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Annals of Tourism Research, Vol. 32, No. 4, pp. 905–924, 2005 Ó 2005 Elsevier Ltd. All rights reserved. Printed in Great Britain 0160-7383/$30.00

doi:10.1016/j.annals.2004.07.012

TESTING THEORY OF PLANNED VERSUS REALIZED TOURISM BEHAVIOR Roger March University of New South Wales, Australia Arch G. Woodside Boston College, USA Abstract: This article probes how well one’s plans for doing, buying, and consuming discretionary tourism services relate to what is actually done. Using group level data, it includes an empirical study of hypotheses comparing planned and actual consumption behaviors. The main propositions tested are that realized consumption behaviors are greater in number than planned and that the level of matching between planned and realized actions varies as a function of contingency factors of composition of the tourist group, product experience, and motivations. Data from two large-scale surveys serve to examine the theory. The findings support the hypotheses partially and provide guidance for planning survey research and marketing management strategies. Keywords: consumer plans, services, unplanned behavior, experience. Ó 2005 Elsevier Ltd. All rights reserved. Re´sume´: La mise a` l’essai d’une the´orie pour comparer les comportements touristiques planifie´s et re´alise´s. Cet article examine a` quel degre´ les projets pour faire, acheter et consommer des services discre´tionnaires du tourisme se rapportent a` ce que l’on fait vraiment. L’article utilise des donne´es de niveau groupe et comprend une e´tude empirique des hypothe`ses pour la comparaison des comportements de consommation projete´e et re´elle. Les principales propositions qui sont mises a` l’essai sont que les comportements de consommation re´alise´e sont plus nombreux que ceux qui avaient e´te´ projete´s, et que le niveau de correspondance entre les actions projete´es et re´alise´es varie en fonction des facteurs de contingence de la composition du groupe touristique, de l’expe´rience du produit et des motivations. Des donne´es de deux sondages a` grande e´chelle servent pour examiner la the´orie. Les re´sultats soutiennent les hypothe`ses en partie et fournissent des conseils pour la planification des recherches par sondage et des strate´gies de gestion de marketing. Mots-cle´s: projets de consommateurs, services, comportements impre´vus, expe´rience. Ó 2005 Elsevier Ltd. All rights reserved.

INTRODUCTION Models of consumer behavior typically predict intention (or purchase decision) as the immediate antecedent of purchase (Engel, Blackwell and Miniard 1993; Howard and Sheth, 1969; Peter and Olson

Roger March is Senior Lecturer in the School of Marketing, University of New South Wales (Sydney 2052, Australia. Email ). His tourism research interests include international distribution systems, Japanese behavior, and unethical issues. Arch Woodside is Professor of marketing at Boston College. He is a Fellow of Royal Society of Canada, Society for Marketing Advances, American Psychological Association, and the American Psychological Society. 905

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1999). The resulting implication is that intention and subsequent consumption behavior are theoretically indistinguishable. Similarly, behaviors available within a given environment that are unplanned, unintended, are not conceptualized in consumer behavior models. Foxall labels marketing theory’s aversion to the study of unplanned and impulsive behavior as ‘‘pathological’’ (2000:93). The present article’s objective is to bridge this empirical gap and offer insights into the similarities and differences between consumers’ planned and actual purchase and consumption behaviors. The empirical research setting examined focuses on vacation destination behavior. Using a between subjects quasi-experiment (Cook and Campbell 1979), the field study examines consumption behaviors that respondents plan to undertake, as reported in an entry survey to the destination, and the behaviors undertaken, as reported in an exit survey to the same destination. The study investigates several behaviors (length-ofstay, spending, and number of activities undertaken) and examines effects of contingency influences (group composition, product experience, and motivations) on the differences between planned and realized length-of-stay and spending. The field study reported here is not the conventional approach to planned and actual behaviors. Previous research into intentions and consumption overwhelmingly focuses on planned behaviors, or intentions, and specifically with two aims: to improve the use of intention measurement in its predictive power of future behavior and to influence purchasing. Though a multitude of factors and situations interfere or constrain an individual’s ability to act upon his or her intentions (Belk 1974, 1975; Filiatrault and Ritchie 1988), intention is still an important construct found to relate significantly to actual behavior.

COMPARING INTENTIONS AND ACTUAL BEHAVIOR The extant literature includes substantial empirical research into the relationship between planned purchases and actual consumption. Typically, these studies aim to measure intentions for the purpose of predicting future consumption behavior. The US government conducted studies and experiments concerning purchase intentions between the 40s and 70s (Young, DeSarbo, and Morwitz 1998). Many of these studies report significant relationships between intentions to buy durable goods and subsequent purchase, using various econometric models on panel data (Juster 1966; Tobin 1959). Kalwani and Silk (1982) demonstrate that factors such as type of product, type of measurement scale, time from measurement of intent until actual behavior, and recency of the previous purchase influence the intention-behavior relationship. Many studies examine the relationship between purchase intentions and behaviors for durable goods (Clawson 1971; Ferber and Piskie 1965) and nondurable ones (Gormley 1974; Tauber 1975; Warshaw 1980). Young, DeSarbo and Morwitz conclude, ‘‘Overall, based on empirical evidence, intentions appear to almost always provide biased measures of purchase propensity, sometimes underestimat-

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ing actual purchasing and other times overestimating actual purchasing’’ (1998:189). Studies often focus on the predictive powers and accuracy of intentions. Most models of consumer behavior incorporate intentions as an important predictor variable to forecast sales (Kalwani and Silk 1982; Morwitz and Schmittlein 1992). Few distinctions are made between buyer intentions and actions. Situational variables are used to rationalize divergences between intentions and behavior. In the words of Juster, ‘‘Purchases (actions) are directly related to (or predicted by) intentions, modified by the incidence of unforeseen circumstances’’ (1964:66). This view remains both speculative and lacking in specifics—what unforseen circumstances affect intention-action gaps and the effect size of such influences need examination. This article probes the nature and size of such gaps as they relate to planning and doing tourism behavior. Lynch and Srull (1982) offer one reason for the apparent lack of investigation into the ‘‘final stage’’ in the consumer consumption process. In their view, consumer research primarily is phenomenon, as opposed to theory, driven. For marketing practitioners, particularly in advertising related fields, the predictive power of intentions to forecast future consumption behavior accurately has obvious commercial appeal. This view builds on the assumption that consumers both attending to commercial messages and making plans have reciprocal influences—intentions are worthy of study because they reflect benefit-seeking behavior that would enable destination strategists to craft effective communication messages (Woodside and Jacobs 1985). Consequently, examining consumers’ planned strategies offers unique strengths that relate especially to learning what brings tourists to a destination the first-time as well as the second and future visits. The concept of ‘‘unplanned’’ behavior is one dimension of the issue regarding the relationship between intentions and actual behavior that has been examined in marketing. Stern’s (1962) seminal article proposes four categories of unplanned purchases: ‘‘pure’’ impulse buying, characterized by a total lack of preplanning; reminder impulse buying, whereby purchases are sparked by previous personal experience or recall; suggestion impulse buying, where one sees the purchased product for the first time and buys it; and planned impulse buying, typified by a shopper entering a store with some specific items in mind, but with the expectation and intention of making other purchases dependent on such things as price and coupon specials. By the mid-80s, scholars began to deconstruct the unplanned concept, and focus on its impulse dimension (Rook and Hoch 1985; Rook and Gardner 1993). Though an impulse purchase is unplanned, it is also includes substantial complexity in terms of antecedents, consequences, and subcategories of impulse behavior. Since the 80s an increasing number of scholars have informed these impulse buying issues (Agee and Martin 2001; Beatty and Ferrell 1998; Gardner and Rook 1987; Rook and Fisher 1995; Weun, Jones, and Beatty 1998). However, the characteristics and antecedents of unplanned behavior in the broader sense remain unexplored and unknown.

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The complexity of the ‘‘unplanned’’ concept needs explication. Behavior can be unplanned yet done, either in the form of impulse buying (purchase of a chocolate bar at the supermarket check-out counter) or ‘‘unplanned purchases’’, when knowledge of and interaction with the task environment and time pressure combine to force a decision that otherwise would have been foregone (Bettman 1979). To complicate matters more, not all impulse buying may be totally unplanned. Rook and Hoch report that some people ‘‘plan on being impulsive’’ as a shopping strategy (1985:25). Cobb and Hoyer (1986) draw an interesting distinction between impulse and partial planners. While both cohorts appear to be impulse purchasers because they delay brand decisions until entering the consumption environment, impulse planners act almost entirely in a spontaneous manner, while partial planners exhibit careful insite purchase behavior by engaging in detailed search and being price sensitive. Previous research into planned, unplanned, and actual consumption was done mainly in supermarkets (Bruce and Green 1991; Kollat and Willett 1967; Prasad 1975) thus limiting the insights that can be generalized into non-supermarket contexts. Studies in the overall retailing sector, which includes malls as well as supermarkets, consistently report that a significant proportion of what is actually purchased is not planned. Moreover, in findings of particular relevance to leisure-destination research, the extent of unplanned behavior increases under the following conditions: the more that the consumption environment is unknown to the buyer (Bettman, Luce, and Payne 1998); when customers regard consumption outcomes as positive (Bagozzi and Nataraajan 2000); when few constraints exist on their time and effort (Kollat and Willett 1967); when multiple items are purchased, rather than just a few (Inman and Winer 1998; Kollat and Willett 1967); and when the overall transaction involves a large, rather than a small, amount of money (Prasad 1975). Thus, planned versus realized strategy gaps are likely to be smaller versus larger among consumers planning to stay only a few versus many nights in a destination; and among tourists on a limited expenditure budget. A large number of studies into unplanned behavior and impulse behavior quantify the extent of unplanned purchases. In contrast, few attempts have been made to quantify the differences in what is planned and what is actually purchased. Abratt and Goodey found that 41% of respondents reported that they had spent more than their expressed spending intention. They suggest, ‘‘the proposition that consumers tend to spend more than they planned may hold’’ (1990:119). For a specific destination, it is important to learn what activities are planned much more than done (if any) versus those unplanned but done frequently (if any), and what the causes and consequences of such combinations are. Explicating a theory of planned and realized strategies that helps to answer such issues is likely useful for guiding research and management practice. The tourism literature includes several relevant studies for constructing a theory for explaining planned versus realized behavioral gaps. For example, Stewart and Vogt surveyed the same tourists prior to

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and during a vacation for a number of measures, including length-ofstay, activities, accommodation, and group composition. While they found that people tend to plan more activities than they actuate, those regarding length-of-stay, group, and transport mode were ‘‘carried out as planned’’ (1999:91). For at least two reasons these results must be treated with caution. First, significance tests were not applied. Second, the same respondents were interviewed, thus creating two methodological problems: self-generated validity, whereby respondents attempt to justify their earlier expressed intentions (Feldman and Lynch 1988) and social desirability bias (Cobb and Hoyer 1986), in that impulse or unplanned purchasing is underestimated in a person’s effort to appear rational and goal oriented. Perdue (1986) touches upon the subject in an exploratory investigation seeking to empirically verify the proposition that unplanned yet realized behavior yields higher spending than the unplanned and unrealized. He reports that consumers who purchase a product not planned for are likely to express satisfaction with it as a means of justifying the purchase to themselves and other members of their group. Ajzen and Driver (1992) use leisure activities as the research setting for testing the theory of planned behavior. They found that the theory is useful in predicting influences upon intentions and actual behaviors from intentions. Their study has the limitation of being confined to college students and in being limited to five leisure activities. As Ajzen and Driver (1992) conclude, future research needs to examine other recreation activities and to use more accurate and valid reporting means. Here again, this report builds upon the previous work discussed by examining influences in a real tourism/leisure setting, with a large number of respondents and across a wide range of activities and experiences. In this context, existing models of consumer decisionmaking (Howard and Sheth 1969) focus mostly on tangible products, rather than intangible services in tourism. Its product is experiential with emotional undertones whose decision process differs vastly from the rational, problem-solving scenario applied to many tangible goods. Mayo and Jarvis (1981) argue that tourism is a special form of consumption behavior involving an intangible, heterogeneous purchase of an experiential product. As a consequence, existing models omit important realities. Um and Crompton suggest, ‘‘that perceptions of alternative destinations’ physical attributes in the awareness set . . . are susceptible to change during the period of active solicitation of information stimulated by an intention to select a travel destination’’ (1990:437). Finally, several tourism researchers argue that the benefits realized from a consumption experience may be more useful to understand than the benefits that consumers say they intend to seek (Dann 1981; Pearce and Caltabiano 1983; Shoemaker 1994; Woodside and Jacobs 1983). The present report advances the proposition that learning both benefits sought and plans made, as well as benefits realized and activities done, provides valuable information for building tourism theory of antecedents and consequences of such behavior. Research that investigates the process by which some intentions are actualized

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behavior and convincingly explains the influences resulting in unplanned as well as planned behaviors is likely to make a valuable contribution to the advancement of knowledge in the field of tourism. Exploring Consumer Plans and Behaviors The two hypotheses (as well as their rationales) relating planned and actual behaviors focus on behavior within consumers’ ‘‘tourism consumption systems’’ (Becken and Gnoth 2004; Woodside and Dubelaar 2002). A tourism consumption system is the set of related thoughts, decisions, and behaviors by a discretionary tourist prior to, during, and following a trip. ‘‘The central proposition in a theory of [tourism consumption system] is that the thoughts, decisions, and behaviors regarding one activity influence the thoughts, decisions, and behaviors for a number of other activities’’ (Woodside and Dubelaar 2002: 120). The concept is similar but still distinct from Solomon’s notion of consumption constellations. The latter are ‘‘sets of products and activities used by consumers to define, communicate, and perform social roles’’ (Solomon 1999:562). Distinct from this concept, a tourism consumption system implies the likelihood of a contingent causal chain of observable activities before and during discretionary travel, for example, the decision by a Canadian couple to visit France for two weeks might trigger a search for places to visit and accommodations. H1: Realized consumption behaviors are greater in number than planned for a set of customer activities related to a tourism consumption system.

Three contingencies common in consumer behavior and consumption plans are product experience, motivation, and in tourism, composition of the group. These were incorporated into the model as moderating variables acting upon planned and realized behaviors. First, product experience is critical when studying the dynamic choice processes of consumers new to a market (Heilman, Bowman and Wright 2000). Experience, which is the accumulation of routine and habitual buyer behavior, allows for purposive and intelligent behavior without deliberation (Katona 1975). Tourists who vacation at the same place regularly are likely to engage in little pre-arrival planning, relying instead on their accumulated knowledge and experience from previous visits (Fodness and Murray 1999). Second, motivations underlying a leisure trip are likely to have significant influences on behavior (Morrison 1996). Tourists visiting friends or relatives are more likely to rely on the advice of their hosts, less likely to use product information, and thus more likely to deviate between planned and eventual behaviors (Gitelson and Crompton 1983). Leisure tourists, on the other hand, are more likely to engage in prearrival planning by obtaining information, particularly if they are first-timers. Excitement and adventure seekers tend to look for more information and undertake more activities (Gitelson and Crompton

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1983). Hence, their planned behavior is more likely to approximate their eventual behavior. Third, in the general marketing environment, the social setting (presence or absence of others) that characterizes the consumption of a product or service influences both planned and actual behaviors, as it does other consumer behavior (Stayman and Deshplande 1989). Fisher (2001) finds that greater collaboration led to higher decision quality and smaller deviations between consumers’ planned and actual expenditures. In leisure settings, the composition of the group heavily influences the behavior of its members (McIntosh and Goeldner 1990). Leisure tourism is a product that is jointly consumed, and its activities reflect direct and indirect influences of all group members (Chadwick 1987). This phenomenon is noticeable particularly when children are present (or absent). Taking children to a destination likely requires greater planning and forethought than is required by couples or tourists going without. Therefore, groups with children are likely to plan their trip itinerary prior to, rather than after, arrival in the destination (Fodness and Murray 1999). Further, larger groups comprising friends will require greater coordination in order to meet differential needs than will couples or individuals. H2: The level of matching between planned and realized actions varies as a function of the following contingency factors: group composition, product experience, and motivations.

Research Method Two large-scale data files, from the 1992 face-to-face entry and exit surveys to Prince Edward Island (PEI), Canada, were used to investigate the research questions. The entry survey consisted of 2,239 individual interviews and the exit survey 2,362. The long-interview method (McCracken 1988) was employed for both data sets. The surveys were undertaken by the Marketing Agency (a PEI government-sponsored organization). While the data were collected over a decade ago, they provide a rare ability for comparing planned versus realized tourism behavior. Heretofore, the two data sets have never been used in a single research project. The only previous use was a government descriptive report profiling tourists’ demographics, attitudes, and behaviors (Marketing Agency 1993) and a study using only the exit interviews on the impact of PEI’s 1992 advertising campaign on attitudes, behaviors, and expenditures (Woodside, Trappey and MacDonald 1997). The data were collected using 13-page entry and 12-page exit questionnaires. The interviews were completed during the peak tourism season (May 22 to October 5, 1992), a period when over 95% of leisure tourists visit PEI. The questionnaire was administered at all points of entry and exit (ferry, airports, and cruise ships) in matching proportions to total trip visits for each travel mode. Over 93% of all tourists to PEI arrive by one of two ferries; 6% via the airport and 1% via cruiseships. The interviews

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were conducted at ferry wharves prior to boarding, at the province’s major airport near Charlottetown, and on board selected cruiseships. At the time of study, no ‘‘fixed-link’’ (bridge) existed connecting PEI to the North American mainland. A team of nine interviewers worked on three-day-on, two-day-off schedules to ensure that weekdays and weekends were covered adequately. Respondents in the exit interviews were screened so as to leave out those participating in the entry interviews a second time. A quota sampling procedure was used to insure that the proportions of respondents from Canada, United States and Europe matched the population of tourists from these three origins: 65% of completed interviews were with Canadians; 31% from the United States; two-thirds of PEI leisure tourists were estimated previously to be Canadian and about 30% were estimated previous to the study to be Americans. The overall cooperation/completion rate for the exit questionnaire was 94%. Due mainly to some nonresponses to some of the questions, the usable number to test the propositions was close to 88% of the completed interviews. The analytical approach is exploratory and empirical. Group-level analyses (Bass, Tigert and Lonsdale 1968) were performed on data from the two surveys. A quasi-experiment between-groups research design was made possible from the use of the two data sets (Cook and Campbell 1979); this procedure ensures that the same participants being asked earlier planning questions do not sensitize responses in the second data set. Interviewing the same people twice (at the start and end of their visits to PEI) likely would have increased their awareness, intentions, and behaviors toward PEI attractions, activities, and destinations. The two data sets allow quantification of planned and unplanned behavior by tourists entering PEI and actual or unrealized behavior by those leaving.

Study Findings The great majority of tourism decisions are likely made while considering issues related to temporal and financial affordability: whether tourists can afford to take time off; how much they can afford to spend; which destination option best fits within their time constraints; which destination option best fits within their financial budget constraints; and when trade-offs are necessary, what choice heuristics should apply in selecting among destination options being considered. While the time a tourist allocates to a vacation is generally fixed (as is confirmed below), the amount of money set aside for vacation purposes is more flexible. Or put another way, while most people have a predetermined number of days they will or can be away from home, the number of consumers with a specific monetary amount (say, $1,500 for discretionary spending on non-essential items) is likely to be much smaller. As found in the entry survey, more than one in three (37%) did not state a specific budget for their trip. It is unlikely that all the respondents who provided a monetary figure had that exact figure in mind beforehand. Consumers are likely to have a ballpark figure

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only in mind when considering the financial limits on spending prior to departure and in the destination. Nevertheless, among the respondents who could provide a monetary figure for spending, significant differences occurred between the planned budget for, and final trip expenditure, on PEI. Spending increased from an average stated budget of $388 per respondent (n = 1,231; s.d. = 408) to $505 (n = 2,105; s.d. = 576) for stated spending in the exit survey (p = .001). Overall, the average reported spending behavior is 30% higher than planned spending. Since realized spending was significantly greater than planned, this finding supports hypothesis one. Planned and Reported Length-of-Stay. Planned length, expressed in terms of number of overnight stays, was 3.7 nights (n = 2,341; s.d. = 5.5), compared to the reported realized average number of 4.2 (n = 2,138; s.d. = 4.9, p < .001). Expressed as a percentage, the difference between planned and reported length-of-stay behavior is 15%. The significantly greater realized length-of-stay compared to the planned supports hypothesis one. What might account for the greater increase in spending more money than time for the planned versus realized strategies? Some viewpoints from prior research help to answer this question. For example, Stewart and Vogt (1999), comparing planned versus actual length-ofstay, found that the greatest concordance was in the 7+ day category, in which 90% of respondents who planned to stay that many or more days actually stayed that length of time. They conclude with the following self-evident truth, ‘‘If visitors changed plans, they were more likely to lengthen than shorten their stay. . .’’ Tourists can be confronted with a number of compelling reasons to shorten their holidays, such as weather, illness, issues at home, or sheer boredom. Any compulsion to stay longer must be accompanied by the capacity to extend their stay. Time is much less transferable and substitutable than, for example, money (Leclerc, Schmitt and Dube 1994). If a taxi costs $50 more than expected, consumption can be reduced in other areas to cover the loss. But if the taxi ride from the airport takes an hour longer than expected, this may be difficult to recoup. Conversely, time saved cannot be stored and used later, and hence is less attractive than money saved. Individuals will spend substantially more money than planned, but are unwilling or unable to substantially increase the amount of time spent in the destination. One simple explanation could be that time is less flexible than money, and that consumers are always more likely to engage in more unplanned spending than extend the amount of time allocated to the particular task. Planned and Realized Activities. Respondents were queried about their intention and consumption of 13 leisure activities. Table 1 ranks the planned activities, ranging from the most popular, sightseeing (81% stating they intended to do sightseeing), to the least popular, nightlife (5%). (Respondents were asked to name their intended activities in two unaided stages: first, ‘‘What do you intend to do, while on the Island?’’ and, after naming one activity the respondent was asked

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Table 1. Comparison of Planned and Realized Activities Activity

Sightseeing Visiting museums and historical sites Going to the beach Lobster supper General shopping Antiques and handcrafts shopping Live theater Active sports Land or harbour tour Nightlife Water sports Golfing

Planned % (n = 2,131) 81 36 22 25 21 16 14 8 6 5 11 9

Done % (n = 2,239) 87 62 43 44 58 54 22 14 16 13 10 9

Chi-square

28.215 315.827 178.615 174.713 625.682 689.105 41.913 36.741 108.831 70.358 1.217 0.400

p