The Impact of Situational Factors on Chinese Mall Shoppers’ Buying Decisions

ABSTRACT - This study investigated the impact of situational factors on Chinese mall shoppers’ buying decisions. It found that, of the 14 situational factors considered, three (Buying Intention, Weekend Shopping, and the amount of Time Spent in the Mall) had positive impacts and one (Travel) had a negative impact on Chinese mall shoppers’ buying decisions. It also found that the factors influencing Chinese mall shoppers to buy food and nonfood products were different. Implications of the findings were suggested.

Citation:

Guijun Zhuang, Nan Zhou, and Fuan Li (2002) ,"The Impact of Situational Factors on Chinese Mall Shoppers’ Buying Decisions", in AP - Asia Pacific Advances in Consumer Research Volume 5, eds. Ramizwick and Tu Ping, Valdosta, GA : Association for Consumer Research, Pages: 351-357.

Asia Pacific Advances in Consumer Research Volume 5, 2002      Pages 351-357

THE IMPACT OF SITUATIONAL FACTORS ON CHINESE MALL SHOPPERS’ BUYING DECISIONS

Guijun Zhuang, Xi’an Jiaotong University, China

Nan Zhou, City University of Hong Kong, Hong Kong

Fuan Li, Eastern Kentucky University, U.S.A.

ABSTRACT -

This study investigated the impact of situational factors on Chinese mall shoppers’ buying decisions. It found that, of the 14 situational factors considered, three (Buying Intention, Weekend Shopping, and the amount of Time Spent in the Mall) had positive impacts and one (Travel) had a negative impact on Chinese mall shoppers’ buying decisions. It also found that the factors influencing Chinese mall shoppers to buy food and nonfood products were different. Implications of the findings were suggested.

INTRODUCTION

The shopping mall plays a major role in the US lifestyle (Swinyard 1998). It has been reported that three-fourths of the population goes to a mall at least once a month in the United States and that Americans spend more time in shopping malls than anywhere else except for home and work (Swinyard 1998). The shopping mall is a contemporary town square, a community center serving as a nesting place (Bloch, Ridgway, and Dawson 1994). Patterns of mall use in the United States may indicate trends for shopping mall developments in developing countries.

As a new type of retailing space, shopping malls appeared in the People’s Republic of China (hereafter China) in the late 1980s (Lin 1998). Though mainly located in large cities such as Beijing, Shanghai, Guangzhou, Wuhan, Shenzhen, and Xi’an, malls have become among the most promising retail venues in China (Lin 1998). However, there have been few studies on shopping malls in China and on the behaviors of Chinese mall shoppers. To fill this gap, this study seeks to discover the factors influencing Chinese mall shoppers’ purchase decisions.

What interests mall marketers (from mall developers to suppliers, retailers, and other service providers) most is how mall shoppers make their purchase decisions. They would like to know if a shopping trip actually leads to purchases and what factors may affect mall shoppers in their decisions to buy. Answers to these questions are part of the bases on which mall marketers formulate marketing strategies (Kotler, Armstrong, Brown, Adam, and Chandler 1998). Many factors have been identified as determinants of a shopper’s purchase decision, including the shopper’s personal and psychological characteristics, cultural, social, and other environmental forces, and the marketing programs of marketers (Howard and Sheth 1967). Belk (1975) grouped these factors into situational factors and non-situational factors. Based on Belk’s framework, we focus only on the impact of situational factors in this study. The questions we try to answer are: Do the situational factors significantly influence Chinese mall shoppers’ buying decisions? If so, which situational factors are influential?

In addition, we investigate the differing impacts of situational factors on the purchase of food and nonfood products, since, in the context of a Chinese shopping mall, different types of decision processes are involved in buying food and nonfood products (Zhuang, Zhou, Li, and Zeng 2001). Our question is: Do the factors influencing a shopper to buy food differ from those influencing the shopper to buy nonfood products?

The rest of this paper is divided into five sections. In the first section, we develop two hypotheses based on our literature review. In the next section, we describe our research methodology. In the third section, we report our results. We discuss the findings of our analysis in the fourth section. In the last section, we draw conclusions and suggest future research directions.

LITERATURE REVIEW AND HYPOTHESES

Previous studies on consumers’ shopping behaviors indicate that many factors influence shoppers’ buying decisions. Belk (1975), based on "a revised stimulus-organism -response paradigm" (p.151), grouped such factors into two broad categories: situational and non-situational factors. Non-situational factors refer to the lasting and general characteristics of an individual or an object, for example, personality, intellect, sex, and race for an individual, and brand image, quality, size, and functions for an object. Situational factors, on the other hand, refer to "all those factors particular to a time and place of observation which do not follow from a knowledge of personal and stimulus attributes and which have a demonstrable and systematic effect on current behavior" (p.152).

Belk further divided situational factors into physical surroundings, social surroundings, temporal perspectives, task definitions, and antecedent states. Physical surroundings include geographical and institutional locations, decor, sounds, aromas, lighting, weather, and visible configurations of merchandise or other material surrounding the stimulus object. Social surroundings include other persons present, their characteristics, their apparent roles, and interpersonal interactions. The temporal perspective is "a dimension of situations which may be specified in units ranging from time of day to season of the year" (Belk 1975, p.153). Task definition refers to an intent or requirement to select, shop for, or obtain information about a general or specific purchase. Finally, antecedent states are momentary moods or momentary conditions such as acute anxiety, pleasantness, hostility, cash on hand, fatigue, and illness.

As determinants of shoppers’ buying decisions, some of these factors have been frequently studied. It has been identified, for instance, that there are various motivations for people to shop (Babin, Darden, and Griffin 1994; Batra, and Ahtola 1991; Baumann, Cialdini, and Kenrick 1981). Some customers shop for purely utilitarian reasons. They are very focused and they regard shopping as the equivalent of work, a means to serve their functional needs. In contrast, some others expect hedonistic outcomes from shopping experiences. They like to shop and derive pleasure from the act of shopping or purchasing, rather than from the product purchased (Rook 1987). Generally speaking, utilitarian shoppers are more likely to buy than hedonistic shoppers because they are more strongly motivated by the purchase itself.

Purchases made during shopping trips may be either planned or unplanned (Engel, Blackwell, and Miniard 1995). Many consumers prepare mental or written lists before going shopping, while others make intentional use of product displays in retail stores as a shopping list (Kollat 1967). In addition, some shoppers are impulse buyers whose purchase decisions are made primarily based on hedonic or experiential concerns (Holbrook and Hirschman 1982). Because planned buyers know clearly what they want to buy before starting their shopping trip, they are more likely to buy than are unplanned buyers.

Other people may have a decisive role in consumers’ purchase behaviors and consumers are susceptible to such influences (Beardon, Netemeyer, and Teel 1989). Nicholls, Roslow, and Comer (1994) found that mall patrons tended to buy more products and spend more money when accompanied by other people.

The pressure of time can significantly alter shopping behaviors (Nicholls, Roslow, and Dublish 1997). It has been observed that shortage of time affects consumers’ in-store shopping decisions, reducing both planned and unplanned purchases (Iyer 1989; Park, Iyer, and Smith 1989).

Consumers’ familiarity with the shopping environment is also important. Frequent customers, who know a store’s layout, make fewer unplanned purchases compared with those who are unfamiliar with the layout (Iyer 1989; Park et al. 1989).

Furthermore, Simonson and Winer (1992) argue that purchase behavior can also be modified by the way in which inventory is arranged. Kumar and Leone (1988) maintain that point of purchase displays can be very useful in stimulating sales. The color of the background setting off a product can also affect shoppers: blue calms; red causes tension (Bellizzi and Hite 1992). The height of a product on the shelf makes differences to sales (Hitt 1996). In addition to visual influences on purchase behavior, auditory stimuli, such as music, influence shoppers’ behaviors as well (Alpert and Alpert 1990). Previous research indicates that auditory and visual sensations, e.g., music and decor, can affect patrons’ moods, and these, in turn, influence their purchase behaviors (Bruner 1990). In general, contented consumers tend to buy more than unhappy ones (Curren and Harich 1994; Knowles, Grove, and Burroughs 1993).

To sum and to guide the investigation, we propose:

H₁: Situational factors have significant impacts on Chinese mall shoppers’ buying decisions.

Most buying behaviors fall under one of the following three categories: impulse buying, habitual buying, and consumption problem solving buying (Bagozzi, Rosa, Celly and Coronal 1998). Impulse buying generally occurs because an external (e.g., display in a store) or internal (e.g., hunger) stimulus has caught the shopper’s attention and the product is easy to acquire. Impulse buying decisions are made with very little thought. Under habitual buying, prior learning is crucial. While needs usually initiate the purchase in such instances, cognitive processes predominate and include the execution of action sequences and the evaluation of limited decision criteria. Extensive search, processing of information, and integration with one’s needs are involved in consumption problem solving buying.

A shopping mall is distinguished from a single retail store in that it is "a group of retail businesses planned, developed, owned and managed as a unit" (Kotler et al. 1998). Additionally, it gives consumers the convenience of one-stop shopping. A consumer can find many more types of products there. It has been found that the factors influencing a shopper to buy food products in a shopping mall may differ from those influencing the shopper to buy nonfood products, given the perishable nature of most of the food industry’s product lines and the limited amount of time available for food-related shopping (Lewison 1997). This is especially true in the context of a Chinese shopping mall. Chinese people prefer to buy fresh food, and it is easy for them to find fresh vegetables or meats either in the open markets or in stores around their neighborhoods. Therefore, their purchase of food products in shopping malls is often driven by external or internal impulses. On the other hand, buying other products, especially those high in value, may involve more efforts in search and processing of information. The arguments lead us to posit that

H₂: The situational factors influencing shoppers to buy food or beverages differ from the situational factors influencing shoppers to buy nonfood products.

METHODOLOGY

The sample

Secondary data are used in this study. The data were collected for a multinational survey (for details see Li, Nicholls, Zhou, Mandokovic, and Zhuang 2001; Zhuang et al. 2001) in Xi’an from 24 to 30 April, 2000. A quasi-randomized selection procedure was adopted (Churchill 1991; Sudman 1980). Table 1 illustrates the demographics of the respondents.

The questionnaire

The instrument used in the survey was initially developed by Nicholls et al. (1997). Some changes were made to adapt to the specific situation involved in the survey. The instrument covered the following measures in addition to several general questions concerning demographic attributes: (1) motivation for mall visits: respondents were asked to report what motivated their shopping trips from a list of different shopping objectives such as looking and browsing, making a specific purchase, bargain hunting, eating at the mall, shopping at a specific store, meeting friends and so on. A purchase intent measure was formed later by dichotomizing the shopping objectives into purchase-driven and other motivations with the former being those who defined making a purchase as the main tak of their shopping trip; (2) reasons for selecting the mall: respondents were requested to give the reasons they had selected the mall as the destination of their shopping trip. They were given a short list to choose from, included among which were assortment of merchandise, favored stores, shopping atmosphere, convenience for access, travel destination, quality of products; (3) shopping patterns: respondents were asked to indicate the time they spent on the road and in the mall; the frequency of their mall visits; the number of persons if any they came with. The responses were later dichotomized during data analysis whenever appropriate; (4) purchase: the purchase of each respondent was measured by two items: purchase of food or beverages or purchase of other products (nonfood products). After the data were collected, the measure of total purchase was formed by combining the purchase of food and other products. Table 2 lists the variables included this study and how they were coded.

Analytical techniques

Logistic regression analysis was employed in the data analysis. This technique is often employed to examine the probability of a person or an organization taking an action under the particular impacts of particular factors (Menard 1995). We separately measured total buying, buying food, and buying non-food products as dependent variables because the factors influencing shoppers to buy food or non-food products may be different. The situational factors were independent variables. To investigate the net impacts of situational factors, we included some non-situational factors such as gender, age, education, and living place (home) as control variables.

The analytical model used is as follows:

EQUATION

where logit(Y_j)=ln{P(Y_j=1)/[1-P(Y_j=1)]} is the probability of making a purchase, Xij is the ith factor influencing the customer’s buying behavior, b₀ is constant, bij is the coefficient of factor i, the focus of our study.

TABLE 1

DEMOGRAPHICS OF MALL RESPONDENTS (N=459)

RESULTS

Table 3 presents the results of the analysis in two sections, namely initial model and final models. In the initial model section, the three logistic regression models were fitted with all the factors measured. The purpose of this section is to show how many situational factors were considered in this research and their impacts. In the final model section, the three models were automatically determined by using the "Backward-Conditional" function of the logistic regression program in SSPS as the initial models. This section shows the factors left at p<0.1 and their impacts when the other factors are dropped sequentially. The final models are the focus of our study.

The results in final model section show that the Chi-squares for the three final models are 70.574, 36.606, and 83.396 respectively. This indicates that, in the models, the independent variables as a whole have a significant impact on the dependent variables (p<0.001; Menard 1995). Among the models, the third (final model Y₃) is superior to the others in terms of variance explanation (R_CS2=0.189 and R_N2=0.258).

To test H₁, we looked at the regression coefficients of the factors and their significant levels in final model Y₁. We found that four situational factors, namely Buying Intention (X₁), Travel (X₆), Weekend (X₁₀) and Staying Time (X₁₁), had significant affects on our mall shoppers’ buying decisions at p<0.05. More specifically, they all had positive impacts on the mall shoppers’ buying decisions except for Travel.

To test H₂, we compare the significant factors in final model Y₂ with those in final mode Y₃. We find that, of the situational factors considered, three factors, namely Staying Time (X₁₁), Frequency (X₁₂), and Number of Stores Visited (X₁₃), had significant impacts on the shoppers’ buying food or beverage behaviors (p<0.05; final model Y₂), while four factors, namely Buying Intention (X₁), Travel (X₆), Staying time (X₁₁), and Frequency (X₁₂), had significant impacts on the shoppers’ buying nonfood products behaviors (p<0.05; final models Y₃). Only Staying Time (X₁₁) and Frequency (with opposite signs) are shared by both models. This is consistent with the prediction of H₂.

In addition, the impacts of the control variables further confirm the different patterns of the shoppers in buying food or nonfood products. We find that Gender (X₁₅) had a significant influence only on the shoppers’ buying nonfood products behaviors (p<0.05; final model Y₃), while Age (X₁₆) had a significant influence only on the shoppers’ buying food or beverage behaviors (p<0.05; final model Y₂).

DISCUSSION

Our study, firstly, indicates partial support for hypothesis one: of the 14 situational factors considered, four had significant affects on Chinese mall shoppers’ buying decisions. These factors are Buying Intention (X₁), Travel (X₆), Weekend (X₁₀) and Staying Time (X₁₁). Buying Intention, Weekend and Staying Time had positive impacts, while Travel had a negative impact.

The impacts of these significant factors are understandable. Buying Intention (X₁) is jointly measured by shopping motivation and planning to buy. Therefore, the higher a shopper’s buying intention, the stronger the shopper is motivated by a specific purchase, and the more likely he/she buys. A possible reason for the negative impact of Travel (X₆) is that the shoppers with the greater travel intent treat the mall more as an attraction to visit than as a place to purchase. This may make them less likely to buy. Many things may make Weekend Shoppers (X₁₀) more likely to buy. For instance, weekend shoppers not only have stronger motivations of buying, but also have more time to spend in shopping. Considering the impact of Weekend (X₁₀) alone by controlling the other factors indicates that the weekend effect may be due to the weekend atmosphere in the mall. The positive impact of Staying Time (X₁₁) on shoppers’ buying decisions could be explained this way: the more time a shopper spends in a mall, the more likely he/she would make unplanned or impulse purchases.

TABLE 2

THE VARIABLES INCLUDED AND THE RULES FOR CODING VARIABLES

TABLE 3

THE RESULTS OF LOGISTIC REGRESSION

Secondly, consistent with the prediction of H₂, we found that the decision patterns of the shoppers for buying food products and for buying other products are strikingly different in terms of significant situational factors. Among the factors in final model Y₁ discussed above, three out of four had significant impacts on the shoppers’ decisions in buying nonfood products, while only one had a significant impact on the shoppers’ decisions in buying food or beverages. We also found when testing H₂ that the impact of the Frequency of Visiting the Mall (X₁₂), though insignificant on total buying, is significant but opposite in direction on the shoppers’ decision to Buy Food compared with the shoppers’ decision to Buy Nonfood Products (p<0.05; final models Y₂ and Y₂). The insignificant impact of Frequency on Total Buying (final model Y₁) can be explained by the opposite influences of this variable on shoppers’ buying of the two types of products.

The variable of the Number of Stores Visited (X₁₃), however, did not have a significant impact on shoppers’ decisions to Buy Nonfood Products, while shoppers’ Total Buying had a significant but negative impact on shoppers’ decisions to Buy Food or beverages (p<0.05; final model Y₂). This suggests that the more stores o boutiques a shopper visited during a shopping trip, the less likely he/she would be to buy food or beverages. This seems to be contradictory to the impact of Staying Time (X₁₁). Usually, the longer a shopper stays in a mall, the more stores or boutiques he/she would visit in the mall, and the more likely he/she would buy food according to the relationship between Staying Time and Buy Food (final model Y₂). However, since X₁₁ and X₁₃ are simultaneously considered in the logistic regression model, the real meaning of the coefficient of X₁₃ is the impact of the Number of Stores Visited on a shopper’s decision to Buy Food while holding Staying Time constant. In other words, when comparing those who had stayed in the mall for the same amount of time, the more stores a shopper visited in the mall, the less likely he/she would buy food or beverages. This may reflect the weaker motivations of "window shoppers" for buying.

Taking the information revealed by the three final models together, it seems that (1) the buying decision pattern of Chinese mall shoppers is dominated by buying nonfood products; (2) Chinese shoppers are more arbitrary about buying food than about buying nonfood products. The first point can be seen in the fact that the pattern of final model Y₁ is much more similar to the pattern of final model Y₃ than it is to the final model Y₂. The second point can be seen, on the one hand, in the less explained variance of final model Y₂ and, on the other hand, by the factors included in final model Y₂ being (basically) those from the temporal perspective.

Finally, the difference in decision patterns of the shoppers buying food products and nonfood products is further confirmed by the significant non-situational factors. As observed, Gender had a significant impact on only the decision to buy nonfood products, while Age had a significant impact on the decision to buy food or beverages. Our further analysis show that males were more likely to buy nonfood products than were females, and the younger generations were more likely to buy food or beverages than were the older generations.

CONCLUSION

We conclude that, first, Buying Intention (X₁), Weekend Shopping (X₁₀), and Staying Time at the mall (X₁₁) had positive impacts, while Travel (i.e., tour ) as the reason for the mall trip (X₆) had a negative impact on Chinese mall shoppers’ buying decisions; and, secondly, the factors influencing Chinese mall shoppers to buy food and to buy nonfood products are different. It appears that Chinese mall shoppers’ buying decision pattern is dominated by the decision to buy nonfood products, and that Chinese mall shoppers are more arbitrary about buying food than about buying nonfood products.

Several contributions may have been made with this study. First, the study provided evidence that situational factors affect mall shoppers in making purchase decisions in the context of a Chinese shopping mall. Second, the study supported the argument that the situational factors influencing shoppers to buy food or beverages differ from those influencing shoppers to buy nonfood products. This implies that we should examine the behavior of buying food separately from the behavior of buying other products when investigating consumer behavior in shopping malls.

Implications of the findings for practitioners are as follows: First, situational factors are partially affected by a marketer’s marketing programs. They can, therefore, be altered by marketing programs. For instance, the developer of a shopping mall can increase shoppers’ buying intent by better promotion programs; the developer can also extend shoppers’ staying time by providing more entertainment activities. The retailers in the mall, on the other hand, can induce more shoppers to buy with clear buying plans by building a distinguishing imae.

Second, in the context of a Chinese shopping mall and among the situational factors considered, a marketer should pay more attention to shoppers’ buying intent, staying time, and weekend shopping. According to the findings of this study, raising shoppers’ buying intent, extending shoppers’ staying time, and attracting more weekend shoppers can significantly increase the likelihood of buying. However, food retailers should consider different situational factors from those considered by retailers of other products. Buying intent may not be an important factor in decisions to purchase food items. Instead, in-store displays or promotions is more important given the nature of food buying (e.g., buying beverage) as a form of impulse purchase in the mall context.

A limitation of the current study lies in the selection of the mall where the interviews were conducted. Using the data from a single place in the country limits the generalizability of these findings. Some of our findings may be store/mall specific rather than representative of the general population of Chinese shoppers. Thus, caution should be taken in interpreting and utilizing our results.

Secondly, many situational factors, e.g., factors in physical surroundings, were not included in the study. Future studies can look into the impact of these factors.

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