The Wine Sector in the Digital Era: An Empirical Evaluation of E-quality
Opportunities

Mason MC; Gos L; Moretti A

The Wine Sector in the Digital Era: An Empirical Evaluation of E-quality Opportunities

Mason MC^1*, Gos L² and Moretti A¹

Department of Economics and Statistics, University of Udine, Via Tomadini 30/A, 33100, Udine, Italy
Department of Agricultural and Environmental Sciences, University of Udine, Via Delle Scienze 208, 33100 Udine, Italy

Corresponding Author: Mason MC, Department of Economics and Statistics, University Of Udine, Via Tomadini 30/A, 33100, Udine, Italy, Tel: 00380432249223; E-mail: mason@uniud.it

Received August 31, 2015; Accepted November 10, 2015; Published November 12, 2015

Citation: Mason MC, Gos L, Moretti A (2015) The Wine Sector in the Digital Era: An Empirical Evaluation of E-quality Opportunities. J Internet Bank Commer 20:135.

Copyright: © 2015 Mason MC, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Wine quality has been viewed as critical to the success of online businesses, with the growing understanding that it is one of the main aspects of customer satisfaction and loyalty. The aim of this research is to analyze the multi item web quality construct in its relationship with loyalty intention and satisfaction. The study aims to contribute to the literature by addressing two hypotheses: 1) Does loyalty moderate the relationship between satisfaction and the multi-item construct of web-quality? 2) Does satisfaction moderate the relationship between loyalty and the multi-item construct of web-quality? In order to answer these research questions a questionnaire and an interview were used on a sample of 2.782 users of the nine wine consortia web sites of Friuli Venezia Giulia between November 2013 and November 2014. Friuli Venezia Giulia is an Italian Region of ancient winemaking traditions situated in Italy’s northeast and borders Slovenia and Austria. The web consortia aim to regulate, promote and support the regional wine tradition in Italy and abroad. During the filed research data was gathered trough a structured questionnaire that was comprised using a multi-item scales which referred to the seven dimensions of web quality derived from the literature review (i.e., usability, design, processing speed, information quality, contact, navigability and content). Two moderated regression analyses were conducted to investigate first the moderating effects of loyalty on the relationship between web quality and satisfaction and secondly the moderating effect of satisfaction the relationship between web quality and loyalty. Specifically the results show that usability, design, contacts, navigability and contents are the five items of e-quality that have the main positive influence on customer satisfaction, instead of the processing speed that has a negative impact; when we consider customer loyalty as a dependent variable; the main effects are presented by all the seven items.

Keywords

Wine; E-quality; E-commerce; Italy; Usability; Design; Speed; Information; Contact; Navigability; Content; Loyalty; Satisfaction

INTRODUCTION

With the introduction of the Internet and the increasing application of e-commerce in organizations, the importance of measuring and monitoring quality and service has gained recognition in the virtual world.

The conceptualization and development of e-service quality (e-SQ) measures are necessary because they will help to monitor and improve the performance of online businesses [1]. Most e-service quality scales are developed on the basis of the instrument SERVQUAL, by Parasuraman [2,3]. The SERVQUAL instrument was used to measure the quality of service, and some studies have applied the SERVQUAL model to measure e-service quality and have reformulated its items. However, the use of the SERVQUAL scale simply to reword the articles seems inefficient in terms of quality and service [4]. The dimensions of the generic SERVQUAL model must be formulated to be used in the context of e-services, as the e-service is very different from the traditional service. Three aspects stand out:

a) Absence of sales staff: In e -service there is no interaction between the customers and the seller,

b) Absence of traditional tangible: In e -service, the service process is almost completed in a virtual environment with some intangible elements;

c) Self-service customers and in-service: Self-service customers make purchase and implement control in the business process.

Given the differences between traditional service and e-service, the SERVQUAL model is not appropriate to measure the quality of e-services [5]. Some dimensions of the SERVQUAL can be applied to e-service quality, but there are additional dimensions in e-servicemany of which are specifically related to technology. This introduces a discussion of the different models of the dimensions of online service quality.

Loiacono et al. [6] developed WebQual, a scale for rating websites on 12 dimensions: informational fit to task, interaction, trust, response time, design, intuitiveness, visual appeal, innovativeness, flowemotional appeal, integrated communication, business processes, and substitutability. Researchers cite this study as the most comprehensive research, both theoretically and empirically, on the identification of the quality of the website [7,8]. WebQual introduces these 12 dimensions to help designers to better shape their websites. The researchers conducted the study on a sample of students who provided feedback for websites without making actual purchases, so this scale excludes a critical detail: the customer service. For the same reason, WebQual does not include fulfillment as a dimension. The focus of the scale is, therefore on evaluation of design rather than a measure of service quality [9].

Zeithaml et al. [10] developed e-SERVQUAL with 11 dimensions in a series of focus group interviews the dimension are access, ease of navigation, efficiency, flexibility, reliability, personalization, security/ privacy, responsiveness, trust assurance, site aesthetics, and price knowledge. Their research concluded that the fundamental dimensions of service quality including regular efficiency, implementation, reliability and privacy were the same as online. At the same time, they provided services with which responsiveness, compensation, and real time access were used to help as core dimensions of service recovery for online services.

Yoo and Donthu [11] developed a 4-dimensional scale called SITEQUAL to measure online service quality of websites. The four dimensions are ease of use, aesthetic design, processing speed, and security. The data for the development and testing of SITEQUAL was collected from a convenience sample. In the sample, students enrolled in marketing courses were invited to visit and interact with three online shopping sites of their choice and to evaluate each site. The SITEQUAL does not capture all aspects of the buying process and, therefore, does not constitute a comprehensive assessment of the service quality of a site.

Cox and Dale [12] set up a 6-dimensional scale to measure online retailing service quality. The six dimensions are website appearance, communication, accessibility, credibility, understanding and availability.

Barnes and Vidgen [13] developed a completely different scale to measure e-service quality, which they also call WebQual. This scale provides an index of a site’s quality and has five factors: usability, design, information, trust, and empathy. They took into account the customers’ perceptions of weighted importance. Data used in developing and testing the questionnaire was obtained by means of convenience samples of university students/staff who were directed to visit one of three bookstore sites to collect information about a book of their choice. They were then instructed to rate their experience on a scale. The scales design functions without the necessity a respondent’s completion of the purchasing process; it is, therefore, a transactionspecific assessment of a site rather than a comprehensive evaluation of the service quality of a site.

Wolfinbarger and Gilly [8] introduced one of the first psychometrically robust electronic service quality oriented instruments, eTailQ. Their study offered important implications for measuring consumer perceptions of online shopping experiences. Their analyses suggest that judgments about the quality of an online site are strongly related to factors of website design and fulfillment/reliability. From their model, 14 specific elements measure the four factors globally. Four factors extracted from Wolfinbarger’s and Gilly’s [8] model are defined as: Website design, customer service, fulfillment/reliability, and privacy/security. From their results emerged the idea that the inferences of security/privacy are initially obtained by other quality factors, particularly web design, when buyers are new to a website. Moreover, they argue that initially consumers’ judge security/privacy based on factors like the professional look and feel of the website, as well as functionality and organization reputation.

Yang and Fang [14] further examined the differentiation of dimensions for online service satisfaction and dissatisfaction. They argue that there are four main quality dimensions leading to both satisfaction and dissatisfaction including responsiveness, reliability, ease of use and competence.

In 2005, Parasuraman et al. [7] adapted the SERVQUAL model in order to evaluate the quality of the service providers’ on-line stores using ES-QUAL, focusing on four dimensions of service (efficiency, fulfillment, system availability and privacy). Successively, to evaluate also the quality of service in response to complaints, they added the scale E-REC-QUAL. This is the result of three dimensions of quality (responsiveness, compensation and contact details). In this research, the authors evaluate suppliers of retail on-line in terms of quality and service. Their results show that the efficiency embodies the strongest effects on the quality of service, followed by the availability of the system and its privacy.

Today web source quality is the most crucial factor for the performance of e-business and e-government, and can increase the success of various web-based applications. Quality-oriented web evaluation and selection is necessary for efficient use of web information, information analysis, knowledge discovery and decision-making [15]. With the rapid development of communication technologies and the globalization of the market in recent years, the Internet has become an important tool in the business world. With the Internet, distance and time barriers are disappearing. The world is increasingly becoming an integrated community of buyers and sellers that interact via the Internet. Services and products are completely moved into digital form and delivered through the Internet. Many researchers have begun to study the perception of the quality of websites [13,16]. In an assumption concerning these studies, it seems that the website of a company is an essential tool for communication and is the primary interface for Internet users who are looking for both information and/or products; however, this hypothesis has not been explicitly verified in all cases. Through the web, an organization can get in touch with its customers, provide information, and may also sell goods and services online.

Today, the concepts of e-service quality and service have become increasingly important issues in research. E-service is different from traditional service, which is based on the interactive flow of information between customers and service providers. Basically, the website captures the attention of the people who know very little about the company and are interested in it. It tells users what the company is doing in the context of the industry in which it competes [17]. The website is part of the connection between a company and its customers so it should reflect the quality efforts that are in place across the company. The companies therefore wish to offer quality interfaces to their customers [18].

In addition to this, there is another reason why a company must provide high quality websites: there is no human contact through a website, since the interaction takes place through technology. Although the companies may try to match human behavior with technology, the interaction is different, since some aspects of human interaction cannot be replaced with technology. These aspects include courtesy, friendliness, helpfulness, care, commitment, flexibility, and cleaning. These aspects must be replaced by a better performance on the “new” web-specific factors [18]. E-service quality has been considered to have the potential not only to provide strategic advantages, but also to improve operational efficiency and profitability [10,19]. E-service is becoming increasingly critical for companies to retain and attract customers. What brings online customers back to the company websites is a sense of loyalty that comes from good services offered by companies. Companies can achieve competitive capabilities by offering good electronic services to customers [20]. The quality of service has a strong impact on customer satisfaction. Improving the quality and service to satisfy and retain customers is becoming a challenging problem.

Numerous studies have been reserved to identify the basic dimensions of quality of web service that directly affect customers’ perceived service quality of a website [6,8,9,11,21]. On the basis of previous studies of dimensions of quality for websites, seven attributes of quality of service are identified in this study as the main elements affecting the site. These include: Usability, Design, Speed, Information, Contact, Navigability and Content.

This paper is organized as follow. Section 2 gives a brief description of the research context. In section 3 the theoretical framework is presented. In section 4 the methodology is described. Section 5 points out the results. Section 5 gives the conclusions.

Research Context

Italy is one of the main wine producers in the world. The production amounts to 40 million hectoliters produced in 2012 of which over 60% came from the 521 wines with denomination of origin (330 Denomination of Controlled Origin, DOC; 74 Denomination of Controlled and Guaranteed Origin, DOCG and 118 Typical Geographic Identification, IGT).

In Italy the domestic consumption has fallen below the threshold of 40 liters per capita per year. According to ISTAT data relating to the first 11 months of 2012 (processed by Federvini), Italian wine on international markets has forfeited +7.5% on the same period last yearbringing the export turnover to 4.66 billion euro. The export turnover of wine is covered by the United States (+6% in value), Canada (+11%) as well as Germany (+4%) and the UK (+5%). Double-digit growth has kicked in for the Far East, where China and Japan have advanced respectively by 15% and 28%.

Friuli-Venezia Giulia is Italy’s most North-Eastern region. It covers an area of 7,856 square kilometers of ancient winemaking traditions. This land is rich in its variety of vines, from which some of the best Italian wines are produced. The region, high on the list for national production, is represented by 2 DOCG, 9 DOC and 3 IGTs (this means that nearly all of the products come from protected varieties). This Region accounts for 3.1% of the national vineyard area, amounting to nearly 20,000 hectares and 3,286 wine firms (Chamber of Commerce, official database of winemakers in Friuli-Venezia Giulia in 2012). The concentration of micro and small firms is extremely high; the average surface is two hectares per winemaker, and the presence of big firms is sporadic. Of the production area, 75% is reserved for the production of high- quality wines that can hold the “DOC Guarantee of Origin” label. There are 11 DOC areas in the region [22]. It is estimated that less than 15 per cent of the wine firms (i.e., 430 wine firms) have a website and only 7.4% (i.e., 32 wine firms) of them are involved in e-commerce [23]. This shows that unfortunately the majority of wineries have just treating their site like an online brochure. The web-based direct selling is limited to niche markets. Hence there is a low level of implementation of the electronic and digital tools, and the adoption of a limited variety of strategies. Actually, the wine sector invests little resources in the area of electronic commerce. A study conducted by University of Udine on a sample of 48 wine companies with web site located in Friuli Venezia Giulia shows that only a small number of them view their website as a crucial part of their marketing strategy, whereas most producers focus their Internet efforts to use the Web as a promotional tool [23]. Additionally the research has also given evidence that that the most important motivational factor for companies adopting e-commerce is increasing the demand from customers. The wine products sold by the majority of the companies are positioned in the medium-high quality segment, which makes them suitable for online sale. A huge obstacle in adopting e-commerce seems to be the mentality of the producers, and their reluctance to change and to adopt new technologies for selling their products. Many of the surveyed companies believe that e-commerce would lead to cost reductions. Many companies also see interesting advantages in understanding the competition in online wine retailing. Many companies think that administrative problems exporting wine products to foreign markets would overwhelm benefits gained from engaging in e-commerce. Moreover many companies do not even consider e-commerce. In addition, in Friuli Venezia Giulia, many small enterprises face difficulties in establishing an effective e-commerce system. This research examined and identified the slow rate of e-commerce use and to what extent its adoption is taking place in the Friuli Venezia Giulia agri-food sector. Although the majority of the wine firms have already developed websites, besides these basic virtual strategies, these have not realized yet the full potential of this commercial tool. The frequent confusion in the answers of management shows only a partial knowledge of the full benefits that they could acquire. E-commerce business on the web, in general, does not seem to be very developed, partially due to a lack of adequate incentives and motivation to activate a real e-commerce channel. A possible innovative solution could be to consider e-commerce on a consortium basis, establishing a centralized e-commerce market platform to make available Friuli wine products online, to domestic and foreign markets. This approach, known as a co-operative consortium, has many advantages. First of all one can save money and can redistribute the risk among participants. Secondly the participating businesses can pool their resources to provide mutual support which in turn will strengthen their business sector without losing their business independence.

Theoretical Framework

E-quality and consumer satisfaction

Many researchers have tried to discover the attributes that contribute most significantly to the quality assessment by consumers in relation to the services consumed [2,24]. In the traditional service environments the service quality refers to a consumer’s global judgment relating to the superiority of a service handed over relative to competing offerings [25]. In this situation, antecedent research has supported a strong positive relationship between service quality and satisfaction [3,26]. Oliver [27] suggests that service quality is a more specific judgment, which can lead to evaluations of satisfaction by the consumer. In the framework of the Internet, e-service quality is defined as the consumer’s overall evaluation and assessment of the quality of service of mail delivery in the internet market [28]. In line with the traditional definition, is the provision of a superior consumer experience in every aspect of the service provided through the website of an organization. Moreover, it was also identified by Cox and Dale [12] that consumers mainly make their evaluation and assessment of the quality and service according to specific characteristics of the interface of the website because of the limited human interaction with the service provider provision of electronic services. As a result, consumers need to evaluate in a different way as compared to traditional services. They rely on entirely different attributes [12]. Therefore, the focus is on the interaction between the consumer and the interface of the website. However, despite the absence of an agreement on the dimensionality of quality and service, the existing research suggests that the site characteristics influence consumer satisfaction [29-31]. Satisfying customers has now become an imperative marketing, with many organizations realizing the value of satisfied customers in terms of positive brand attitudes, positive word-of-mouth repeated purchases, and brand loyalty.

Consumer satisfaction has been defined as a summary of affective reactions to a service incident [27]. Woodruff and Schumann suggest that satisfaction is an immediate reaction to how much value is received using the product for use in specific situations. Taking these opinions on e-service implies that satisfaction would occur if the quality of service is perceived to be delivered via the website. Thus, once a customer makes a judgment or assessment of total quality derived from the experience offered by the website, then satisfaction should result if the quality judgment is positive. Available research suggests that consumer satisfaction is likely to be driven by the characteristics of the site (for example the usability), since the website is the main interface between the consumer and the company [28]. In addition, several quality attributes seem to have an important positive impact on consumer satisfaction [8,31,32]. The way in which the service is delivered through a website plays a critical role in determining consumer satisfaction. As a result, positive (or negative) consumer perceptions of the quality attributes of the various e-services will result in satisfaction (or dissatisfaction) with the e-services provided through the web site. If the e-service delivery is evaluated as a high quality service, then it should bring satisfaction. On the basis of the literature, we proposed the following hypotheses:

HA: Perceived e-service quality has a significant positive influence on consumers’ satisfaction evaluations.

E-Quality and loyalty intention

Previous research in traditional marketing settings through a variety of services has found a significant positive relationship between perceived service quality and consumers’ loyalty intentions [33]. Previous studies in the information systems literature investigating the adoption of technologies have also found that the ultimate outcome of perceived usefulness and ease-of-use (in using a variety of information technologies such as software systems, email and the internet), is increased intention to use. In the context of the internet environment, perceived e-service quality can potentially increase loyalty intentions such as revisiting the web site, consumer stickiness (i.e., duration of visit to the site), purchase intention and positive word-of-mouth recommendation behavior’s, which can maximize the online competitive advantages of e-commerce [28]. Previous e-retail research in this context has found that a direct relationship exists between e-service quality and various behavioral intentions. We see for example that several studies have found that the e-service quality is positively correlated with purchase intention and site revisit [6,16,32], the probability to recommend the website to others [8] and lead to a decrease in complaint behavior’s. For this reason, it is argued that a consumer’s positive assessment of e-service quality is directly correlated with specific behavioral intentions. When a consumer’s assessment of e-service quality is negative, the consumer will not engage in or avoid specific behaviors, such as exhibiting negative word of mouth communications, discontinue visiting the web site or switching to competitor web sites. As such, e-service quality appears to directly lead to behaviors such as word-of-mouth recommendation behaviors, site revisitation and purchase intentions [33]. On the basis of the literature, we proposed the following hypothesis:

HB: Perceived e-service quality has a significant positive influence on consumers’ behavioral intentions.

Methods

On the basis of two hypotheses HA and HB were developed the two conceptual models. The conceptual model 1 describes the relationship between e-service quality and the loyalty intention with the moderator effect of the satisfaction. The conceptual model 2 describes the relationship between e-service quality and the customer satisfaction with the moderator effect of the loyalty intention.

Conceptual model 1

We have proposed a model that describes the direct relationship between variables of e-service quality that are: website design, processing speed, quality of information, contact, navigability, content, with the loyalty intention. There has been investigation of moderator effects of satisfaction in the relationship between various components of e-service quality and loyalty intention (Figure 1).

Hypotheses development for the model 1

Usability and customer satisfaction: Usability refers to the ease of use, especially with regard to the search for information [11,14]. Ease of access to the information available is an important reason for consumers to choose to purchase via the Internet [8,32]. Usability is thus a key factor for the quality and service and customer satisfaction [9]. This usability is an important aspect of e-service quality because the environment of e-business can be complex and intimidating for many customers [4]. Given that the Internet-based operations may seem complex and threatening for many customers, it is reasonable to expect that the ease of use of websites would be a determining factor in perceived e-service quality [7]. Davis [34] states Usability is defined as “the extent to which the prospective user expects the target system to be free of effort “. Functions like looking for a site, the download speed, overall design, and organization are among the key elements that affect usability. Usable systems must be compatible with users’ cognitive skills in communication, comprehension, memory and problem solving [13,35].

Usability can be measured with the following five attributes: learnability, efficiency, memorability, low error, and personal satisfaction. Loiacono et al. [16] research has also shown that usability in terms of search engine of the site, the overall design, the download speed, and the overall organization of the site affect the usability of web sites by consumers [36]. Sharma and Natarajan [35] define the characteristics of usability of a website in terms of ease of navigability, the adequacy and quality of the links, site structure and interactivity.

Based on the above we propose the following hypothesis:

H1A: Usability positively influences customer satisfaction.

Navigability and customer Satisfaction: According to Palmer, navigability is defined as” the sequencing of pages, well-organized layout, and consistency of navigation protocols. Montoya-Weiss argue that the navigability is normally measured by the number of clicks it takes to log on and through the site. Sigman and Boston [36] also indicated several tools for effective navigation. First, the website should facilitate users to get information in the fewest possible steps. Secondly, the website should always provide hyperlinks within each web page. Thirdly, the web site must not contain broken hyperlinks. Finally, the relevance of the hyperlink and the description of the intended destination should be clearly communicated. Madu and Madu [21] proposed that the ease of navigation is key to improving customer satisfaction. They have established that users might be unhappy when the site is difficult to navigate. Loiacono et al. [16] pointed out that the navigation mechanism is very important and stressed the importance of building a good quality website with constant connections and good navigational tools. In addition, for a site to be perceived positively by customers, it must provide functions that help customers find what they need quickly and easily, provide user-friendly environments, and allow the customers to be in control by moving quickly back and forth through the pages [10,35]. Yoo and Donthu [11] found that a positive correlation exists between customer perceptions of website navigation and satisfaction with website performance. Further support is provided by Huizingh and Hoekstra who found that navigation had a direct influence on consumer attitudinal changes towards websites. Cry found that navigation of websites influenced consumers’ satisfaction and loyalty. Therefore, the following hypothesis is proposed:

H2A: Navigability positively influences customer satisfaction

Quality of Information and customer satisfaction: Consumers visit mobile websites to look up information, such as price information, product information, and promotional information. Providing information is the fundamental objective of a website. The quality of information refers to the amount, accuracy and format of the information about the products and services offered on a website. Consumers seek general information, information about the product / service, customer support information, service information for the customer [37], complete information on specific products and information to compare all alternatives [38]. According to Yeung and Law explained the quality of information plays an essential role in the success of a website. Ranganathan and Grandon [38] found that the quality of information is one of the most important reasons why travelers make reservations on a specific travel website. McKinney concluded that the best quality information increases satisfaction with the online experience. Turban and Gehrke [39] also showed that the measures of quality information on the website indicate whether or not customers will be attracted to a website. As a result, DeLone and McLean [40] found that high quality information is positively associated with customer satisfaction. Based on the above, we offer the following hypothesis. Consumers will be satisfied with the results when they find the information they need on the mobile websites [40]. Therefore, in this study the relationship between information quality and satisfaction is posited as follow:

H3A: Quality of information positively influences customer satisfaction

Website Design and customer satisfaction: A past empirical study found that website design factors are strong predictors of customer quality judgments, satisfaction, and loyalty for internet retailers [8]. The results of prior empirical studies found that web user’s intention to revisit the website was significantly influenced by her positive attitude towards the website. The web design takes into account the graphic style that involves issues such as color, layout, print size and type, the number of photographs and graphics and animations [9]. Numerous studies have shown that the design involves site appearance and visual design [40]. It is believed that the design is an important element of perceived service quality. According to Loiacono, Chen and Goodhue [16] the time it takes to download a webpage, the graphical and textual features that affect the user’s sense of ease or comfort with the website and the aesthetics of a website should be considered in website design. A good strategy includes knowing what to emphasize on a website, presenting with consistency, and using up-to-date technology. In our study web design construct deals with the visual presentation of the website, the use of graphics, colors, photographs, various font types to improve the look and feel of the website and other related characteristics. Based on our review of previous studies, we present our first hypothesis as follows:

H4A: Website design positive influences customer satisfaction

Speed and customer satisfaction: Processing speed refers to the promptness of online processing and interactive responsiveness to a consumer’s request. Kim and Stoel concluded that fast response time had strong positive influence on consumer satisfaction with online shopping. Szymanski and Hise [31] suggested that convenience and the saving of time and effort, significantly influenced consumer satisfaction, while fast load speed reduced consumers’ waiting time when they visited mobile websites. A study by Zona Research in 1999 showed that the tolerance of the expectation from the users is less than ten seconds; after eight seconds on average, if the page had not fully downloaded 30- 50% of users would choose to neglect the page. Other authors [41] also highlight some typical behaviors and preferences of the users and, in particular, focus on the economic impact linked to long waiting times for the downloading page. The response times for each user can depend on many factors (performance of servers and network connection type and speed, amount of information that needs to be transferred, etc.), but users are not interested in these motivations [31]. If the wait is long, users just do not think they are being offered a good service and the level of trust in the supplier is bound to decrease. Fast response time could make consumers’ visiting experience fluent and save consumers’ time. Therefore, the following hypothesis is proposed:

H5A: Processing speed positive influences customer satisfaction

Contact and customer satisfaction: A website must provide facilities for users to interact with the Webmaster, a particular author of content in the site. Providing summarized answers to FAQ’s, clear error messages and contact information are some of the possible methods used to facilitate interaction of users with the website [14]. Interactive feedback systems, email communications and toll free call systems are basic tools which support the interaction of users with the website [14]. Points of contact for a customer include the options to speak with a live customer service agent online or through the phone [37]. Based on our review of previous studies, the following hypothesis is proposed:

H6A: Contact positive influences customer satisfaction

Content and Customer satisfaction: Content is one of the most important influences on perceptions of website experience. With the absence of tangibility on the Internet, the key driver of consumer traffic online would be the subject matter available that is relevant to consumer needs or goals [42]. Especially on websites concerning wine, users come to the site looking for a particular piece of information. The main identity of web applications is found in their combination of information, services and functionality. The information provided on a website should be relevant, engaging and appropriate for users [42]. The content is the most critical part of a website. Users connect to a website mainly looking for a specific type of information, giving less attention to the ease navigation, visual design and interactivity of the site [42]. This is due to the fact that users are goal oriented and only look for the information they already have in mind when they come to the site [35,36]. Consequently, they do not give much attention to aspects of website design apart from the contents of the site. In addition to relevancy, the novelty and quality of content helps to further enrich consumer experiences whilst on websites. As opposed to a website that contains plain text, a website with the right combination of animated graphics, videos and text would provide a far more enriching and valuable experience to consumers, and evidence have found higher satisfaction levels for the latter website [42]. Moreover, intuition would suggest that consumers would be dissatisfied with websites with incomplete or incorrect information. Indeed, Natarajan et al. [35] found evidence to prove information quality has a positive correlation with overall customer website satisfaction. Moreover, this study further proposes that novel content which is relevant and valuable to customers provides a distinct competitive advantage for retailers. Thus, it is theorized that the relevancy, quality and novelty of website content also drive consumer perceptions of website experience satisfaction for e-commerce websites, leading to the following hypothesis:

H7A: Content positive influences customer satisfaction.

The following hypotheses have been proposed for the conceptual model 1

H1A: Usability positive influences satisfaction

H2A: Website design positive influences satisfaction

H3A: Processing speed positive influences satisfaction

H4A: Quality of information positive influences satisfaction

H5A: Contact positive influences satisfaction

H6A: Navigability positive influences satisfaction

H7A: Content speed positive influences satisfaction

Satisfaction as Moderator: Some researchers have found that satisfaction moderates the relationship between service quality and loyalty intentions. The nature of an interactive or moderating effect is that the relationship between an independent variable and a dependent variable varies according to the level of a third variable (moderator) present also in the relationship. When there is a significant interaction quality-service satisfaction, the slope of the relationship between service quality and behavioral intentions depends on the level of satisfaction felt by the consumer. This means that the slope of the relationship between service quality and a positive intentional consequence, like advocacy, would become more positive at higher levels of satisfaction or would become less positive with a dissatisfying service encounter. For the service marketer, a moderating relationship means that the degree of satisfaction/dissatisfaction with the recent service encounter would change the extent to which prior service quality evaluations remain a good predictor of customer intentions.

The following hypotheses have been proposed for the conceptual model 1 as the satisfaction moderator effect:

H8A: Satisfaction moderates in the relationship between usability and loyalty intention

H9A: Satisfaction moderates in the relationship between website design and loyalty intention

H10A: Satisfaction moderates in the relationship between processing speed and loyalty intention

H11A: Satisfaction moderates in the relationship between quality of information and loyalty intention

H12A: Satisfaction moderates in the relationship between contact and loyalty intention

H13A: Satisfaction moderates in the relationship between navigability and loyalty intention

H14A: Satisfaction moderates in the relationship between content and loyalty intention.

Conceptual model 2

We have proposed a model that describes the direct relationship between the following variables of e-service quality that are: website design, processing speed, quality of information, contact, navigability, content, with the satisfaction. There has been investigation of moderator effects of loyalty intention in the relationship between various components of e-service quality and customer satisfaction (Figure 2).

Hypotheses development for the model 2

In the online environments, it is more difficult for a company to build customer loyalty when consumers can leave with just a mouse click away. Although customer increasing their interactions with company through websites, there are plenty of online shops offering the same product or service, customer can easily switch their purchasing decision than they do in physical environment [43]. For the companies, attracting new online customers will be more difficult and costly to retain existing ones [34] therefore, customer satisfaction and customer loyalty are another important issues in online retailing. The customer loyalty is divided into two parts: a) behavioral intention to use, b) word of mouth. The behavioral intention to use was obtained from the Technology Acceptance Model (TAM). To understand the user’s behavioral intention and actual intention to use a lodging website, the technology acceptance model (TAM) need to be discussed briefly. Research on adoption of the Internet as an information source has been conducted by many researchers using the framework of the TAM. TAM postulate that perceived ease of use and perceived usefulness can predict attitudes toward technology that then can predict the usage of that technology. TAM was introduced by Davis [34] in his study of adoption of computer technology, which was based on the theory of reasoned action (TRA). He applied the theory of Ajzen and Fishbein about reasoned action (TRA) to show that beliefs influence attitudes which lead to intentions, and therefore generate behaviors. Initially, Ajzen and Fishbein introduced TRA as a general model, but it was also considered as a well-suited framework for computer acceptance modeling that specifically explained users’ computer usage behavior [34]. TAM is based upon a theoretical basis of TRA to specify the causal relationships between two main variables (perceived usefulness and perceived ease of use) and users’ attitudes, intentions, and, ultimately, users’ computer behaviors [43].

Davis asserts that TAM’s belief–attitude–intention–behavior relationship predicts user acceptance of information technology. Davis said that perceived usefulness and ease of use represent the beliefs that lead to such acceptance. Perceived usefulness is the degree to which a person believes that a particular information system reducing the time to accomplish a task or providing timely information. The perceived ease of use is the degree to which a person believes that using a particular system would be free of effort. In TAM there are two other constructs. These are attitude toward use and behavioral intention to use. Attitude towards use is the evaluation of the users’ opportunity to use a particular application of information systems. Behavioral intention to use is a measure of the probability that a person will use the application. Dependent variable of TAM is actual usage. It has typically been a self-reported measure of time or frequency of employing the application. Some authors have considered additional relationships. Some have ignored intention to use or attitude [43] and instead studied the effect of ease of use or usefulness directly on usage. Findings about the effects of attitude and intention have not always been significant. Word of Mouth was published about 40 years ago by William Wight, in a classic study of marketing in Fortune magazine. Word of Mouth can be defined as communication between consumers about products, services or companies in which incorporators follow no commercial goal in this communication [44]. Word-of-mouth (WOM) is one type of behavioral intention which resulted from e-quality and satisfaction. When customers are satisfied with the service provided, they are more likely to recommend the company to others [3,44]. Similarly, Hennig indicated that WOM is very important for a firm’s long- term economic success to attract new customers. Furthermore, existing and continuing customers may validate the reputation of the firm to new customers [10]. In the context of e-commerce, WOM plays an important role because customers rely heavily on the advice and suggestions from others who have experienced the service. In traditional commerce, unhappy customers communicate their negative experience to five friends, whereas on the Internet 5000 people can learn of it. Indeed, customers often trust others more than they trust communication from merchants [44]. Thus, we can conclude that WOM offers a good correlation between satisfaction and likelihood of recommending a site to others. Positive Word of Mouth consists of desirable and good advices people give to others about products, services or commercial brands [45]. Regarding to various kinds of behavioral intentions impressed by electronic services quality and satisfaction, Word of Mouth is introduced as one of most common applied variants. Parasuraman perceived that when consumers’ comprehensions of services quality are high, they advise the company to others [46]. In another research it became clear that preserving customers cause attracting new customers through Positive Word of Mouth [45]. Based on the literature, we formulated the following hypothesis.

The following hypotheses have been proposed for the conceptual model 2

H1B: Usability positive influences loyalty intention

H2B: Website design positive influences loyalty intention

H3B: Processing speed positive influences loyalty intention

H4B: Quality of information positive influences loyalty intention

H5B: Contact positive influences loyalty intention

H6B: Navigability positive influences loyalty intention

H7B: Content speed positive influences loyalty intention

Loyalty intention as a moderator: Loyalty is defined in the marketing context as “an intention to perform a diverse set of behaviors that signal a motivation to maintain a relationship with the focal firm, including allocating a higher share of the category wallet to the specific service provider, engaging in positive word of mouth (WOM), and repeat purchasing” [47]. Note that this conceptualization considers positive WOM as a component of loyalty. This approach is common in a great number of studies in the marketing literature [45] and we found that it was employed in 40 studies reviewed in our analysis. These studies could not, however, be included because they did not present specific results for the WOM construct. However, most of the studies in the WOM literature measure WOM as a single uni dimensional construct, addressing WOM valence only indirectly by measuring the likelihood of positive or negative recommendations. In some instances, studies work with a neutral position, mixing positive and negative measures among the construct. In our theoretical framework we establish WOM as a moderator. WOM valence has been receiving more attention in recent investigations. There is evidence that extremely positive and extremely negative WOM are the most common examples of WOM [45]. The rationale, as shown in Oliver and Anderson, is that customers in the extremes of high or low satisfaction are more likely to spread WOM. Research has shown that highly satisfied customers have a desire to tell others about their positive experience [45]. Thus, we expect that customers spreading positive WOM are those customers who have high satisfaction and, hence, when WOM assumes a positive valence, there will be a direct relationship between satisfaction and WOM (higher satisfaction is associated with higher positive WOM). On the other hand, we also expect that customers spreading negative WOM are those with the lowest satisfaction levels, such as those experiencing a service failure followed by an unsatisfactory recovery. The reason is that disappointed customers (those with unsatisfied expectations) often experience negative emotions such as regret, frustration, and anger (Bonifield and Cole; Sweeney et al.). As a consequence, they use negative WOM as a way of “venting” their negative emotions and achieving a feeling of relief after commenting on the incident [45]. Therefore, we expect that customers spreading negative WOM are those with lower satisfaction and, thus, when WOM assumes a negative valence, there will be an inverse relationship between satisfaction and WOM (lower satisfaction is associated with higher negative WOM). Based on the above discussion, we propose:

The following hypotheses have been proposed for the conceptual model 2 as the loyalty intention moderator effect:

H8B: Loyalty intention moderates in the relationship between usability and satisfaction

H9B: Loyalty intention moderates in the relationship between website design and satisfaction

H10B: Loyalty intention moderates in the relationship between processing speed and satisfaction

H11B: Loyalty intention moderates in the relationship between quality of information and satisfaction intention

H12B: Loyalty intention moderates in the relationship between contact and satisfaction

H13B: Loyalty intention moderates in the relationship between navigability and satisfaction

H14B: Loyalty intention moderates in the relationship between content and satisfaction

Results

Factor analysis is a multivariate statistical method, which starts from the research related to the dependence of the internal variables and concludes by compiling the numerous complex variables into a few comprehensive factors. Through this analysis we determine the correlations between a large number of variables. The factorial analysis refers to the technical interdependence, which categorizes the information from a large number of variables into factors depending on their relationships (Hair). The purpose of the factor analysis is to simplify the understanding of the data, which can be obtained both from an exploratory and a confirmatory point of view (Hair et al.). Exploratory factor analysis and confirmatory factor analysis are two statistical approaches used to examine the internal reliability of a measure. The latter is generally used to explore the factor structure of a measure and examine its internal reliability. Exploratory factor analysis is often recommended when researchers have no assumptions about the nature of the factor’s structure at the basis of their size. In this study, exploratory factor analysis was used because the purpose was to determine the correlations between the dimensions of the quality of the websites for Consortia of wine. The factor analysis uses a correlation matrix to establish whether variables have similarities or not. A group of similar variables comprise a factor. Depending on how well the factor describes the variable, the variables have different loadings on the factor. The similarities between the variables in one factor and the difference of these to the variables in other factors can be used to describe the “meaning” of a factor. The higher the correlation of the variables is, the less factors are needed. In the present study, the factor analysis has been done with SPSS as the instructions indicate and a varimax rotation has been used. The factors are sorted and numbered by the variance they explain.

Descriptive statistics

The questionnaire was created with Google Drive and proposed to people connected on line. At the end of the research, more than 300 questionnaires were collected for each consortium (Table 1) for a total of 2782 questionnaires. There weren’t missing data, for the compilation of all the questions was obligatory in order to send in the questionnaire.

The statistical regarding gender, age and education are summarized in the Table 2.

According to Table 3, 50.3% of respondents are men and 49.7% of them are women. The highest frequency is related to the age group 19 to 29 and the lowest frequency is related to the age group from 65 years and more. Furthermore, 38.8% of them have degree and the lowest frequency for education is related to those with a primary school.

To better understand the descriptive analysis, we have created the following graphs. As we see from Table 3, the total sample consists of 2782 questionnaires in which 50.3% of respondents are men and 49.7% of them are women.

Research variables and measurement

The study’s constructs were developed by using measurement scales that were adopted from previous studies (see Appendix for details). The study’s variables were measured using a seven-point Likert-type scale.

The statistical computer program used for the questionnaires data analysis was SPSS for Windows Version 20.0. The multiple regression analysis was used to further explain the significance of the independent and dependent variables. The statistical significance difference targeted was.06 alpha levels which is typical in most research (Cooper and Schindler; Sekaran).

The hypotheses in this study test the seven dimensions of e-service quality: Usability, website design, processing speed, quality of information, contact, navigability and content as the independent variables relate to satisfaction and loyalty intention of e-service quality. Multiple regression analysis was employed to test the hypotheses (Table 4).

The following conclusions can be drown from the results of our modeling in the Nine Consortia:

H1B: Usability positive influences loyalty intention. The hypothesis is confirmed (+0,048) is significant

H2B: Website design positive influences loyalty intention The hypothesis is confirmed (+0,163) is significant

H3B: Processing speed positive influences loyalty intention. The hypothesis is confirmed (-0,128) is significant

H4B: Quality of information positive influences loyalty intention. The hypothesis is confirmed (-0,008) is not significant

H5B: Contact positive influences loyalty intention. The hypothesis is confirmed (+0,153) is significant

H6B: Navigability positive influences loyalty intention. The hypothesis is confirmed (+0,209) is significant

H7B: Content speed positive influences loyalty intention. The hypothesis is confirmed (+0,057) is significant.

In Figure 3 one can observe that in the case of high satisfaction the increase of usability, the information quality, the contact, the navigability and the content corresponds to an increase of the loyalty intention, while in case of low satisfaction the increase of usability, the information quality, the contact, the navigability and the content corresponds also to an increase of the loyalty intention. In case of high satisfaction the increase of the design and processing speed corresponds to a decrease of the loyalty intention, while in case of low satisfaction the increase of the design and processing speed however cause a decrease of the loyalty intention.

The following considerations can be drown from the results of our modeling in the nine Consortia:

HB: The relationship between Usability and Loyalty Intention taking in account the moderator effect of the satisfaction (SA) is stronger (+0,048+0,074 = +0,122 SA) without SA it is positive but it is weaker (+0,048)

H9B: In the absence of SA, the relationship between X=Website design and Y=Loyalty Intention is weaker for the website with SA=1 (+0,163-0,158= +0,005) that the site without SA=0 (+0,163).

H10B: The relationship between Processing Speed and Loyalty Intention taking in account the moderator effect of the satisfaction (SA) is negative but it is weaker (-0,128-0,023= -0,151 SA) without SA it is stronger (-0,128).

H11B: The relationship between Quality of Information and Loyalty Intention taking in account the moderator effect of the satisfaction (SA) is stronger (-0,008+0,092= +0,084 SA) without SA is weaker (-0,008).

H12B: In the absence of SA=0, the relationship between X=Contact and Y=Loyalty Intention is weaker (+0,153) with SA=1 (+0,153 +0,086= +0,239) is positive and it is stronger.

H13B: The relationship between Navigability and Loyalty Intention taking in account the moderator effect of the satisfaction (SA) is stronger (+0,209+0,003=+0,212 SA) without SA is weaker (+0,209).

H14B: In the absence of SA=0, the relationship between X=Content and Y=Loyalty Intention is weaker (+0,057) with SA=1 (+0,057+0,026= +0,83) is stronger (Table 5).

The following conclusions can be drown from the results of our modeling in the nine Consortia:

H1A: Usability positive influences satisfaction. The hypothesis is confirmed (+ 0,081) is significant

H2A: Website design positive influences satisfaction. The hypothesis is confirmed (+0,281) is significant

H3A: Processing speed positive influences satisfaction. The hypothesis is confirmed (+0,048) is significant

H4A: Quality of information positive influences satisfaction. The hypothesis is confirmed (+0,191) is significant

H5A: Contact positive influences satisfaction. The hypothesis is confirmed (+0,084) is significant

H6A: Navigability positive influences satisfaction. The hypothesis is confirmed (+0,039) is significant

H7A: Content speed positive influences satisfaction. The hypothesis is confirmed (+0,059) is significant.

In Figure 4 one can observe that in the case of high loyalty intention the increase of usability, processing speed, information quality, contact and content corresponds to an increase of satisfaction while in case of low loyalty intention the increase of usability, processing speed, information quality, contact and content corresponds to an increase of satisfaction. In case of high loyalty intention the increase of the design and the navigability corresponds to a decrease of the satisfaction while in case of low loyalty intention the increase of the navigability corresponds to a decrease in satisfaction while in case of low loyalty intention the increase of the design corresponds to a increase in satisfaction.

The following considerations have gone into the light of the results of our modeling in the all nine Consortia:

H8A: The relationship between Usability and Satisfaction taking in account the moderator effect of the Loyalty Intention (LYI) is stronger (+0,081+0,042 =+0,123 LYI) than out of LYI (+0,081)

H9A: In the absence of LYI=0, the relationship between X=Website design and Y=Satisfaction is stronger (+0,281) with LYI=1 (+0,281- 0,141 = +0,140) it is positive but it is weaker.

H10A: The relationship between Processing Speed and Satisfaction taking in account the moderator effect of the Loyalty Intention (LYI) is stronger (+0,048+0,032= +0,080 LYI) without LYI=0 is weaker (+0,048)

H11A: The relationship between Quality of Information and Loyalty Intention taking in account the moderator effect of the Loyalty Intention (LYI) is stronger (+0,191+0,077 = +0,268 LYI) than out of LYI (+0,191)

H12A: In the absence of LYI=0, the relationship between X=Contact and Y=Satisfaction is weaker (+0,084) with LYI=1 (+0,084+0,025 = +0,077) it is positive and it is stronger.

H13A: The relationship between Navigability and Loyalty Intention taking in account the moderator effect of the Loyalty Intention (LYI) is weaker (+0,039-0,005= +0,034 LYI) without LYI=0 is stronger (+0,039)

H14A: In the absence of LYI=0, the relationship between X=Content and Y=Satisfaction is stronger (+0,059) with LYI=1 (+0,059-0,003 = +0,056) it is weaker.

Conclusions

Internet has become an important tool for online shopping, information search and developing a knowledge-base of consumers. Researchers are attempting to find relationships between web quality and its outcomes. One of the objectives of the present research is to find direct interactions between the components of e-service quality with the loyalty intention and customer satisfaction. The results of this study provide additional insights into the relationship between perceived service quality with satisfaction and loyalty intention by analyzing the moderating effects of loyalty intention and satisfaction. Our findings provide support for this notion. First, satisfaction indeed moderates the relationships between service quality and loyalty intention. Second, loyalty intention indeed moderates the relationships between service quality and satisfaction.

The results confirmed that the websites of the Friuli Venezia Giulia consortia can be considered of good e-quality and this is linked to customer satisfaction and loyalty intention but there are many differences between consortia. Specifically, taking into account all the nine consortia together we have seen that the conceptual model 1A results on the moderate regression reiterate the positive relationship between usability and loyalty intention, design and loyalty intention, contact and loyalty intention, navigability and loyalty intention and content and loyalty intention. The quality of information and the loyalty intention have a negative relationship. Results also support the moderation hypotheses of satisfaction in affecting the direct relationships between usability, information quality and contact. While the results of the conceptual model 1B on the moderate regression of all the nine consortia reiterate the positive relationship among all nine quality factors and customer satisfaction. Results also support the moderation hypotheses of loyalty intention in affecting the direct connection between usability, processing speed, and quality of information. Both models show that the usability and quality of information are very important elements in the evaluation of the e-quality done by the customers.