The Impact of Wi-Fi Service in Restaurants on Customers’ Likelihood of Return to a Restaurant

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The Impact of Wi-Fi Service in Restaurants on Customers’ Likelihood of Return

to a Restaurant

CIHAN COBANOGLU

University of South Florida Sarasota-Manatee, Sarasota, FL, USA ANIL BILGIHAN

Ohio State University, Columbus, OH, USA KHALDOON “KHAL” NUSAIR University of Central Florida, Orlando, FL, USA

KATERINA BEREZINA

University of Florida, Gainesville, FL, USA

The purpose of this study is to explore the impact of wireless internet (Wi-Fi) service on customers’ likelihood of returning to a restaurant. Data was collected via an online structured ques- tionnaire from randomly chosen 1000 restaurant customers in the U.S. The results showed that (1) Wi-Fi access has become an important amenity in restaurants and cafes in the United States;

(2) Technology-savvy customers prefer restaurants or cafes with Wi-Fi service; (3) Customers prefer free Wi-Fi in restaurants over paid models; and (4) A multiple regression model supported that Wi-Fi service availability, Wi-Fi service quality, price of Wi-Fi ser- vice, perceived risk of using Wi-Fi service, and perceived value of Wi-Fi are predictors of likelihood of a customer’s return to a restau- rant. The research findings highlight the importance of internet amenities in restaurants. It shows that providing Wi-Fi service in a restaurant is a predictor of the likelihood of customers returning to a restaurant.

KEYWORDS information technology, restaurants, Internet, Wi-Fi

Address correspondence to Cihan Cobanoglu, University of South Florida Sarasota- Manatee, 8350 N. Tamiami Trail, Sarasota, FL 34243, USA. E-mail: [email protected]

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INTRODUCTION

There has been a consequential growth in Wireless Fidelity Networks (Wi- Fi) technology in recent years (Houliston & Sarkar, 2004). Intel calls Wi-Fi

“the most disruptive technology since the Internet” (Foremski, 2002). The future of technology is in secure, wireless, mobile, go-anywhere computers, and anything that helps people achieve this is a step in the right direction (Smithers, 2007). Internet has an important place in daily life.

People have become dependent on their connection to the Internet for daily activities involving work, school, and ever-growing online social networking. Over the past few years, IEEE 802.11 wireless networks have become increasingly widely deployed (Collins & Cobanoglu, 2008). Wireless local area networks (LANs) can be found in coffee shops, airports, hospitals, and restaurants. Wi-Fi is increasingly becoming a must for public places and consumers are demanding it. Lieshout and Rodriquez (2007) reported that Cafe owners who don’t offer Wi-Fi are seeing one to three people leave every day. Not only do they leave, but most of them will not return, not to mention the number of people each one of them will deter from visiting the cafe. Wi-Fi Internet access may be the tool that attracts new customers, holds customer loyalty, and increases store sales. Hotspots employ many different revenue models to drive this business (Wi-Fi Alliance, 2004).

However, this technology also brings many responsibilities to restaurant and cafe owners in the area of security. The purpose of this study is to explore the impact of Wi-Fi service on customers’ intention to visit to a restaurant.

REVIEW OF LITERATURE

Over the past few years, there has been an exceptional increase in the number of wireless users, applications, and network access technologies (Balachandran, Voelker, & Bahl, 2005). Technology is one of the most significant competitive advantages for any hospitality company in this con- temporary and swiftly changing environment (Olsen & Connolly, 1999;

Olsen & West, 2008). According to Buhalis (1998), tourism enterprises are required to implement innovative approaches and to improve their com- petitiveness. Tourism enterprises can use information technology as a key contributor to competitive advantage by means of differentiation and cost reduction. As stated by Buhalis (1998), technologies have an effect on competitive advantage. Wireless LANs are providing a networking platform to expand network connectivity to the hotspots in those fast changing envi- ronments (Balanchandran et al., 2005). Consumers have started to demand Wi-Fi in public places and restaurants have already begun to take advantage of wireless connectivity to attract guests. Mercer (2006) mentioned that the

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use of Wi-Fi is becoming close to mainstream in the U.S. and Europe. One of the first restaurants which employed Wi-Fi was McDonalds. The fast food chain pioneered free high-speed wireless (Smithers, 2007). McDonalds tried to attract new customers by giving away free Wi-Fi; however, the idea did not fit the company’s mission. Later they changed the Wi-Fi model which makes people buy items so that they could use the Wi-Fi. A free model might work better in independent coffee shops since people feel more obligated to buy something.

History of WLAN and Wi-Fi Basics

Wi-Fi technology has been standardized by the IEEE committee as 11 Mega bit per second (Mbps) wireless LANs (Houliston & Sarkar, 2004). Wireless LANs also known as Wi-Fi’s are based on IEEE 802.11 technology (IEEE, 1999) and they provide high wireless data connectivity. WLANs were intended to substitute wired LAN connections in offices. Research by Motorola showed that wired LAN connection costs were very expensive, especially in large offices (Brodsky, 1995).

Overview of IEEE 802.11

The IEEE assigns standards for protocols and sets numbers for each of the protocols to categorize these standards. IEEE 802.11 is the standard for the family of wireless network protocols (IEEE, 1999). OSI Layer 1 (Physical) and 2 (Medium Access Control) are the points that IEEE 802.11 handles.

Wireless networking is still in its growth stage as more technologies for wireless networking are revealed day after day. IEEE 802.11a, 802.11b, and 802.11g have been the most popular standards for wireless connectivity. Most of IEEE 802.11 standard specifications are approximate.

One of the most popular protocols is 802.11b, which operates at the 2.4GHz unlicensed frequency band. IEEE 802.11b has a maximum bandwidth of 11Mbps. 802.11g is backwards-compatible with 802.11b and has a headline data rate of 54Mbps. In order for multiple networks to share the same medium, thus having more than one wireless network in the same physical place, there are different communication channels that may be used, each with a different frequency band. Channels in 802.11b/g vary from 1 to 14 (2,412–2,484GHz), but have legal constraints on which subset of channels may be used; for example, channel 14 is used only in Japan. 802.11a operates around 5GHz and also has a maximum bandwidth of 54Mbps. One of the most recent wireless standards is 802.11n, which has a bandwidth of 100Mbps (Behzad et al., 2007; Xiao, 2005).

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Uses of Wi-Fi

A Wi-Fi enabled device such as a personal computer, portable computer (i.e., laptop), game console, cell phone, MP3 player, or personal digital assistant can connect to the Internet when within range of a wireless network connected to the Internet. Even though it was designed primarily for private applications, Wi-Fi is also being deployed in public places to create so- called hotspots, where Wi-Fi-capable users can obtain broadband Internet access. Several technical and business-related challenges such as “ease of use” and “security” must be overcome with the use of Wi-Fi (Henry & Luo, 2004). Hotspots can cover as little as a single room with wireless-opaque walls or as much as many square miles covered by overlapping access points such as university campuses. Global hotspots will grow by 40% over 2007 (Gallen, 2008). Wi-Fi also allows connectivity in peer-to-peer (wireless adhoc network) mode, which enables devices to connect directly with each other. This connectivity mode is useful in consumer electronics and gaming applications.

Advantages of Wi-Fi

Wi-Fi has significant advantages over wired connections. Wi-Fi is easy to set up and it is inexpensive. They’re also unnoticeable; you may not even notice when you’re in a hotspot unless you’re on the lookout for a place to use your laptop (Brain & Wilson, 2001). Additionally, taking advantage of mobility is a valuable aspect of a Wi-Fi network (Al-Alawi, 2006). The usability advantage of Wi-Fi stems from two things: wireless networks can be used everywhere, and the need for cables is eliminated (Hassinen, 2006).

Since Wi-Fi is standardized by Wi-Fi Alliance, different brands are inter- operable. Any standard Wi-Fi device will work anywhere in the world.

Disadvantages of Wi-Fi

Security is one of the significant disadvantages of Wi-Fi. According to Avila (2008), many Wi-Fi users don’t know that hackers posted at hotspots can steal personal information out of the air relatively easily. With an unsecure Wi-Fi, hackers might have access to credit cards, bank accounts, and other personal financial information; additionally, they can even sneak into users’

networks. AirDefense, which is a wireless security manufacturing company, observed wireless access points at stores and other retail outlets in Atlanta, Boston, Chicago, Los Angeles, New York City, San Francisco, London, and Paris as part of an annual wireless security survey; they found that a quar- ter of the 4,748 access points surveyed had no encryption (Cheng, 2007).

Additionally, Hu, Myers, Colizza, and Vespignani (2008) claims that there is absolutely a concern about the wireless spread of Wi-Fi based malware.

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THEORETICAL FRAMEWORK Better Value Theory

Better value theory states that “consumers are most likely to choose a product or a service because it reflects their perception of receiving value for the time and cost involved” (Christodoulidou, Brewer, & Countryman, 2007, p. 230). Better value attributes include price, functionality, usefulness, and necessity at the time of purchase. Value is a function of quality perception of the customer (Athanassopulos, 2000). Better value technology relates to another theory, Social Exchange Theory, as explained below.

Social Exchange Theory (SET)

Although different views of social exchange have emerged, theorists agree that social exchange involves a series of interactions that generate obligations (Emerson, 1976). SET has been defined as Homans (1958, p. 606) as:

Social behavior is an exchange of goods, material goods but also non- material ones, such as the symbols of approval or prestige. Persons that give much to others try to get much from them, and persons that get much from others are under pressure to give much to them. This pro- cess of influence tends to work out at equilibrium to a balance in the exchanges. For a person in an exchange, what he gives may be a cost to him, just as what he gets may be a reward, and his behavior changes less as the difference of the two, profit, tends to a maximum.

This interaction between two actors (people, firms, etc.) results in var- ious contingencies, where the actors modify their resources to each other’s expectations (Emerson, 1962). According to Cook (1977), social exchange is the voluntary transfer of resources between multiple actors. In the context of this research, SET has been applied for the exchange between restaurants and its customers. This exchange takes the form of three business models of offering wireless internet access in restaurants. The next section will discuss these business models.

Technology Acceptance Model (TAM)

Technology Acceptance Model (Davis, Bagozzi, & Warshaw, 1989) is a model for user acceptance of information systems. The model aims to present an elucidation of the determining factors of computer acceptance, providing a basis for tracing the impact of external factors on internal

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beliefs, attitudes and intentions. The model proposes that actual system use is determined by perceived usefulness and perceived ease of use of the technologies. Consequently, cafes and restaurants that offer Wi-Fi should make sure that their Wi-Fi usage (e.g., initial setup, connecting) is easy to use.

Wi-Fi Business Models Public WLANs

Gadh, Sridhar, and Rao (2003, p. 1) put hot spots into three categories:

explicit, implicit, and emplicit.

1. Explicit—An explicit hot spot is offered by a location owner in partner- ship with a wireless Internet service provider (WISP) to generate revenue through paid subscribers. Examples are Internet cafes that offer Wi-Fi.

Typically, the user is authenticated by the WISP, which in turn offers security to the paying customer. The WISP may provide the subscriber with roaming capabilities to its partner WISP’s hot spots (called roaming hot spots) for a fee. Today, roaming is enabled via third-party brokers that charge a fraction of the amount transacted, splitting the revenues with the location provider, the home WISP (a subscriber’s primary provider) and roaming WISP. While brokers are now able to keep part of the transaction fee, in the future, roaming may become a standard feature of explicit hot spots just like it is for cellular services.

2. Implicit—An implicit hot spot is offered free of charge to the customer with the cost being borne by the location provider. In the context of public hot spots, these are typically offered by a retail location to attract customers. An example is the Wi-Fi offered by the city of Long Beach, California to attract tourism. Such hot spots typically aren’t secured and don’t require authentication and therefore could become a source of intrusion or Internet attacks possibly resulting in liability for the location provider. When offered within an enterprise, implicit hot spot investments are justified by potential increased employee productivity, reduced costs, increased revenues and provision of additional services to customers.

In general, these tend to require user authentication and typically offer security as well. While Wi-Fi networks of today are mostly for data use, within a few years, such networks could fill a variety of enterprise needs.

For example, a hotel may be able to integrate the following range of functions within a single wireless LAN infrastructure, thereby achieving significant savings in the following ways:

● Get WLAN-based Internet access from the hotel room

● Send faxes or print wirelessly from the hotel room

● Make wireless voice over WLAN phone calls from room to room in a peer-to-peer mode

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● Use the WLAN infrastructure to make calls both inside and outside the hotel while roaming through the hotel

● Allow the ability to view movies played on a remote DVD connected via a hotel WLAN network

● Watch television over the WLAN infrastructure (which would require higher bandwidth than is currently available)

● Access games available for playing wirelessly within the rooms

● Eventually wireless-enabling all devices using digital content (including vending machines, safes, etc.).

Needless to say, there are several technical and business challenges to be addressed prior to successful implementation, but Wi-Fi-based hotels could offer significant cost savings by coupling an integrated infrastructure with technological appeal to more sophisticated customers.

3. Emplicit—Perhaps a majority of hot spots lie between the explicit and implicit categories, in which the business needs of the location owner are linked to those of the WISP. These are being called emplicit hot spots.

An example is a hot spot within a cafe that is serviced by a WISP, where purchase-based concessions by the cafe could apply to hot spot usage by the customer or online coupons offered by the WISP would apply to discount purchase of goods within the cafe. Authentication allows the vendors to link usage by the customer to their sales activity. For example, in 2003 McDonald’s announced that it will offer Wi-Fi for a fee or will offer Wi-Fi for a customer purchasing specific food items from its restaurants. While each of these categories of hot spots offers tremen- dous opportunities, they still have major challenges ahead of them such as managing the access and security.

Problem Statement

The number of Wi-Fi-enabled devices and its uses are increasing. More consumers are demanding Wi-Fi service in the public places they visit and patronize (Umali, 2004). The hospitality industry is a main attraction for Wi-Fi users (Starkov, 2003). Some restaurants offer Wi-Fi services for free or for a fee. However, there is little research on the impact of Wi-Fi service in restaurants on customers’ likelihood to return to a restaurant. In other terms, there is a need to understand if Wi-Fi service is determinant of customers’

return to a restaurant.

Based on the above, the following regression model was hypothesized for this study:Ha=there is a significant relationship between “Wi-Fi service availability,” “Wi-Fi service quality,” “cost of Wi-Fi service,” “perceived risk of using Wi-Fi service,” “perceived health risks of Wi-Fi at a restaurant,” “use of encryption while using Wi-Fi at a restaurant,” and “perceived value of Wi-Fi,” and the likelihood of return to a restaurant.

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METHODOLOGY Questionnaire Instrument

A descriptive cross-sectional, online survey research design was formulated, and data analysis techniques were selected. The survey was developed as a self-administered instrument in five sections based on review of the literature. A qualifying question was employed. The respondents were asked if they were familiar with wireless networks (Wi-Fi). If they answered this question as “No,” then they were asked to exit the survey. The first section asked questions related to the respondents’ Internet usage behavior, such as how often they use the Internet, how long they stay online, and their purpose for Internet usage (Cobanoglu, 2001). The second section consisted of questions related to general Wi-Fi use. The third section listed statements related to Wi-Fi usage in restaurants. In this section of the survey partici- pants were asked to rate their level of agreement with the statements using a five-point Likert scale response format (1=Strongly Agree, 2=Agree, 3= Somewhat Agree, 4=Disagree, 5=Strongly Disagree). The fourth section of the questionnaire asked respondents about the impact of Wi-Fi when choos- ing restaurants. The final section of the survey consisted of demographic questions concerning gender, marital status, age, educational background, annual income, and profession. The dependent variable was “the likelihood of return to a restaurant.” This variable was measured and included in the survey with the question: “How likely are you to come back to this restaurant in the future?” The study employed seven independent variables: “Wi-Fi service availability,” “Wi-Fi service quality,” “cost of Wi-Fi service,” “perceived risk of using Wi-Fi service,” “perceived health-risks of Wi-Fi at a restaurant,”

“use of encryption while using Wi-Fi at a restaurant,” and “perceived value of Wi-Fi.” A multiple regression was used to examine the impact of Wi- Fi service on the likelihood of return to a restaurant. Multiple regression is a “method of selecting variables for inclusion in the regression model that starts by selecting the best predictor of the dependent variable” (Hair, Anderson, Tatham, & Black, 1998, p. 147). Additional independent variables can be selected in terms of the incremental explanatory power they can add to the regression model. Tabachnick and Fidell (2001, p. 117) suggest a formula for calculating sample size requirements for a multiple regression analysis. Using the formula, (N>50+8m,where:m=number of independent variables), we found that the required sample size for this study should be greater than 106.

Multi-collinearity among independent variables is a common problem with using a multiple regression model (Hair et al., 1998). To address this problem, Hair et al. (1998) suggests examining variance inflation factors (VIF) and tolerance value to determine the correlation between independent variables. The VIF and tolerance value report the degree to which each independent variable becomes a dependent variable and is regressed against

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the remaining independent variables. Small tolerance values and high VIF denote high collinearity. According to Hair et al. (1998), a common cutoff threshold is a tolerance value of .10, which corresponds to a VIF value above 10. None of the independent variables had a VIF value more than 10.

Sampling

A random sample of 1,000 American consumers was selected from a national database company (Rent-A-List.com). A total of 257 responses were achieved with a 25.7% response rate. However, 37 of them were eliminated from the study because they were not familiar with Wi-Fi, or did not use Wi-Fi. This yielded a net sample of 220, which is larger than what Tabachnick and Fidell (2001) suggest. A Cronbach’s Alpha of .872 was calculated on seven independent variables on average. A Cronbach’s Alpha score of .70 was deemed appropriate by Hair et al. (1998).

FINDINGS

Sixty-seven percent of the respondents were female while 33% were male.

About 30% were between 35–44 years old, 24.5% were between 45–54, 23.7% were between 25–34, and 10% were between 18–24 years old. Twenty percent of the respondents finished high school, 35.2% had some college education, 14.8% had associate degrees, 19.1% had bachelor’s degrees, 3.9%

had master’s degrees, and 1.2% had doctoral degrees. Forty-two percent of the respondents were single, divorced, or widowed; 46.3% were married with children; and 11.3% were married with no children. About 30% of the respondents do not use Wi-Fi on a regular basis while 23.35% use Wi-Fi once a day. About 18% use Wi-Fi several times a week. Findings represent that approximately 27% of respondents always have accessibility to a Wi- Fi network, whereas about 23% have no regular access to a Wi-Fi network.

In general, respondents have accessibility to a Wi-Fi network. One may claim that respondents who have access to Wi-Fi would want to be connected to the Internet on the go; as a result, they would want to connect to the Internet via their Wi-Fi enabled devices in cafes and restaurants.

Results showed that about 70% of respondents own a Wi-Fi enabled device. About 7% of respondents own four or more Wi-Fi enabled devices.

Since most of the respondents own a Wi-Fi enabled device, they would want to use them in any setting. The price of chipsets for Wi-Fi continues to drop, making Wi-Fi a more economical networking option. In the course of time, more people will own a Wi-Fi enabled device (Umali, 2004). About 61%

of the respondents use a Wi-Fi connection for a multitude of things. The findings brought to light that respondents use Wi-Fi not for a single purpose

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but rather for a variety of purposes. Only 3.57% of respondents use Wi-Fi solely for academic and education purposes.

About 70% of the respondents agreed or strongly agreed that they pre- ferred restaurants and cafes with Wi-Fi hotpots. The results of the findings indicate that about half of the survey respondents think that it is safe and secure to utilize Wi-Fi in public places; on the other hand, about 10% of respondents strongly disagreed with the statement. Respondents agreed the most with the statement that the future of communications is wireless (M = 1.43). Similarly, they also agreed that they use encryption while using Wi-Fi.

Results point out that respondents choose restaurants and cafes that have Wi-Fi hotspots; one may claim that there is an opportunity for restaurateurs since installing that hotspots are relatively cheap.

The findings reveal that majority of the respondents agreed that wireless Internet service is very important to them with only 13% of the respondents stated that wireless Internet is not important to them. Additionally, other studies showed that wireless Internet service plays an important role in people’s life (Umali, 2004; Starkov, 2003). Roughly 39% of respondents agreed that wireless Internet in a cafe or restaurant is not important. However, our findings attest that approximately 61% of respondents agreed that Wi-Fi is somewhat important in a cafe or a restaurant whereas 20.52% of respondents gave a thumbs-up to the importance of Wi-Fi in a cafe or restaurant. Also, about 16% of respondents agreed that wireless Internet is very important in a cafe or restaurant. The findings also revealed that respondents are likely to see Wi-Fi in cafes and restaurants.

Hypothesis Testing

To test the hypothesis, a multiple regression was used to determine the impact of Wi-Fi service on customers’ frequency of visit to a restaurant. The regression model for the impact of Wi-Fi service on customers’ likelihood to visit a restaurant was proposed as follows:

Ys=β0+β1X1+β2X2+. . .β7X7, where

Y_s =the likelihood of return to a restaurant β0 =constant (coefficient of the intercept) X₁ =Wi-Fi service availability

X2 =Wi-Fi service quality X₃ =cost of Wi-Fi service

X4 =perceived risk of using Wi-Fi service

X5 =perceived health-risks of Wi-Fi at a restaurant

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X₆ =use of encryption while using Wi-Fi at a restaurant X7 =perceived value of Wi-Fi

The summary model results for the multiple regression are shown in Table 1.

Table 1 reports the results of the regression analysis. The coefficient of determination (R² = 0.536 and Adjusted R² = 0.523), shows that the seven variables give an acceptable result in predicting the variance of the restaurant customers’ likelihood of returning to a restaurant. AnR²of between 0.50 and 0.60 is considered acceptable (Lewis, 1985). TheF-ratio, which has a value of 41.044, suggests that the regression model we have adopted could not have occurred by chance. The results revealed that four variables remained significant in the equation with a different value of the beta coefficients, thus con- tributing different weights to the variance of restaurant customers’ likelihood of returning to a restaurant. The explanatory power of the model is good.

Given the coefficient of the significant independent variables, the regression equation for the return model can be written as follows (see Table 1):

Y=.436 +.297 (Wi-Fi availability)−.186 (perceived risk of using Wi-Fi)

−.218 (cost of Wi-Fi access in restaurants) +.160 (perceived value of Wi-Fi).

The first variable that is significant is “Wi-Fi availability” (p < .001), indicating that there is a positive relationship between “Wi-Fi availability”

TABLE 1 Multiple Regression Results Goodness-of-fit

MultipleR=.732|R²=.536|AdjustedR²=.523|Standard Error=1.030 FRatio=41.044|SignificanceF =0.000

Coefficients^a

Unstandardized coefficients

Standardized coefficients

Model B

Std.

error Beta t Sig.

1 (Constant) .436 .256 1.703 .090

Wi-Fi availability .297 .062 .323 4.751 .000^∗

Cost of Wi-Fi access in restaurants −.218 .069 −.220 −3.138 .002^∗ Perceived risk of using Wi-Fi service −.186 .065 −.175 −2.840 .005^∗ I use encryption while using Wi-Fi −.056 .049 −.061 −1.133 .258 I do not mind paying for Wi-Fi access −.004 .074 −.003 −.051 .959

Perceived value of Wi-Fi .160 .063 .146 2.522 .012^∗∗

I am concerned with radiation from Wi-Fi usage.

−.003 .060 −.059 −.067 −.759 Note: Dependent variable: How likely will you come back to this restaurant in the future?

∗p<=0.01,^∗∗p<=0.05.

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and the likelihood of returning to a restaurant. In other words, one unit of increase (on a five level scale) in “Wi-Fi availability” variable would lead to a .297 unit increase in the likelihood of returning to a restaurant. The other variable that is significant is “perceived risk of using Wi-Fi” (p =.005), indicating that there is a negative relationship between “perceived risk of using Wi-Fi” and the likelihood of returning to a restaurant. In other words, one unit of increase in “perceived risk of using Wi-Fi” would lead to a .186 unit decrease in the likelihood of returning to a restaurant. Similarly,

“cost of Wi-Fi access in restaurants” was negatively related to likelihood of returning to a restaurant (p =.002).

One unit of increase in “cost of Wi-Fi access in restaurants” would lead to a .218 unit decrease in the likelihood of returning to a restaurant.

Finally, “perceived value of Wi-Fi” was positively related to the likelihood of returning to a restaurant (p=.012). One unit of increase in “perceived value of Wi-Fi” would lead to a .160 unit increase in the likelihood of returning to a restaurant.

CONCLUSIONS AND RECOMMENDATIONS

This study suggests that providing Wi-Fi service at restaurants is positively correlated with a customer’s intention to return to a restaurant. This finding makes more sense particularly considering that 70% of the respondents utilize a Wi-Fi enabled device. It is natural that they would want Wi-Fi access for their Wi-Fi-enabled devices including laptop computers and personal digital assistants. The number of consumers that own dual mode telephones (i.e., iPhone) that allow phone conversations to be conducted over the Wi-Fi network for cheaper or for free is increasing. In the future, it is likely that the demand for Wi-Fi access in public places such as restaurants will only increase.

This study also suggests that there is negative correlation between the cost of Wi-Fi access in restaurants and the intention to return. This means that as the cost of Wi-Fi access at a restaurant increases, customers are less likely to return to that restaurant. Wi-Fi service should be offered to customers either free of charge or restaurants may use a business model where they do not charge for Wi-Fi service but may require a purchase to allow free access to Wi-Fi. This way, the cost of the service will be satisfied.

The data also shows a positive correlation between perceived value of Wi-Fi and intention to return to a restaurant.

There is a negative correlation between the perceived risk of using Wi- Fi at a restaurant and the intention to return. Free Wi-Fi is often an extension of the brand image, therefore keeping it secure is of vital importance. Many quick service restaurants have recently have invested in the SecureConnect Wi-Fi hotspot solutions. In addition, users should use tools such as a secure

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socket layer and virtual private networks that will enable security while using Wi-Fi. However, restaurants may offer secure Wi-Fi access for their loyal customers. The most common Wi-Fi security is Wi-Fi Protected Access which is an advanced encryption method for Wi-Fi access.

The overall results show strong support for the impact of providing Wi- Fi access at restaurants on the likelihood of return to a restaurant. Restaurant owners and operators may do well by reviewing the results of this study and as a result revise their high speed Internet offering strategy.

The limitation of this study is the small sample size; therefore, the results may not be generalized beyond the population. Future studies may employ a larger sample, not only in the U.S. but also in international markets. In addition, future studies may examine the impact of Internet access in hotels on customer intention to revisit. Finally, another study may examine the impact of city-wide Wi-Fi services such as Philadelphia on businesses including restaurants and hotels.

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