RESEARCH IN BRIEF

Alternative models framing UKindependent hoteliers’ adoption

of technologyWai Mun Lim

Plymouth Business School, University of Plymouth, Plymouth, UK

Abstract

Purpose – While there is a plethora of literature examining the antecedents affecting technologyadoption decision, there have been limited investigations into the various stages of technologiesadoption by hoteliers. This paper aims to examine two established theoretical paradigms jointly,facilitating an understanding of not only the antecedents affecting technology adoption but also thehoteliers’ intensity of technology adoption.

Design/methodology/approach – The development of Davis’s Technology Acceptance Model(TAM) will be explored, from its adaptation of the Theory of Reasoned Action (TRA) to the Theory ofPlanned Behaviour (TPB). Following which, Roger’s Diffusion of Innovation will be discussed andwhether the concepts should jointly be explored so as to provide a more comprehensive elucidation ofhoteliers’ internet technologies adoption decisions.

Findings – The literature has corroborated that the TAM is effective in evaluating the concept of theuser’s perception of technology use by including the construct of internet applications’ usefulnessregardless of innovation intensity. Owing to the perpetual proliferation of internet technologies, theinvestigation of hoteliers’ propensity to adopt internet technologies could be enhanced with theinclusion of the various levels of internet applications that are adopted. Rogers’ diffusion of innovationparadigm helps to address this problem.

Practical implications – The concepts discussed here could help practitioners to become moreaware of the factors that drive their internet technologies adoption. Academics could advance thepaper’s discussion of internet technologies adoption to other sectors of the tourism and hospitalityindustry.

Originality/value – The paper provides insight into the use of broader theories in understandingtourism and hospitality management phenomena. It is expected that academics would develop thediscussed concepts further in order to create a wider awareness of how the industry responds tointernet technologies at various stages.

Keywords Hotels, Internet technologies, TAM, Innovation, Diffusion

Paper type Research paper

IntroductionA hotel’s selection of electronic marketing channels can vary in degrees of complexity.This occurs because hotels have bedrooms that differ in style and comfort, especially inthe case of higher end unaffiliated independent hotels. These hotels are now presentedwith more marketing channels via the internet, which may or may not be aresemblance to the traditional marketing structure. For instance, an online site maysolely promote only independent hotels, or it would only market and sell

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Received 28 January 2008Revised 3 March 2008,20 May 2008,30 August 2008Accepted 15 October 2008

International Journal ofContemporary HospitalityManagementVol. 21 No. 5, 2009pp. 610-618q Emerald Group Publishing Limited0959-6119DOI 10.1108/09596110910967836

accommodation that also has an in-house award-winning restaurant. The complexityand variety of products offered by these hoteliers contribute to the variety of channelsthat are available to hoteliers today.

Given that marketing and distribution revolve around institutions and agenciesparticipating in the process of “making a product available to the end-user, dependenceis a crucial concept in channels research” (Kumar et al., 1995, p. 351). Independenthotels differ in their levels of operational complexity and it is therefore often difficultfor them to ignore the internet as another potential marketing tool. Furthermore, anindependent hotel often has limited marketing and distribution resources compared tothe larger chains; where the latter are often able to present their hotel property invarious sales and distribution channels that are perceived to be the most productive,whether they are print ads, direct mail, public relations, sales call or electronicdistribution channels. Only the most cost effective and compelling channels will beallocated the limited resources that will in turn bring in guests and improve occupancy(Burns, 2000). This paper will therefore explore a combination of concepts that havebeen tested in other industries and sectors but have yet to be explored within thecontext of UK independent hotels.

ConceptsThere is a plethora of literature on the constructs of the internet applications adoptionmodels. Of particular significance was Davis’s (1989) technology acceptance model(TAM) which emphasized the importance of perceived ease of use and perceivedusefulness in influencing technology adoption decisions (Venkatesh and Davis, 1996;Poku and Vlosky, 2004; Karahanna and Straub, 1999; Malhotra and Galletta, 1999).

Perceived usefulness (PU) has been defined as the extent to which an individualbelieves that by adopting a particular technology, it would improve his or herperformance (Davis, 1989). The notion of “perceived ease of use” (PEOU) on the otherhand illustrates the individual’s perception of how much effort is required to use anadopted innovation. After all, the use and adoption of the internet entails a certain levelof expertise in the technology (Ranchhod and Gurau, 2000). The main motivator in theuse of technology is perceived usefulness (instead of perceived social pressure) (Igbariaet al., 1996). Both PU and PEOU therefore serve to inform the attitude a user takes onand this attitude can be defined as the user’s perceived appeal to the adoption of atechnology.

From this point on, the attitude developed by the user will influence his/herbehavioural intention to use the system. Actual use by the system is then predicted bybehavioural intention (Malhotra and Galletta, 1999). In general, the TAM has beenproven to be successful in predicting about 40 per cent of a system’s use (Ajzen andFishbein, 1980; Legris et al., 2003). While the TAM is an effective tool in explaininghow and why businesses accept and adopt technology, the model does not take intoaccount the continual increase in internet applications available to industries. It wouldbe beneficial for both academics and practitioners to explore the antecedents that affectvarious stages of internet technologies adoption. This would help clarify why someindependent hoteliers tend to adopt more or less internet technologies than the rest.The following section will first explore the basis of TAM followed by an examinationof how diffusion of innovations could be examined jointly to achieve the objective.

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Development of technology acceptance model (TAM)The TAM was adapted from the fundamental construct of the Theory of ReasonedAction (TRA) developed by Fishbein and Ajzen (1975). It examines the attitudinaldeterminants of predicting and understanding human behaviour. This includes the“determinants of behaviour and relations among beliefs, attitudes, subjective norms,intentions, and behaviour” (Igbaria et al., 1996, p. 227) as they play important roles ininfluencing an individual’s decision to use technology (Poku and Vlosky, 2004). Moresignificantly, Sheppard et al.’s (1988) meta-analyses investigating the TRA’seffectiveness in marketing showed that the TRA “has a strong predictive utility,even when utilized to investigate situations and activities that do not fall within theboundary conditions originally specified for the model” (p. 327).

Within the context of this study, the theory predicts the intention to carry out abehaviour based on the decision-maker’s attitude towards that behaviour, rather thanby the decision-maker’s attitude towards an internet application per se. The theoryfurther posits that a decision maker’s plan to perform a behaviour may be influencedby the normative social beliefs held by the decision maker. For example, “a decisionmaker might have a very favourable attitude toward having a drink before dinner at arestaurant. However, the intention to actually order the drink may be influenced by thedecision maker’s beliefs about the appropriateness (i.e. the perceived social norm) ofordering a drink in the current situation (with friends for a fun meal or on a jobinterview) and her/his motivation to comply with those normative beliefs” (Hawkinset al., 2001 in Hansen et al., 2004, p. 540). Conversely, the same applies for an hotelierwho is deciding to adopt an internet application. The hotelier may have a veryfavourable attitude towards marketing the hotel via the local travel guide, but theintention to advertise again with the guide in the forthcoming year, may be influencedby the hotelier’s beliefs about the suitability of the mode, in light of an array of onlinemarketing channels and his/her motivation to comply with competitors’ actions and/orbeliefs.

Applied to internet application decisions, the TRA states that the immediateantecedent of the decision to apply is the intention to adopt an internet application(internet application intention). An internet application in turn is predicted by theextent to which the hotelier evaluates adopting the internet application positively ornegatively (adoption attitude), and the perception of social pressure to adopt theapplication (subjective norm). Therefore, according to the TRA, hoteliers are morelikely to adopt an internet application if they have a positive rather than a negativeevaluation of adopting an application. They will be further inclined to do so if theyexperience positive social pressure from others relevant to the industry to do so (thesesignificant others could be competitors, clients, suppliers etc.).

Extending the TRA, Ajzen (1985) proposes the theory of planned behaviour (TPB),which is about the perceived difficulty to perform the behaviour of interest (Van-Hooftet al., 2006). The highlight of TPB is the belief in the presence of factors that mayfurther or hinder the behaviour of interest (Bamberg et al., 2003). In contrast, the TRAis concerned with behaviours that an individual has control over (Hansen et al., 2004).This theory was widely disputed because, as was observed by Sheppard et al. (1988)there are actions undertaken that can result from factors that are beyond anindividual’s control, which fall outside the conditions of the model. For instance, an

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hotelier may be prevented from adopting an internet application if the hotelierperceives the adoption process as too difficult, or if the hotelier does not have thefinancial or human resources required to carry out the planned behaviour. The TPBincludes the consideration of factors that are beyond an individual’s control therefore,“perceived behavioural control” (PBC) as a determinant of behavioural intention isadded to the TRA to shape the TPB.

The additional element of perceived behavioural control is important to the studybecause it allows for the consideration of other elements such as the perceived costsand perceived competitive marketing intensity (in questionnaire).

In summary, the TAM is an adaptation of the TRA while the TPB is an extension ofit, there are however two key differences between the TAM and the TRA/TPB. First,the TAM does not include the consideration of the subjective norms construct (found inthe TRA) as it was found to be insignificant in one of Davis’s later studies (Davis,1989). Second, the behavioural construct (found in the TPB) is also not included in theTAM because specifically, behavioural control has had limited importance in relationto technology usage behaviour (Dishaw and Strong, 1999). However the TAM includesthe very important assumption that users’ behaviour is voluntary or at the discretion ofthe user (volitional) which also partially explains the exclusion of both the subjectivenorm and behavioural constructs in the model. While the independent hoteliers’adoption of internet applications may be voluntary, TAM does not help to explain theuse of internet technologies spread among its users.

Understanding technology adoption using diffusion of innovationAs the adoption of varying internet applications increase, the diffusion of internetapplications adoption in different sectors has also been a subject of interest forinvestigation (Foley and Samson, 2003). While TAM has been found to be effective inevaluating the concept of user’s perception of technology use and acceptance, byincluding the usefulness of internet applications regardless of innovation intensity, itcould perhaps prove to be a better enhanced model with the explicit inclusion of thevarious levels of internet applications adopted. Rogers’ diffusion of innovationparadigm helps to address this gap.

According to Knol and Stroeken (2001), adoption and diffusion are separateconcepts that occur at very different levels, as adoption “takes place at the level of theindividual adopting unit and at the micro-economic level (p. 228)”. However, theproliferation of internet use in expanding distribution has meant that the diffusion ofvarying types of internet use is gradually being perceived as the norm. The concept ofdiffusion can be described as “the process by which an innovation is communicatedthrough certain channels over time among the members of a social system” (Rogers,1995, p. 5). More specifically the diffusion of internet applications across the hotelsector could be explained by Rogers’ diffusion of innovations decision process. Rogers’diffusion of innovations construct has some semblance to the TAM as it recogniseswithin the innovation-decision process model that the process is where “an individualpasses from first knowledge of an innovation, to forming an attitude toward theinnovation, to a decision to adopt or reject, to implementation of the new idea, and toconfirmation of the decision” (p. 163). It was also noted in Moore and Benbasat’s (1995)paper that perceptions which are hypothesized to have an effect on attitude are also

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classified as perceived characteristics of innovations; where both were found to belinked with adoption or rejection decisions.

Moreover, Rogers has also specifically stated that the diffusion of innovationsmodel is the “process and rate at which various groups of individuals adopt an idea orinnovation in a given society (Shea et al., 2004, p. 146)”. In short, Rogers’ diffusionmodel could be directly applied to the independent hotel sector as a social system as italso recognises that “innovation” changes over time. In this context, internetapplications could be adopted or un-adopted at various stages of a hotel’s operation.The decision to adopt or reject an application occurs within the social system wasdefined by Rogers (1995, p. 23) in the innovations context “as a set of interrelated unitsthat are engaged in joint problem-solving to accomplish a common goal”. The systemanalysed in this diffusion study could therefore consists of independent hoteliers in theUK, where each hotelier in the social system can be differentiated from other units.However, the model includes a clause which states that the structure of a social systemcan facilitate or impede the diffusion of innovations in a system, although it is alsoacknowledged that it is very complex to separate the members of the system from thestructure of an individual’s characteristics.

A possible scenario in the said structure within the UK independent hoteliers sectoris in the example of the location of a hotel, whether it is located provincially or in thecity. A hotel that is located in a city is more likely to adopt new internet applicationsmore readily compared to a provincial hotel (Lim, 2007). This could be due to factorsranging from easy access to new technology (after-all not too long ago, getting a highspeed connection was a lottery of geographic location as one had to be within aparticular catchment area of a cable company (Wearden, 2006)) to the availability of aconcerted destination marketing strategy within the hotel’s locale.

Furthermore, the groups of individuals who adopt a form of system use (or adoptergroups) are then categorized as innovator, early adopters, early majority and laggardsor non-adopters (Rogers, 1995) and each of these groups can be further characterizedby a whole range of social factors, e.g. personal or organisational characteristics,socio-economic status etc. With these adopter categorizations, the criterion ofinnovativeness has to be met, although it was acknowledged in Rogers’s (1995) workthat such a classification is only a generalization to primarily understand humanbehaviour.

These categorizations of adopter categories reflect the fact that internet applicationsare adopted over a period of time, because not all hotels in an industry adopt aninternet application or a number of applications at the same time. The time seriespartially helps to explain the diffusion effect, as it is the degree of collective influenceon an individual to adopt. While the number of individual adopter grows, the next levelof adopter categorization ensues, allowing for diffusion to take place.

However, as pointed out by Shea et al. (2004) the diffusion effect also depends on thedegree of interconnectedness among the players of an industry. Communication stirsthe interconnectedness and concurrently spurs the diffusion of information for a newsystem or internet application. According to Rogers (1995), diffusion in the originaldiffusion model was largely based on the physical and geographic proximity ofindividuals, but in contrast, communication via electronic mail which uses internet

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technology allows information to spread more quickly in a geometric manner becauseof the breadth of interconnectedness between individuals and groups (Wilson, 2000).

ConclusionIt has long been established that conceptual models based on literature could helpresearchers to link science to the real world, enabling findings of scientific research tobe put into better use in practice (Sagasti and Mitroff, 1973). A number of implicationshave been manifested in this investigation.

While diffusion and adoption behaviours are separate concepts that could beexamined independently, they should nevertheless be examined simultaneously inorder to obtain a complete understanding of the complexities of internet applicationswithin the hospitality industry. Past examples of studies integrating the two includeGinzberg’s (1981) use of the diffusions of innovations model to examine the adoption ofinformation systems, and Lucas et al’s (1990) reference to the TRA and diffusionmodels when describing their adoption model. More recently, Sigala et al. (2000)integrated both innovation and adoption theories in the investigation of the diffusionand application of multimedia technologies in the tourism and hospitality industries,where the diffusion of the medium was found to be rampant across the two industries.

Evidence of the complexity in understanding the adoption of information,communication and technology (ICT) by small medium sized hotel enterprises (SMHE),was further strengthen in Murphy’s (2004, p. 514) attempt to build a model portrayingthe diversity of diffusion of information and communication technologies in thehospitality sector. She confirms that the adoption of technology is “far from being asimple stage by stage progression”. As her table reveals, the adoption of ICTs bySMHE is not only influenced by internal factors, but by uncontrollable external factors.At each stage of the computer era, an estimate of the time frame is presented togetherwith examples of more common IT applications of the era, and an indication of whatthe general external environment was like, in terms of marketing, funding and humanresource issues. This supposition is in line with Ajzen’s (1985) Theory of PlannedBehaviour – the element of “perceived behavioural control”.

An extensive number of past studies have also suggested that core variables foundin TAM, such as perceived affordability of adopting a technology, perceived usefulnessand perceived ease of use are paramount to the consideration of technology adoptionand deployment (Garces et al., 2004). As the operators’ growing awareness of newopportunities to compete with larger organisations (hotels) on an international scaleand on equal terms (Buhalis, 1998; Christian, 2001; Wardell, 1998), the adoption ofinternet technologies within the industry has evidently intensified. Future studiescould adopt TAM combined with the understanding of Rogers’ model (identified as anexcellent general practitioners’ guide by Ellsworth (2000)) so as to enable changeinitiators to learn about the features they can build on with the provisions ofinnovation, and to facilitate its acceptance by the targeted adoption category.

The theoretical implications of aligning both the Technology Acceptance Model andRogers’ Diffusion of Innovation could facilitate the identification of adoptercharacteristics in each of the adopter categories. The investigation of TAM’svariables could be used to enhance the understanding of how hoteliers adopt internettechnologies. Although the “perceived ease of use” and “perceived usefulness” have

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been examined in past studies, new measures of technology acceptance such as“perceived affordability” could be added to the framework of investigation.Determining the importance of such variables may help researchers to analyse if thedifferences in perceptions (of selected variables) could help to categorise adopters (ornon-adopters) into different categories. These findings may then be examined inalignment with Rogers’ adopter categories, not only further illuminating thecharacteristics of adopters, but adopters at various stages of technology adoption too.

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Corresponding authorWai Mun Lim can be contacted at: wmlim@plymouth.ac.uk

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