a value above 0.7 except one item. This confirms the internal consistency of the scale item as it was found above 0.70 [68]. Details of reliability and validity are presented in

Table 4. Construct reliability and validity.

Constructs	Items Code	Factor Loading	Cronbach’s Alpha	CR	AVE	MSV
Performance Expectancy (PE)	PE1	0.772		0.882	0.599	0.555
	PE2	0.759
	PE3	0.827	0.880
	PE4	0.878
	PE5	0.725
Effort Expectancy (EE)	EE1	0.749	0.862	0.862	0.611	0.555
	EE2	0.743
	EE3	0.832
	EE4	0.799
Social Influence (SI)	SI1	0.846		0.855	0.663	0.433
	SI2	0.819	0.852
	SI3	0.776
Facilitating Conditions (FC)	FC1	0.759		0.769	0.529	0.507
	FC2	0.797	0.783
	FC3	0.612
Hedonic Motivation (HM)	HM1	0.750	0.864	0.865	0.616	0.495
	HM2	0.797
	HM3	0.795
	HM4	0.796
Habit (HH)	HH1	0.842	0.901	0.901	0.694	0.498
	HH2	0.842
	HH3	0.855
	HH4	0.793
Perceived Value (PV)	PV1	0.814	0.843	0.844	0.644	0.494
	PV2	0.831
	PV3	0.761
Omnichannel Shopping Intention (OSI)	OSI 1	0.794	0.903	0.904	0.702	0.498
	OSI 2	0.839
	OSI 3	0.879
	OSI 4	0.838

Source: Authors Calculation.

.

The analysis of discriminant validity was analyzed using the Fornell–Larcker test is presented in

Table 5. Discriminant validity and correlation matrix.

	PE	EE	SI	FC	HM	HH	PV	BI
PE	0.772
EE	0.740 ***	0.782
SI	0.636 ***	0.637 ***	0.814
FC	0.611 ***	0.712 ***	0.438 ***	0.727
HM	0.657 ***	0.588 ***	0.592 ***	0.508 ***	0.785
HH	0.533 ***	0.534 ***	0.626 ***	0.416 ***	0.656 ***	0.835
PV	0.662 ***	0.665 ***	0.615 ***	0.624 ***	0.618 ***	0.629 ***	0.803
BI	0.702 ***	0.639 ***	0.638 ***	0.464 ***	0.693 ***	0.661 ***	0.673 ***	0.838

Note: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating condition (FC), hedonic motivation (HM), habit (HH), perceived value (PV), and perceived risk (PR); *** p < 0.001; Average variance extracted on diagonal. Source: Authors Calculation.

. The recommended value for AVE (average variance extracted) is greater than 0.5, and it should be more than other correlation values seen among the latent variables [69]. Table 3 shows the square root of PE is 0.769, which is larger than the correlations in the column of PE and larger than any of those in the row of PE. Similar results were seen for other constructs, indicating the discriminant validity as well established.

Confirmatory factor analysis (CFA) was conducted for all 30 items involving eight constructs used in the research. CFA analysis results showed a good fit with the chi-square of the measurement model is 547.771 with Df = 378 and with X2/Df = 1.461. The comparative fit index (CFI) = 0.977, Tucker–Lewis index (TLI) = 0.974, IFI = 0.978, and Root-mean-square error of approximation (RMSEA) with 0.034.

Table 6. Model fit indices.

Fit Indices	Estimates	Recommended Level of Acceptance
Probability level	0.001	<0.05
X2/d.f. Ratio	1.461	CMIN/DF < 3
CFI	0.977	>0.90
TLI	0.974	>0.90
IFI	0.978	>0.90
RMSEA	0.034	<0.05

Source: Authors Calculation.

presents the results of CFA and Hu, and Bentler [70] recommended level of acceptance.

5.3. Structural Model and Path Coefficient Analysis

The structural model fit indices were assessed before conducting the hypothesis testing. The assessment of the structural model for hypothesis testing was performed as the proposed model exhibited a satisfactory level of fit. For the structural model, the chi-square value is significant with p-values of 0.001. All the fit indices were found to match the recommended threshold level, providing evidence of a good fit of the model to the data. This research involves the investigation of seven sets of specific relationships of determinants with omnichannel shopping intention. Specifically, six hypothesized paths were suggested to be positively related and one negatively related to omnichannel shopping intention. The PE, EE, SI, HM, HH, and PV were found to strongly influence shopping intention. However, the research finding shows that facilitating condition was not positively related to omnichannel shopping intention.

The analysis of the structural relationships confirmed six hypotheses out of seven proposed hypotheses. Figure 2 and

Table 7. Results of the path analysis.

Hypothesis	Model Path	Std. Coefficients	p	Comment
Hypothesis 1	PE → OSI	0.246	0.001	Supported
Hypothesis 2	EE → OSI	0.143	0.029	Supported
Hypothesis 3	SI → OSI	0.089	0.023	Supported
Hypothesis 4	FC → OSI	−0.074	0.137	Not Supported
Hypothesis 5	HM → OSI	0.214	0.001	Supported
Hypothesis 6	HH → OSI	0.170	0.001	Supported
Hypothesis 7	PV → OSI	0.175	0.018	Supported

Source: Authors Calculation.

present the results from the structural model. The path coefficient analysis represents the strength and direction of the relationship between the constructs. The results show performance expectancy having a strong impact on omnichannel shopping intention ($\mathsf{\beta }1=0.246), \mathrm{thus} \mathrm{supporting} \mathrm{H}1$. The path estimates findings suggest a positive influence of effort expectancy on omnichannel shopping intention ($\mathsf{\beta }=0.143), \mathrm{thus} \mathrm{supporting} \mathrm{H}2$. The path estimates findings suggest that social influence positively influences omnichannel shopping intention ($\mathsf{\beta }=0.089), \mathrm{thus} \mathrm{supporting} \mathrm{H}3$. The analysis also reveals facilitating conduction as positively associated with omnichannel shopping intention with $\mathsf{\beta }=0.074, \mathrm{and} p \mathrm{value} \mathrm{higher} \mathrm{than} 0.05, \mathrm{thus} \mathrm{not} \mathrm{supporting} \mathrm{H}4$. Hedonic motivation ($\mathsf{\beta }=0.214)$, habit ($\mathsf{\beta }=0.170)$, and perceived value ($\mathsf{\beta }=0.175)$ also have a strong influence on the omnichannel shopping intention for sporting goods. Details of the results of testing the hypothesis are presented in Table 7 and Figure 2.

5.4. Moderating Effect of Gender

To examine the moderating effects of gender, a multigroup analysis was conducted to compare the effect on omnichannel shopping intention. The findings of this research have indicated two variables, performance expectancy, and habit, as having statistically significant differences between men and women. The multigroup analysis finding revealed that the effect of performance expectancy of females ($\mathsf{\beta }=0.287)$ is higher effect than males ($\mathsf{\beta }=0.227)$ on omnichannel shopping intention. Similarly Hedonic motivation of female ($\mathsf{\beta }=0.344)$ show higher effect than male ($\mathsf{\beta }=-0.067)$ on omnichannel shopping intention. However, when it comes to habit, males ($\mathsf{\beta }=0.230)$ showed a higher effect than females ($\mathsf{\beta }=0.134)$ on omnichannel shopping intention. The social influence of males ($\mathsf{\beta }=0.330)$ show a higher effect than females ($\mathsf{\beta }=-0.062)$ on omnichannel shopping intention (

Table 8. Moderating effects of gender.

Model Path	Male		Female
	Estimate	p	Estimate	p
PE → OSI	0.227	0.001	0.287	0.001
EE → OSI	0.137	0.017	0.046	0.367
SI → OSI	0.330	0.001	−0.062	0.494
FC → OSI	−0.069	0.392	−0.048	0.438
HM → OSI	0.067	0.372	0.344	0.001
HH → OSI	0.230	0.001	0.134	0.005
PV → OSI	0.132	0.059	0.171	0.019

Source: Authors Calculation.

).

6. Discussion

The main objective of this research work is to identify the determinants of omnichannel shopping intention for sporting goods. This study attempts to identify the determinants of omnichannel shopping intention based on the Venkatesh et al. extended Unified Theory of Acceptance and Use of Technology (UTAUT2). The finding of this research reveals most of the variables (Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Habit, and Perceived Value) as having an influence on omnichannel shopping intention for sporting goods. This is in line with the previous research conducted on omnichannel shopping behavioral intention by Gunawan et al. [46] and Kim et al. [20]. This research finding shows that the UTAUT2 model holds good predictive power and is able to explain the omnichannel shopping intention.

The research finding shows that performance expectancy is the most important determinant in explaining the omnichannel shopping intention, and this is in line with previous research findings (e.g., [20,29,53,71]). This result demonstrates the customer using different channels perceived in the accomplishment of their shopping tasks like checking for better prices online or being able to conveniently shop sporting goods. Hedonic motivation is the second strongest determinant and positively influences omnichannel shopping intention. Findings from the research reveal the positive effect of hedonic motivation on omnichannel shopping intention, matching with the findings of Silva et al. [49], Shi et al. [19], Kim et al. [20], and Truong et al. [18]. This shows that consumers enjoy when shopping in omnichannel retail stores. The research findings match the previous research findings of Kim et al. [20]. Findings also show that hedonic motivation has a higher effect on the intention to shop in omnichannel outlets for female consumers.

Habit is the third strongest determinant, in this current study, which influences omnichannel shopping intention. This could be because customers are used to using different channels, especially after COVID-19. The research findings match the previous research findings of Tamilmani et al. [60]. Hedonic motivation has been found to be one of the determinants which influence omnichannel shopping intention.

Research findings reveal social influence on omnichannel shopping intention, which shows the opinions, suggestions, and recommendations of others are considered important for omnichannel shoppers. This shows omnichannel shopping intention is conditioned by other people’s opinions. The research findings match the findings of Chimborazo-Azogue et al. [56]. Results show facilitating conditions as not important for omnichannel shopping intention as respondents mostly belonged to the 18–25 years of age group where they may be familiar with using the technology. These findings match with the few previous findings on omnichannel shopping intentions [56]. The finding from this research shows that perceived value positively affects omnichannel shopping intention. This finding matches the previous research findings, and much of the literature shows that perceived value is one of the factors which motivate customers’ intention [18]. This is in line with the previous research findings of Truong [18], which show that customers value the benefits derived through omnichannel shopping. This shows that consumers who perceive greater value in shopping in the omnichannel retail store show a higher intention to shop using the channel.

This study also studied the moderating effects of gender using multigroup analysis. The findings show that there is a statistical difference between men and women in omnichannel shopping intention related to performance expectancy, habit, and perceived compatibility. Performance expectancy is found to be higher for females than males, which reveals that women exhibit higher expectancy when it comes to omnichannel shopping. The hedonic motivation was also found to be higher for females than males, which shows that women consider omnichannel shopping as fun. The research findings show that the UTAUT2 model can be applied to omnichannel shopping intention.

7. Conclusions, Contribution, and Future Research

7.1. Conclusions

Omnichannel retailing is a revolutionary business strategy seen in recent years which allows customers to engage with retailers using multiple channels to make their shopping experiences better. This research has applied the UTAUT2 model to a sample of 406 respondents and tested the relationship using the structural equation model. All measures adopted were from the previous studies concerning the UTAUT2 model and modified to fit the context of purchasing sporting goods through an omnichannel retailer. The empirical result of the research shows that performance expectancy, social influence, effort expectancy, hedonic motivation, habit, and perceived value have an influence on omnichannel shopping intention for sporting goods. Among the seven constructs, performance expectancy emerged as the major contributor (followed by hedonic motivation, perceived value, and habit) influences the omnichannel shopping intention. This research has also covered the analysis of the moderating effect of gender and found that performance expectancy and habit have a strong influence on omnichannel shopping intention. The hedonic motivation was found to have a stronger influence on females than on male shoppers. Social influence was found to have a strong influence on males than on female shoppers.

7.2. Theoretical Contribution

Less of the literature has paid attention to the omnichannel shopping intention of sporting goods. The main purpose of this research is to address the gap in the literature by investigating omnichannel shopping intention using the UTAUT2 model. This research contributes to theory by presenting and empirically testing a conceptual framework identifying the factors that influence consumer intention to shop from omnichannel. This is one of the few studies in the world and the first study in India to empirically identify the determinants of omnichannel shopping intention for sporting goods. The second major theoretical contribution of this research was to validate the UTAUT2 model in the context of omnichannel shopping intention. This finding is consistent with the research findings of Silva et al. [49]. The third theoretical contribution is to study the moderation effect of gender, and the results show that omnichannel shopping behavior is significantly moderated by gender.

7.3. Managerial Contribution

The findings from this research have several important managerial implications for sporting goods retailers. Findings show that motivation to engage in omnichannel retail shopping includes both utilitarian and hedonic factors. The present research finding reveals that perceived expectancy is the major determinant of intention to shop in omnichannel for sporting goods. It means that omnichannel benefits like convenience in searching for information and shopping are major motivators for consumers to use omnichannel retail outlets. Current research findings also show that hedonic motivation is one of the determinants which drive omnichannel shopping intention. This shows that consumers enjoy using technology when shopping for sporting goods. Female consumers were found to exhibit a higher effect than male consumers related to hedonic motivation. Retailers need to provide a good in-store experience to attract more visitors to the store. The omnichannel retail strategy needs to include in-store digital touchpoints using the latest digital touchpoints like kiosks and smart shelf technologies to increase consumers’ experience. One of the primary reasons for showrooming is to identify better prices. Retailers should encourage showrooming as it can drive consumers to visit the stores, and retail needs to set prices close to the online price.

This research also shows that compatibility is one of the important determinants of omnichannel shopping intention. Interestingly, this research found that compatibility is lower for female consumers. The retailers should make the services compatible with consumers’ previous experiences and need to give support to women consumers. Perceived risk is found to be one of the determinants that affect omnichannel intention. Retailers can reduce the financial risks of consumers by introducing a cash-on-delivery method (COD) and giving the option of returning the product if it does not meet their expectations. The retailers can also accept these returns according to the channel of preference; for example, customers who have to purchase the product online can return it in a physical store nearby. Effort expectancy is found to be one of the determinants of omnichannel shopping intention, so retailers need to make sure the website, mobile app, and in-store technology are easy to use, and they should complement the physical store experience. Lastly, findings indicate the presence of gender differences among consumers in omnichannel shopping intention. Omnichannel retailers can propose marketing strategies to improve the perceived compatibility of female consumers to increase their adoption of omnichannel shopping. They also need to be given assistance to help them use in-store technology.

7.4. Limitations and Future Research

One of the limitations of the study is its focus on sporting goods retailers and the sample limited to India. The sporting goods include a broad range of goods like footwear, apparel, accessories, and equipment. Future research can be conducted on one category of sporting goods as the buying behavior may vary across each category. The second main limitation is related to the data collection process using convenience sampling, as it does not allow us to generalize these results to the entire population. Future research suggested using randomized sample procedures through various online and offline data collection methods. The third limitation of this study is that the sample was collected using an online survey, which may lead to a bias toward internet users. Future research can replicate the study using a different demographic group, cross-cultural setting, and different product category to understand the omnichannel shopping intention. This study focused on omnichannel shopping intention; future research can focus on examining customer satisfaction and customer loyalty. Future research also needs to examine the actual omnichannel shopping behavior.