Factors of new technology adoption in the retail sector.

In recent years, small manufacturing enterprises have been implementing new computerized technologies at an every-increasing rate (Acs & Audretsch, 1990; Julien, Estime, & Drilhon, 1993), and the trend is being accelerated by international competition, in particular by the opening up of

national boundaries. However, the same urgency does not seem to exist in the commercial and service sectors, especially among independent retailers (Robertson & Gatignon, 1986; Treadgold, 1989; Julien & Thibodeau, 1991). Certainly, competitive pressure is relatively less intense in these sectors, or at the very least, certain small businesses have an advantage in terms of location, knowledge of their clients and their merchandise, or profit from niches created by market segmentation (for example, gourmet food stores or high fashion boutiques), They are thus under less pressure to become computerized; they may also be less likely to do so and may not believe new information technologies to be very useful because, for example in merchandise analysis, they know their customers and merchandise intimately.

Many small retailers, though, whose ability to differentiate themselves from their competitors is limited, are feeling increasing competitive pressures, especially with the growth of chains and the advent of warehouse stores. A majority of these have neither the experience nor the financial resources required to plan, implement, and operate a computer-based information system (Raymond & Lorrain, 1992). But others have begun to systematically install new point-of-sales (POS) terminals or link up to wholesalers by tele-computing or electronic data interchange (EDI) (Sweeney, Putnam, Brooks, Hyde, Lee, Rubel, Rossiter, Armstrong, & Koenigsberg, 1990; Blili & Raymond, 1993).

There may be many reasons for these behavioral differences, even among firms in the same industry facing the same type of market. In the manufacturing sector, macro-economic factors such as the rate of economic growth and the competitive structure of the industry can be determinant (Bouchut, Cochet, & Jacot, 1984; Julien & Thibodeau, 1991). Researchers have also shown that more "technology-consuming" firms are distinguished from others by: greater profitability, the differential cost of new equipment (Mansfield, 1971, 1975; Khan & Manopichetwattana, 1989); the management profile or the owner-manager's personal characteristics (Miller & Toulouse, 1986); organizational complexity, centralization and formalization (Cohn & Turyn, 1980, 1984; Meyer & Goes, 1987; Collins, Hage, & Hull, 1988); strategy type (Garsombke & Garsombke, 1989); and the quality of technological information obtained together with the capacity to process it (Blili & Raymond, 1993; Julien, Carriere, & Hebert, 1988; Gatignon & Robertson, 1989).

However, very little research has been done on the reasons for new technology adoption in the service and retail sectors. To our knowledge, only Treadgold (1989) and Hogarth-Scott and Parkinson (1990) have examined the question. Treadgold showed that differences in new technology adoption could be explained by the type of external organization (for example, between independent businesses and businesses belonging to groups) and the industry sector. Hogarth-Scott and Parkinson, for their part, showed that the limited use of EDI technologies among independent businesses stemmed from their fear of losing their independence. Miller, Glick, Yau-De, & Huber, (1991) have also indicated that organizations in the retail and service sectors have narrower ranges than in manufacturing on both technology and structure variables, and thus tend to exhibit weaker technology-structure relationships.

The aim of this research, using empirical evidence, was to identify organizational, structural, and strategic factors underlying the level of penetration of various management, service, and information technologies among small retailers, including both independent businesses and those affiliated to trade banners or buying combines. Potential factors were identified from the previously cited literature, and in particular from Julien, Carriere, and Hebert's (1988) study done in the manufacturing sector.

RESEARCH MODEL

The research model is presented in Figure 1. A first set of factors, thought to influence new technology adoption in small retail firms, describes the structural sophistication of the firm in terms of centralization and complexity. In the organization theory literature, structure has often been characterized along these two dimensions or composite factors (Ford & Slocum, 1977; Miller, 1987). This includes prior research on the technology-structure relationship (Miller et al., 1991). Decentralizing and complexifying structure implies more elaborate communication, coordination, and control mechanisms; this in turn requires an infrastructure that can be better enabled through information technology (Huber, 1984; Leifer, 1988). Hence, more decentralized management and complex administrative structures should lead to greater adoption of information technologies by retailers (Cohn & Turyn, 1984; Miller & Toulouse, 1986; Raymond, Pare, & Bergeron, 1993).

The second set of adoption factors is strategic in nature, identifying the level of assertiveness, rationality, and interaction in business decision processes. These three factors have emerged in the strategy literature as fundamental dimensions or derivative constructs of the strategy concept (Miller, 1987; Venkatraman, 1989). The first dimension refers to the proactiveness of decisions and risk taking. The second one concerns the planning and formulation of strategies. The third one refers to the political/bargaining processes that bear upon decisions. Information technology has become one of the key elements in the definition and realization of strategic objectives (Vitale, Ives, & Beath, 1986). In an increasingly complex environment, organizations must "align" their use of information technology with the implementation of their strategic decisions (Chan & Huff, 1993). Thus, when they are more proactive, more future/planning-oriented, and when more executives interact in strategy making, retailers would be more apt to implement computer-based information systems resulting from or in support of their strategy (Shrivastava & Grant, 1985; Schroeder, Gopinath, & Congden, 1989).

The third set of factors thought to influence new technology adoption in small retail firms are organizational, including size, sector, and status (independent or affiliated). Organization theorists have found the first two contingency variables to consistently play an important role in structure-technology-strategy relationships (Ford & Slocum, 1977; Miller et al., 1991). One expects larger firms to have adopted more sophisticated technologies (Dewar & Dutton, 1986; Raymond, Pare, & Bergeron, 1993). The same can be said of firms in more information-intensive retail sectors, e.g. hardware as opposed to clothing (Kimberly & Evanisko, 1981). Firms who become linked to banners or buying combines, as opposed to remaining independent, would also be greater users of information technology (Tornatsky & Klein, 1982). The greater technological sophistication obtained by retailers who cooperate among themselves would tend to reduce environmental uncertainty in terms of markets and competitors (Huber, 1984).

The seven factors of new technology adoption were chosen due to their fundamental nature as descriptors of structure, strategy, and organizational contingencies. These factors have also been identified as fundamental determinants of technology, both theoretically and empirically, and all have been used previously in the small business research context. Note that many of the organization theory and strategic management studies cited here point to the interdependence of strategy and structure, and to the contingent role of size and sector in this regard. However, given the focus and objectives of this study, expected interrelationships between structure, strategy, and organizational contingencies were not made explicit in the research model.

No distinction was made between management and production technologies as indicators of technological levels because in retailing, unlike manufacturing (Raymond & Pare, 1992), it is difficult to differentiate the two. In addition, the actual level of independence or involvement in a group was not measured, even though some retailing groups (buying combines, trade banners, coops, franchises) exercise more control over their members than others. However, franchisees were excluded from the study because, in contrast to the other groups, their independence is very restricted (Castrogiovanni, Bennett, & Combs, 1993). In the Canadian province of Quebec, where the survey took place, independent retailers and members of buying groups or coops are responsible for more than 72% of all retail sales, at the expense of chains and megastores, whereas in the rest of Canada and in the U.S.A., their share is much smaller.

RESEARCH METHODOLOGY

The survey was carried out by collecting data from a sample of small retailers in the food, hardware, and ladies' garment sectors. Their names were obtained from the central business data bank of Quebec's commission on health and safety in the workplace (CSST). The survey was performed in two stages. First, 1,200 letters were sent out to the addresses provided by the CSST, requesting an interview with the owner (4.8% of these were returned due to improper addresses or out-of-business situations). Among the 232 (19.3%) who were willing to cooperate, a total of 79 retailers were selected for semi-structured interviews in the field, with the help of a questionnaire. Using CSST data for the entire 1,200-firm population, the 79 firms were chosen on a stratified sampling basis, to be statistically representative of this population in terms of size and regional distribution for each sector. This was verified through chi-square and t tests. The questionnaire included 56 closed questions and six open questions, divided into four sections: the firm's demographics (size, status, etc.), structure, computer hardware and software, and strategy. A pre-test was done using three firms from each of the three sectors. Interviewing was deemed preferable to mailing the questionnaire, due to the technical or open nature of many questions, and to acquire more in-depth information.

Two main types of technology were distinguished in the survey: hardware for business computing (computers), point-of-sales computing (computerized cash registers, whether or not linked to a central processing unit), and tele-computing (electronic links with outside firms, usually wholesalers); and software, assessed by the nature of the applications portfolio. The measures of hardware and software technology adoption, i.e. the dependent variables, were adapted from previously developed instruments by Julien, Hebert, and Carriere (1988) and by Raymond and Lorrain (1992). Two main dimensions of structure--centralization and complexity--were assessed by measuring the managerial hierarchy (number of managers excluding the owners(s)/all personnel) and the administrative apparatus (clerical workers/all personnel), both measures previously validated and used in a small business context (Paulson & Stump, 1979). Complexity was also ascertained by the presence of a management committee (Julien, Hebert & Carriere, 1988). The same was done with the three principal components of strategy, namely assertiveness, rationality and interaction. Miller's (1987) instrument was used to measure strategic orientation (or proactiveness: reactive-proactive) for the first dimension, organizational time-frame (or futurity: short-long term) for the second one, and strategic decision making (individual-consensual) for the third one.

Table 1 shows the descriptive statistics of the research variables. Thirty-six percent of firms operated in the food sector (grocery stores, butchers, etc.), 40% in the hardware sector and 24% in the garment sector. They employed an average of 14.5 people (excluding owners), including 2.5 office staff. In all, slightly over 50% of the businesses were affiliated to a trade banner or buying combine, and 46% had a management committee. Strategically speaking, they were fairly proactive (4.0 on a scale of 1 to 7), had a medium time-frame (2.7 on a scale of 1 to 5), and shared the decision-making process to a moderate degree (4.0 on a scale of 1 to 7).

ADOPTION OF NEW TECHNOLOGIES

The overall survey results show that 63 of the 79 firms questioned (79.7%) used at least one computer-based technology (i.e. electronic cash registers), a higher number TABULAR DATA OMITTED than the 71% forecasted by Collard (1989) for 1993 in the United States. The percentage is higher in the food sector (92.8%) than in the hardware (84.4%) and garment (57.9%) sectors. In all, 36 firms (46.2%) used only stand-alone electronic cash registers, 11 (14.0%) used cash registers linked to a central register, and 16 (20.5%) had a cash register system linked to a computer. Eight firms (10.0%) had only a fax or telex, 31 (39.4%) maintained outside links by modem for batch data transfers or financial transactions (with banks), and 21 (26.9%) maintained systematic outside links through computers or terminals (EDI).

The number of business computing applications varied from zero to four. Eight firms (10.1%) indicated using four applications, 16 (20.2%) used three, ten (12.7%) used two, and four (5.1%) used only one application. In all, 41 firms (51.9%) did not use any form of business computing. Overall, the type of technology least used by sample firms turned out to be business computing and related applications. The other two technologies were more widespread, as only 15 firms (19.0%) did not have an electronic cash register (POS computing) and 18 (22.8%) did not have an electronic link with an outside organization (tele-computing). Table 2 summarizes mean adoption rates for each technology (using 5-point sophistication scales).

DETERMINANT FACTORS OF NEW TECHNOLOGY ADOPTION

Shown in Table 3 is the intercorrelation matrix of the independent variables. Given its highly significant relationship with five other factors, size was removed from further data analysis to reduce problems due to multicollinearity (Huang, 1970). The same was done with the decision-making variable, as it is strongly correlated with the other two strategy variables. Note that after removal of these two variables, two intercorrelations TABULAR DATA OMITTED greater than 0.3 remain, that is, firms in the clothing sector and firms with a larger bureaucracy (administrative apparatus) are less likely to have an affiliated status (r = -0.52, r = -0.35).

As hypothesized, differences in levels of hardware and software technology adoption by retailers are due to a number of organizational, structural, and strategic factors.

TABULAR DATA OMITTED

Shown in Table 4 are the results of stepwise regression analyses on the four technology variables, revealing the determinant factors in each case. Retailers who adopt business computing are less likely to be in the food sector and tend to be affiliated to a buying combine or trade banner. They are more decentralized, i.e. show a higher ratio of managers (managerial hierarchy), and are more likely to have a management committee. Strategic factors do not seem to come into play here.

The adoption of POS computing is determined by a different set of factors. Clothing merchants are less apt to employ this technology, as are firms with a larger ratio of clerical personnel (administrative apparatus). In addition, firms with POS technology have a longer organization time-frame. The use of tele-computing is mostly determined by the retailer's status as member of a combine or banner, as expected. Firms who have implemented EDI are also more future-oriented than others, confirming Blili and Raymond's (1993) conjecture.

Not surprisingly, hardware stores, with their greater information needs (e.g. for inventory management), are the ones with the more extensive applications portfolio (software), while more decentralized firms implement a more sophisticated management information system. These results are in line with the technology-structure relationships found by Raymond, Pare, and Bergeron (1993). Also, more proactive and future-oriented retail firms implement a more extensive applications portfolio. This confirms the mutually determining impact of strategy and information systems in the context of SMEs (Blili & Raymond, 1993).

Table 4

Regression Analyses on the Four Technology Variables

                                Standardized betas(a)

                             HARDWARE                       SOFTWARE

                             Business   Point-of-sales    Tele-
Application Variable                    computing    computing
computing    portfolio

ORGANIZATION

Size (removed initially)

Sector
Food                          -.22          --              --           --
Hardware                        --          --              --          .22
Clothing                        --        -.32              --           --

Status                         .36          --             .42           --

STRUCTURE

Managerial hierarchy           .30          --              --          .28
Administr. apparatus            --        -.24              --           --
Management committee           .21          --              --          .23

STRATEGY

Orientation                     --          --              --          .24
Time-frame                      --         .23             .26          .26
Decision-making
(removed initially)

R square                       .27         .27             .31          .42

F                             6.6         8.8            15.7         10.0

(p)                           (.000)      (.000)          (.000)       (.000)

a For variables having entered in the stepwise regression.

ENTERPRISE TYPOLOGY AND NEW TECHNOLOGY ADOPTION

To typify the sampled firms in terms of overall technology adoption, a hierarchical cluster analysis on the four technology (dependent) variables was used to divide the sampled firms into three groups: 45 firms using very few or no hardware and software technologies (type 1: LOW TECHNOLOGY); 18 favoring technology for administrative and managerial purposes (type 2: MANAGEMENT TECHNOLOGY); and 16 using technologies to enhance organizational value and performance (type 3: VALUE TECHNOLOGY). Business computing is used equally by the firms in types 2 and 3; these two groups also exhibit the same level of sophistication in their applications portfolio. However, POS and tele-computing are used significantly more by type 3 than type 2 retailers. Type 1 firms use some POS and tele-computing but almost no business computing or applications.

Table 5 presents a breakdown of the adoption factors (independent variables) for the TABULAR DATA OMITTED three types. Type 3 merchants (those seeking value) have the greater size, operate more in the hardware sector, and are generally affiliated. Management is often the responsibility of more than one person, but is less bureaucratic than in the other two types. More than two-thirds of businesses in type 3 have a management committee, and most tend to have more proactive and long-term strategies. The strategic decision-making process is usually shared.

Type 2 firms (those favoring technological management) are evenly spread over the three retail sectors, but are more bureaucratic than type 3 firms. Their strategies are as proactive and the decision-making process is shared to a similar degree, but their time-frame tends to be shorter. Type 1 businesses (those making little or no use of technologies) have the fewest employees and are least likely to be affiliated to trade banners or buying combines. They operate mainly in the food sector. Management is generally the responsibility of one person, but these organizations are more bureaucratic than the other two types, and little use is made of management committees. They tend to be reactive, have a shorter time-frame, and the decision-making process is rarely shared.

Overall, the more value- or performance-oriented the business, the more it will adopt many different technologies. In contrast, small retailers favoring technological management will favor business computing and implement a larger number of applications. The remaining majority (type 1) will opt mainly for POS and tele-computing (EDI).

A discriminant analysis was performed to establish the importance of each factor in determining a firm's being categorized as one of the three types. The results are presented in Table 6. The first variable to enter is the firm's managerial hierarchy (decentralization), followed by the firm's being in a less information-intensive sector (clothing or food). Differences between types 1, 2, and 3 can also be explained by the firm's status, by the presence or not of a management committee, and by the administrative apparatus (structural complexity). The retailer's strategic orientation and time-frame are the last factors to enter in the analysis. These results are in line with the preceding ones and confirm the overall validity of the research model as eight variables display significant explanatory power.

Table 6

Discriminant Analysis of the Three Types of Firms

Type 1: LOW TECH.            Type 2: MANAGEMENT TECH.    Type 3: VALUE TECH.

         Variable             F to      Wilks'             Min.        D.f.
Step     entered              enter     lambda     p       D(a)       coef.(b)

1        Man. hierarchy        6.0        .856    .004     0.2         .442
2        Clothing (sector)     3.4        .781    .002     0.8        -.397
3        Food (sector)         2.5        .728    .001     1.0        -.542
4        Status                3.8        .655    .000     1.2         .492
5        Man. committee        4.2        .582    .000     1.3         .456
6        Admin. apparatus      2.8        .536    .000     1.6        -.287
7        Orientation           2.1        .504    .000     2.0         .339
8        Time-frame            1.0        .488    .000     2.0         .261

a Mahalanobis D-squared distance between groups.

b Standardized discriminant (primary) function coefficients eigenvalue = 0.75;
canonical correlation = 0.654 (p = .000); % of cases correctly classified =
75.6%.

Of particular significance is the early entry of both the clothing and food sector variables, as opposed to hardware which did not enter. The negative coefficients of the first two confirm sectorial differences within retailing regarding new technology adoption, which could be attributed to lesser information requirements as mentioned previously, or to different organizational cultures and managerial habits. Also significant is the presence of the status variable, indicating the importance of cooperation and networking to enhance the value of new technology for small retailers.

CONCLUSION

This study has allowed us to identify a number of organizational, structural, and strategic factors that characterize small retailers making greater use of new technologies. While factors such as sector, centralization, and complexity are common to both retail and manufacturing, the specific importance of the status and strategy factors for small retailers should be stressed because these variables are controllable, and can thus be acted upon.

These results are especially important for those wishing to encourage and support the penetration and diffusion of new management and production technologies in the retail trade. Methods, tools, and training should thus be oriented towards increasing the proactiveness and time-frame of small retailers, and demonstrating the advantages of inter-firm cooperation, especially through electronic means and common information infrastructures. From a prescriptive point of view, the relationship between new technology, retailing alliances and organizational strategy should be expanded to include performance, after the initial impetus provided by this research.

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Pierre-Andre Julien is a professor at the Universite du Quebec a Trois-Rivieres and editor of the Revue Internationale P.M.E. Louis Raymond is a professor at the Universite du Quebec a Trois-Rivieres and director of the Research Group on the Economics and Management of Small and Medium-sized Enterprises (GREPME).