Local Food Shopping: Factors Affecting Users’ Behavioural E-Loyalty

Abstract

While most research on electronic commerce has focused on customer behaviour according to websites’ functional tasks, consumers are influenced by many other factors such as website content and design, especially in online food shopping. This is the first study that aims to examine which variables best explain satisfaction and behavioural e-loyalty (to return to the website and purchase) regarding online local food shopping. It empirically tested a model with a local food e-commerce website using a sample of 305 real e-buyers. The partial least squares structural equation modeling (PLS-SEM) technique was used to estimate the structural relationships. The findings revealed that all the tasks of a website could be strategically designed to enhance users’ loyalty, and stressed the importance of measuring how all website features jointly influence perceived flow and control. This study makes a significant contribution to the consumer literature that deals with local food websites, a subject which is currently under-researched, and the eventual impact on behavioural e-loyalty.

1. Introduction

Online grocery shopping is expected to grow worldwide, although those websites account for only a small proportion of the e-commerce market at the present time (Grunert and Ramus 2004; Heng et al. 2018). Despite the several advantages of grocery online shopping, such as the ability to find products, compare prices, save time, shop for ready-to-eat or semi-ready-to-eat food and arrange delivery at a suitable time, among other benefits, there are still many consumers who have not yet adopted online grocery shopping as a regular habit (Hansen 2008; Quevedo-Silva et al. 2016). The demand for speciality foods is also increasing (Canavan et al. 2007). At the same time, the slow food movement is expanding in response to the modern world’s eating habits (Lee et al. 2015; Heng et al. 2018).

In this changing food culture, consumers are progressively demanding more information regarding the food they eat, such as additional details about the components, the origin and the production chain (Ilbery et al. 2006; Megicks et al. 2012; Pearson et al. 2011; Seyfang 2008). These growing concerns are predictable outcomes of the competitive global food marketplace, where customers have become more specialized (Zepeda and Li 2006). In this quest, consumers are searching for alternatives in relation not only to the product offering, but also to the shopping experience and to the current dominant supermarket food supply chain (Pearson et al. 2011).

Buyers’ main reasons for shopping for local food products are related to economic, social, environmental, and health issues. Specifically, it has been pointed out that these buyers have a high and positive perception of: (1) the quality, freshness, taste, and authenticity of these products; (2) the supply chain that it entails and hence, the support that it offers to local community development; and (3) the environmental, social, safety, and sustainable benefits of the production chain (Pearson et al. 2011; Sims 2009). Whereas some years ago the shopping experience was considered a functional and utilitarian activity, it has proved to have an emotional and entertaining importance (Megicks et al. 2012).

In the last two decades, the expansion of local food retailers and networks from food cooperatives, farm shops, and ecological stores to supermarket chains has been considerable in response to the trend of local food consumption and supermarkets’ strategies regarding corporate social responsibility (Megicks et al. 2012; Tobler et al. 2011). The study of local food consumption has recently become a popular subject of research, where scholars have analysed consumer behaviour, environmentally responsible buying, and sustainable policy buying (e.g., Blake et al. 2010; Megicks et al. 2012; Pearson et al. 2011; Zepeda and Deal 2009).

Although there are many advantages of developing local food websites for the advancement of the supply chain, such as promoting food self-sufficiency, decreasing the environmental footprint and re-engaging consumers with the origin of their food (Pearson et al. 2011), consumers’ behaviour regarding these websites remains under-researched. Research that focuses not only on local food customers’ behaviour regarding the websites’ functional tasks but also on the website design and content is even sparser.

Despite the fact that local food can be purchased from many different stores, it seems that frequent buyers tend to be those that live in nearby rural areas (Pearson et al. 2011). There is therefore an opportunity to examine buyers’ behavioural e-loyalty regarding local food websites as the ability to find these products decreases. It is not only an issue of purely academic interest but also a possible contribution to food supply management practices.

This study serves as a first step toward the development of a model that can be used by future scholars and practitioners to gain knowledge regarding loyal local food consumers. This research provides insight into this area by addressing the relationships among website evaluation (WE) (which comprises aesthetics, content, customization, ease of use, and information quality), personal variables (perceived flow, perceived control), and relational variables (satisfaction and behavioural e-loyalty).

Table 1. References conducted in relation to the navigation experience.

Reference	Variables of the Navigation Experience	Outcomes of the Navigation Experience
Koufaris (2002)	Ease of use, perceived usefulness, entertainment, control, concentration	Purchase intentions
Lee and Lin (2005)	Website design, viability, reactivity, customization	Perceived quality, satisfaction, purchase intentions
Hausman and Siekpe (2009)	Entertainment, utility, information and content	Purchase intentions, revisit intentions
Constantinides et al. (2010)	Usability, interactivity, aesthetics, marketing mix and trust	Website selection
Manganari et al. (2011)	Ease of use	Trust, satisfaction
Rose et al. (2012)	Ease of use, aesthetics, perceived benefits, connectivity, customization, ability, challenge, interaction speed, immersion	Satisfaction, trust, purchase intention
Hsu et al. (2012)	Website quality	Satisfaction, purchase intention
Ha and Stoel (2012)	Privacy and security, content and functionality, customer service, atmosphere	Satisfaction, purchase intention
Hsu et al. (2012)	Utility and perceived ease of use	Satisfaction, loyalty
Pappas et al. (2014)	Customization	Purchase intentions
Pallud and Straub (2014)	Content, made for the medium, ease of use, promotion, emotion, aesthetics, subjective norms, attitudes, facilitating conditions	Intentions to return to the website, intentions to go to the museum
Bilgihan et al. (2016)	Easiness to locate the website or app, ease of use, perceived usefulness, hedonic and utilitarian features, perceived enjoyment, personalization, social interactions and multi-device compatibility	Brand engagement, positive word of mouth (WOM), and repeat purchase

shows prior studies conducted in relation to the navigation experience. A partial least squares path modeling (partial least squares structural equation modeling or PLS-SEM) was used to analyse the hypotheses. Figure 1 presents the proposed model for this research.

2. Literature Review

2.1. Relational Variables in Local Food E-Commerce

Loyalty is economically vital in e-commerce because attracting new customers is more expensive in online businesses than in brick-and-mortar stores (Chang et al. 2014; Luarn and Lin 2003). It has been defined as a bidimensional construct that entails the result of all marketing efforts to maintain existing customers (Pereira et al. 2016). Therefore, developing long-lasting relationships in the e-commerce context can be reflected in intentions to return to the website (Ku and Chen 2014) and purchase and repurchase intentions (San-Martín and Herrero 2012).

Intentions to return to the website denote continuance purposes, which is especially reinforced by positive interactions with the website (Huang et al. 2014). It has been stated that the success of e-commerce business relies more on users’ continued usage intention rather than on their initial adoption (Chou et al. 2010; Kabadayi and Gupta 2011). Purchase intention is a dimension of behavioural intentions (Zeithaml et al. 1996), and it has been argued that it is the best predictor of action (Dedeke 2016). Specifically, consumers usually purchase and repurchase a product or service that can maximize their benefits, which is probably influenced by their satisfaction, their shopping enjoyment, and their desire to continue their exciting shopping experience (Atulkar and Kesari 2017).

Customer satisfaction is based on all cumulative experiences with a certain company that can lead to pleasure or disappointment, and it is not a result of a specific transaction (Atulkar and Kesari 2017; Chang et al. 2014; San-Martín et al. 2012; Filieri et al. 2015). In the online context, satisfaction refers to a favourable navigation experience and the perception of a well-designed website, representing also a basic key element for a successful e-commerce business relationship since it increases rate profitability and long-term sales growth in online shops (Chen et al. 2012; Pereira et al. 2016). Nevertheless, e-commerce is characterized by the absence of the physical interaction with people that leads to users’ dissatisfaction due to the impersonality of the transaction (Pereira et al. 2016). In this paradigm, it is very complex to generate loyalty among users.

Several studies have analysed the effects of satisfaction on continuance intentions (Bhattacherjee 2001; Chiu et al. 2007; Zhao and Lu 2012). Ku and Chen (2014) have corroborated that satisfaction positively influences intentions to continue using the particular website. Chung et al. (2015) have also confirmed the positive impact of satisfaction on continued usage intentions. As Kabadayi and Gupta (2011) pointed out, a satisfactory perception normally results in a positive attitude toward the website.

It has been proved that individual satisfaction has a positive impact on repurchase intention (Mohamed et al. 2014). Specifically, prior studies have confirmed that satisfaction is favourably related to purchase intention (Lin and Lekhawipat 2014; Pee et al. 2018; Wen et al. 2011; Yen and Lu 2008). Hence, it has been proved that satisfied customers tend to repurchase more than dissatisfied consumers (Sánchez-García et al. 2012). In addition, it has been proved in the online context that satisfaction positively and significantly influences behavioural e-loyalty regarding search engines (Sirdeshmukh et al. 2018), about tourism e-commerce (Gonçalves et al. 2016), among female online shoppers (Chou et al. 2015), regarding luxury brands (Yoo and Park 2016), among others. Thus, it is reasonable to think that users of online local food websites that feel satisfied with the navigation experience tend to return to the website and increase their purchase intentions. Therefore,

Hypothesis 1 (H1).

The e-buyer’s satisfaction positively influences their behavioural e-loyalty.

2.2. The Impact of Perceived Flow and Control

Flow has been studied as a psychological factor that describes users’ state of involvement regarding the activity of web surfing (Bilgihan 2016; Hsu et al. 2012). The creation of a positive experience for a user within a website relies on the capacity of a website to induce the consumer to feel engaged during the interaction (Ali 2016; Hoffman and Novak 1996). This state of flow can take place during the search for information of products or during other kind of utilitarian tasks that ensue in a shopping online website (Mathwick and Rigdon 2004). Hence, if using online local e-commerce can induce a state of flow in users, they should be satisfied, and predisposed to purchase and continue visiting these websites.

It has been assumed that the state of flow generates diverse positive responses among users (Mathwick and Rigdon 2004), not only emotional but also behavioural (Lee and Jeong 2012). In this regard, Hausman and Siekpe (2009) revealed that perceived flow has a positive impact on intentions to return to the website and purchase intentions. In addition, Ilsever et al. (2007) concluded that a flow experience had a positive effect on behavioural e-loyalty, understood as intentions to return to the website and repurchase. Furthermore, O’Cass and Carlson (2010) indicated that users’ website-induced flow on professional sporting team websites positively influenced their satisfaction. This is in line with the conclusions indicated by Hsu et al. (2012) regarding travel agency websites, where users’ perceived flow had a positive effect on their satisfaction. Based on the preceding discussion, users that have a favourable perception of flow tend to increase their behavioural loyalty and to be satisfied. Therefore,

Hypothesis 2 (H2).

The e-buyer’s perception of flow positively influences their behavioural e-loyalty.

Hypothesis 3 (H3).

The e-buyer’s perception of flow positively influences their satisfaction.

In addition, there is another variable referring to personal skills related to information and communication technologies (ICTs), that is, perceived control. In interactive technology, perceived control has been defined as the extent to which users feel that they have the skills to manage their actions while shopping on an online website (Mohd-Any et al. 2015).

Control has been determined as a predictor of consumers’ satisfaction (Duman and Mattila 2005), as it reduces anxiety and enhances customers’ positive emotions (Hui and Bateson 1991). Perceived control seems to be essential for online shoppers because they might be searching for more control during the service process (Smith and Bolton 2002). Results of some studies suggested that people behave more positively when they believe they have control over the environment (Ozkara et al. 2017). Hence, it is reasonable to think that if users have a perception of control, their satisfaction levels will rise. Therefore,

Hypothesis 4 (H4).

The e-buyer’s perception of control positively influences their satisfaction.

2.3. The Impact of Website Evaluation on Perceived Flow and Control

In this study, the WE is a variable that comprises several concepts (aesthetics, content, customization, ease of use, and information quality), which other authors have considered (Hausman and Siekpe 2009; Lee and Lin 2005; Pallud and Straub 2014; Rose et al. 2012; Wolfinbarger and Gilly 2003). They are all signals of website quality.

Content and ease of use refer to the task-oriented quality of a product or a service. Content is related to textual and visual information and its adjustment to the needs of the core audience, and ease of use denote users’ perceptions for mainly navigating and searching information (Pallud and Straub 2014; Rose et al. 2012; Venkatesh 2000). Aesthetics is a quality signal more associated with originality and innovativeness of the website design that provides sensory stimuli and supports the development of experience feelings (Eroglu et al. 2003; Pallud and Straub 2014). Customization has been considered one of the most interesting advantages of online shopping over physical stores, because it makes it easier for users to personalize their experiences according to their preferences (Manganari et al. 2009; Wolfinbarger and Gilly 2003), by attending to customers individually and customizing the website appearance and functionality (Lee and Lin 2005; Rose et al. 2012). Information quality has also been considered as another advantage of e-commerce because users can have access to broader, richer and more updated information than in physical stores (Wolfinbarger and Gilly 2003). Customer service in e-commerce is related to the extent to which users perceive that it is easy, quick and cheap to contact the company, and that their questions are answered promptly (Liu and Arnett 2000; Hsu et al. 2012).

It has been stated that hedonic and utilitarian features of a website, as is the case with the variables included in the WE, positively impact flow (Bilgihan et al. 2015). Wu et al. (2016) recognized the positive impact of web skills on flow experience. Specifically, information quality has been identified as a factor that contributes to customers’ positive perceptions of websites (Chen et al. 1999), and it directly and positively influences flow (Hausman and Siekpe 2009). Taking into account the above discussion, this study proposes that all of the characteristics included in the global construct WE positively affect the sensation of absorption and enjoyment implicit in flow.

Websites allow users to build a sense of personal control, which is directly influenced by ease of use and customization (Rose et al. 2012). Moreover, the inclusion of a higher number of interactive elements on a website can increase the users’ sense of control (Hoffman and Novak 1996). Furthermore, the online shopping websites that provide users with clear and simple ways to contact customer service are more likely to encourage users, giving them perceived control with a greater sense of managing the situation (Hoffman and Novak 1996). Therefore,

Hypothesis 5 (H5).

WE positively influences an e-buyer’s perception of flow.

Hypothesis 6 (H6).

WE positively influences an e-buyer’s perception of control.

3. Research Methodology

3.1. Sampling Procedure and Data Collection

A non-probabilistic sampling technique was adopted, namely the convenience sampling technique, because it is a very useful method to identify real online purchasers and it allows for a high level of response rate (Kim and Li 2009). The online questionnaire was placed on an online survey website for approximately 44 days. The survey data were collected from 18 February to 4 April 2016. All participants were requested to participate by an email that explained the objectives of the research and included a link to the questionnaire, which was available in three languages (namely, Spanish, German and English). A total of 305 usable questionnaires from real e-buyers was obtained, which is a higher sample than in other studies conducted in similar contexts and procedures (Rose et al. 2012). The demographic details of the sample are shown in

Table 2. Sample profile.

	n	%		n	%
Gender			Number of visits to the website
Female	175	57	1–5 visits	254	83
Male	130	43	>5 visits	51	17
Age			Time spent on the website
18–25	8	3	0–5 min	105	34
26–30	42	14	6–10 min	108	35
31–35	115	38	11–15 min	56	19
36–40	38	12	>16 min	36	12
41–45	30	10	How often do you buy online?
46–50	25	8	1–5 times per year	98	32
51–55	23	7.5	6–10 times per year	77	25
56–60	14	4.5	>10 times per year	111	36
61–65	7	2	Never	19	6
66–69	2	1	Household monthly income (in euros)
>70	1	0	<900	41	13
Education			901–1200	38	13
Postgraduate	90	29.5	1201–1500	44	15
Graduate	158	52	1501–2000	74	24
Undergraduate	30	10	2001–2000	46	15
Secondary	26	8.5	3001–4000	25	8
Primary	1	0	>4000	37	12
Occupation
Employed	231	76
Student	5	2
Unemployed	16	4
Housewife	9	3
Other	42	14
Retired	2	1

. Respondents were mainly European (287 real e-buyers; 94%, specifically from Austria, Belgium, Bulgaria, Croatia, France, Germany, Greece, Holland, Italy, Macedonia, Netherlands, Norway, Portugal, Romania, Serbia, Spain, Sweden, Switzerland, Turkey, Ukraine and United Kingdom) and non-European (namely, from Argentina, Benin, Bolivia, Chile, Costa Rica, Ivory Coast, Mexico, Tanzania, United Arab Emirates, USA and Vietnam).

3.2. Measurement of Constructs

The scale items used in this research were adapted from previous studies and rated according to a seven-point Likert scale (see

Table 3. Measurement model.

Factor	Description	Mean	Standard Deviation
Aesthetics (AE)
1.	I find that the design of this website looks pleasant.	6.072	1.050
2.	The layout of this website is fascinating.	6.020	1.107
3.	I find the design of this website to be creative.	5.797	1.211
4.	I find that the design of this website looks aesthetic.	5.970	1.138
Content (CO)
1.	This website offers content that is relevant to the core audience.	5.708	1.221
2.	... uses media appropriately and effectively to communicate the content.	5.702	1.288
3.	... provides the appropriate breadth and depth of content.	5.567	1.271
4.	... provides current and timely information.	5.708	1.189
Customization (CU)
1.	This website makes me feel they are talking to me personally as a customer.	5.275	1.431
2.	The requirement to login to this shopping website makes me feel recognized as a customer.	5.266	1.538
3.	It is important to me that this shopping website feels like my personal area when I use it.	5.439	1.510
4.	I like that I am able to customize this shopping website to my own liking.	5.580	1.438
Ease of use (EO)
1.	This website offers clear and understandable goals.	6.007	1.043
2.	… is well-structured and organized.	5.964	1.111
3.	… provides clear and understandable results and feedback regarding your progress.	5.787	1.091
4.	... allows me to easily shop for what I want.	6.010	1.070
5.	It is easy to become confident at this website shopping.	5.751	1.258
6.	Learning how to navigate through this website has not taken too long for me.	6.246	1.044
Information quality (IQ)
1.	The website produces the most current information.	5.767	1.166
2.	... provides me with all the information I need.	5.623	1.335
3.	The information provided by the website is accurate.	5.770	1.212
4.	In general, the website provides me with high-quality information.	5.728	1.331
Intention to return to the website (IR)
1.	I will revisit this website next time I need.	5.587	1.576
2.	It is worth returning to this website again.	5.695	1.449
3.	I am likely to return to this website next time I need.	5.590	1.547
4.	I am encouraged to revisit this website next time I need.	5.587	1.539
Perceived control (PC)
1.	I feel in control of what I am doing when I purchase from this website.	5.810	1.178
2.	I can easily control the information that is provided on this website.	5.721	1.141
3.	I feel I can control my use of information on this website.	5.698	1.146
4.	The level of information provided by this website helps me to feel in control of my purchase decision.	5.757	1.217
Perceived flow (PF)
1.	When I navigate in this website, I felt totally captivated.	5.466	1.333
2.	When I navigate in this website, time seemed to pass very quickly.	5.318	1.444
3.	When I visit this website, nothing seemed to matter to me.	4.475	1.845
Purchase intention (PI)
1.	It is likely that next year I will transact with this website.	4.928	1.820
2.	Given the chance, I intend to use this website.	5.213	1.715
3.	Given the chance, I predict that next year I should use this website.	5.157	1.776
Satisfaction (SA)
1.	Overall, I was satisfied with this online commerce.	5.662	1.288
2.	The online site information content met my needs.	5.495	1.389
3.	It was easy to buy the product I chose.	5.954	1.167
4.	I was satisfied with online buying when compared to offline buying.	5.557	1.420

). The WE was operationalized using the first-order dimensions: aesthetics, content, customization, ease of use, and information quality developed by Pallud and Straub (2014), Rose et al. (2012), and Hsu et al. (2012). Perceived control was adapted from Rose et al. (2012), and perceived flow was measured following Hsu et al. (2012). Satisfaction was adapted from Kim et al. (2011). First-order dimensions for behavioural e-loyalty (intention to return to the website and purchase intention) were adapted from Hsu et al. (2012) and Huang et al. (2014).

The translation of the original version of the questionnaire from English to Spanish and to German received special attention. Native Spanish and German speakers ensured the translation so that all feasible nuances and connotations could be considered. Then, native Spanish and German speakers translated the scale items from Spanish to English and from German to English following the specifications of several scholars (Sireci et al. 2006). Finally, all translators evaluated the scale items in order to resolve any discrepancies.

3.3. Reliability and Validity

In order to estimate the proposed model (see Figure 2), variance-based structural equation modeling was used, also known as partial least squares structural equation modelling (PLS-SEM). This method was particularly suitable for this research because the model was a combination of first- and second-order constructs for which a covariance-based structural equation modeling would have required a higher sample size (Hair et al. 2012). Preliminary tests completed on the sample indicated the presence of non-normal data, and PLS-SEM is less strict with this type of bias (Hair et al. 2014).

Table 3 and

Table 4. Reliability and Convergent Validity of the Final Measurement Model.

Factor	Indicator	Standardized Loading	t-Value (Bootstrap)	CA	rho_A	CR	AVE
Aesthetics	AE1	0.896	58,910	0.918	0.920	0.942	0.803
	AE2	0.907	51,329
	AE3	0.892	63,188
	AE4	0.890	45,549
Content	CO1	0.878	62,306	0.913	0.914	0.939	0.793
	CO2	0.892	57,361
	CO3	0.888	57,888
	CO4	0.904	69,183
Customization	CU1	0.869	48,781	0.896	0.902	0.927	0.761
	CU2	0.904	66,494
	CU3	0.891	49,864
	CU4	0.825	29,581
Ease of use	EO1	0.774	27,960	0.877	0.884	0.907	0.621
	EO2	0.834	39,925
	EO3	0.818	35,686
	EO4	0.779	27,734
	EO5	0.818	37,089
	EO6	0.695	14,603
Information quality	IQ1	0.861	43,953	0.926	0.930	0.948	0.820
	IQ2	0.928	88,819
	IQ3	0.917	54,849
	IQ4	0.914	63,480
Intention to return to the website	IR1	0.952	114,008	0.966	0.967	0.975	0.908
	IR2	0.928	44,796
	IR3	0.973	182,997
	IR4	0.958	78,820
Perceived control	PC1	0.913	80,785	0.938	0.939	0.956	0.844
	PC2	0.930	91,755
	PC3	0.920	89,280
	PC4	0.912	71,321
Perceived flow	PF1	0.909	74,597	0.893	0.897	0.933	0.824
	PF2	0.932	106,859
	PF3	0.881	50,650
Purchase intention	PI1	0.951	105,532	0.962	0.963	0.976	0.930
	PI2	0.970	177,271
	PI3	0.972	190,928
Satisfaction	SA1	0.925	82,634	0.927	0.934	0.948	0.822
	SA2	0.927	102,152
	SA3	0.840	28,827
	SA4	0.931	114,582
Website evaluation	Aesthetics	0.790	24,756	0.892	0.894	0.920	0.698
	Content	0.848	40,512
	Customization	0.833	40,261
	Ease of use	0.867	52,389
	Information quality	0.837	37,856
Behavioural e-loyalty	Intention to return to the website	0.915	61,930	0.831	0.838	0.922	0.855
	Purchase intention	0.934	102,260

Note: All loadings are significant at p < 0.01 level. CA = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted.

present the findings of the measurement model reliability and convergent validity test. Cronbach’s alpha values correspond to the recommendation of 0.60 (Hair et al. 2010). Composite reliability denotes the shared variance among a set of observed items measuring a construct (Fornell and Larcker 1981), where the value of at least 0.60 is considered desirable (Bagozzi and Yi 1988). This was respected for every factor. Moreover, average variance extracted (AVE) for each construct was greater than 0.50 (Fornell and Larcker 1981).

In relation to convergent validity, all items were significantly (p < 0.01) associated to their hypothesized factors, and standardized loadings were higher than 0.60 (Bagozzi and Yi 1988). The discriminant validity of measures was analysed, proving that the shared variance between the pairs of constructs was lower than the corresponding AVE (Fornell and Larcker 1981) (see

Table 5. Measurement Model Discriminant Validity for Higher-Order Constructs.

Factor		F1	F2	F3	F4	F5
F1.	Perceived control	0.919	0.805	0.897	0.838	0.665
F2.	Perceived flow	0.740	0.908	0.890	0.830	0.742
F3.	Satisfaction	0.835	0.816	0.906	0.900	0.781
F4.	Website Evaluation	0.769	0.745	0.820	0.835	0.700
F5.	Behavioural e-loyalty	0.590	0.644	0.691	0.603	0.924

Note: Diagonal values are AVE square root, values below the diagonal are latent variable correlation values, and those above the diagonal are heterotrait-monotrait (HTMT) ratios.

). The heterotrait-monotrait (HTMT) ratio method recently proposed by Henseler et al. (2015) was also applied to test the discriminant validity, and all ratios were less than 0.90 (Hair et al. 2017; Teo et al. 2008). Therefore, all the measures in this research provided enough evidence of reliability, convergent and discriminant validity. Reliability and convergent validity were tested both at the first- and second-order level for the two second-order constructs of the model.

4. Research Findings

The results of the inner estimation for the model proposed are presented in

Table 6. Hypotheses Testing.

Hypothesis		Standardized Beta	t-Value (Bootstrap)
H1	Satisfaction → Behavioural e-loyalty	0.497	6266
H2	Perceived flow → Behavioural e-loyalty	0.238	3161
H3	Perceived flow → Satisfaction	0.437	8306
H4	Perceived control → Satisfaction	0.512	9454
H5	Website Evaluation → Perceived flow	0.745	27,411
H6	Website Evaluation → Perceived control	0.769	28,474

Note: All loadings are significant at p < 0.01 level. R² (perceived control) = 0.590; R² (perceived flow) = 0.553; R² (satisfaction) = 0.783; R² (behavioural e-loyalty) = 0.493. Q² (perceived control) = 0.468; Q² (perceived flow) = 0.430; Q² (satisfaction) = 0.606; Q² (behavioural e-loyalty) = 0.404.

. To establish parameters significance, bootstrapping with individual sign changes of 5000 samples was calculated (Hair et al. 2012). The endogenous latent variable satisfaction presented a R² higher than 0.67 and can be described as substantial. The R² of perceived control, perceived flow and behavioural e-loyalty can be described as moderate because the values were higher than 0.33 (Chin 1998). Positive Stone-Geisser’s Q² were obtained using blindfolding, and therefore the predictive relevance of the model was established (Henseler et al. 2009). In addition, the goodness of model fit was assessed (Henseler et al. 2014; Henseler et al. 2016), and the standardized root mean square residual (SRMR) presented a value of 0.047 (Hu and Bentler 1999).

Table 6 presents the results of the hypotheses testing. As hypothesized, e-buyer satisfaction has a significant effect on their behavioural e-loyalty (H1: β = 0.497; p < 0.01). E-buyer perception of flow has a positive impact on their behavioural e-loyalty (H2; β = 0.238; p < 0.01) and their satisfaction (H3; β = 0.437; p < 0.01). E-buyer perception of control positively influences their satisfaction (H4; β = 0.512; p < 0.01). WE positively influences e-buyer perception of flow (H5; β = 0.745; p < 0.01) and e-buyer perception of control (H6; β = 0.769; p < 0.01). These results are presented in Figure 3.

In addition, the significance of indirect effects was assessed. Only one indirect effect was found, presented in

Table 7. Mediation effect testing.

Paths	Standardized Beta	t-Value (Bootstrap)	VAF	Partial/Full Mediation
Perceived control → Satisfaction → Behavioural e-loyalty	0.547	6786	0.926	Full

Note: All loadings are significant at p < 0.01 level.

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These statistical results offered two major findings: (1) e-buyer satisfaction influences their behavioural e-loyalty; and (2) the construct WE has a positive effect on customers’ personal perception of flow and control.

5. Discussion and Implication

Today’s consumers use information and communication technologies (ICTs) for a large number of tasks, such as obtaining information and purchasing goods and services (Alcántara-Pilar et al. 2017). Literature has mainly focused on revealing users’ behaviour outcomes when web surfing by analysing websites’ functional features. This article initiated the research regarding the influence of online local food shopping on behavioural e-loyalty. The value and usefulness of this application was empirically tested by means of a quantitative research (using PLS-SEM) that explored the perceptions of real e-buyers from a Spanish local food e-commerce website.

This paper highlighted academic and practical contributions, as well as limitations. First, it offered a valuable foundation for understanding online local food e-commerce development by considering two loyalty interrelated concepts: intentions to return to the website and purchase intentions. Secondly, it incorporated website evaluation as a global dimension that included different quality signals: aesthetics, content, customization, ease of use, and information quality. It was observed that the main functional elements of an e-commerce website were related and interacted within the customers’ behavioural outcomes.

6. Conclusions

6.1. Academic Contributions

The findings of this research make several significant contributions to the literature on food supply management, culinary consumers’ behaviour, and e-commerce management. First, this study is the first to analyse the impact of local food shopping websites on users’ loyalty. All the tasks of a website can be strategically designed to enhance users’ intentions.

Second, the study results support the finding that users’ satisfaction is an important relational predictor of behavioural e-loyalty (Chung et al. 2015; Ku and Chen 2014; Lin and Lekhawipat 2014; Mohamed et al. 2014; Wen et al. 2011; Zhao and Lu 2012). Third, the findings confirm previous results concerning the positive effect of perceived flow on the two relational variables of the proposed model (satisfaction and behavioural e-loyalty) (Hausman and Siekpe 2009; Hsu et al. 2012; O’Cass and Carlson 2010). Fourth, this research reveals there is a positive relationship between perceived control and satisfaction in the local food e-commerce context.

Fifth, this study shows the significance of measuring several features (the WE dimension) in order to understand users’ perceptions of the website layout, and how all these features jointly influence e-buyer perceived flow and control. With respect to the above, most research has analysed separately the WE constructs without taking into account the overall website analysis of features and its impacts, and the global future intentions of users after an e-commerce experience. In this regard, past studies have determined the positive effect of certain website features on perceived flow (Bilgihan et al. 2015; Hausman and Siekpe 2009; Wu et al. 2016) and control (Hoffman and Novak 1996; Rose et al. 2012).

6.2. Practical Implications

The findings of this research support the current local food trend (e.g., Ilbery et al. 2006; Megicks et al. 2012; Pearson et al. 2011; Seyfang 2008; Tobler et al. 2011; Zepeda and Li 2006), suggesting that these consumers can meet their needs regarding this type of product and enhance their shopping activity as regular e-buyers. In this regard, many of these consumers complain about the restricted availability of certain products, the lack of information about where to buy the local products and the high costs due to the long supply chain (Pearson et al. 2011). Moreover, consumers compare the perceived values attributed to the other available alternatives (Pedraja and Yagüe 2004). These issues can be improved by offering additional information concerning the moment that seasonal or non-seasonal products will again be for sale (the information can even be dated and give the main reasons for the product scarcity), the supply management chain, the system of price regulation, and to what extent the product supports the sustainable community development. This information can fill consumers’ demands for traditional and local food, which is perceived as authentic as well as safe, distinctive, and traceable (Sims 2009).

Another practical implication is for website designers so that they can improve users’ perceptions and behavioural intentions. For example, photos that emphasize the uniqueness and attractiveness of local food have the potential to enhance not only users’ willingness to purchase those products but also their intentions to return to those websites. Additionally, the information provided can be enriched using storytelling strategies based on local aspects.

Furthermore, local food is a very appealing market for tourists and, in this regard, Khanal et al. (2014) suggest that it could also very interesting for the Spanish government to invest and promote the links between the food sector and the tourism industry in order to improve the general economy. Moreover, these websites can promote the first visit to a destination as well as the continuance of local food consumption once the tourists return home.

6.3. Limitations and Future Research

This research had some limitations, which offer interesting avenues for future studies. First, this research was limited by the use of convenience sampling. The current study involved approaching users of a specific local food e-commerce. Second, in the proposed model, the WE and behavioural e-loyalty were global constructs that comprised related concepts in order to gain parsimony and understand their relations with many other variables. However, the separate effects of the proposed model were not measured.

Future research should test the model by using a probabilistic sampling method in order to improve the generalizability of the findings. Scholars could also approach various local food shopping online websites to obtain different insights into the effect of local food e-commerce on behavioural e-loyalty, and test the influence of local food brands on consumers’ perceived value (Rubio et al. 2014). In addition, researchers are prompted to consider attitudinal loyalty to complete e-loyalty dimensionality and examine the effect of satisfaction on both behavioural and affective attributes of e-loyalty.

Finally, other studies could deal with the moderating effects of nationality, age, and experience on the proposed model, as other studies have demonstrated (San-Martín et al. 2012). Furthermore, it could be interesting to analyse the effect of hedonic web browsing (Rezaei et al. 2016) on behavioural e-loyalty. As well, several scholars have pointed out the relevance of trust to boost online sales (Wang et al. 2015) and predict behavioural intention toward the online shopping website (Bilgihan and Bujisic 2015; Chen and Chou 2012).