Geographical Review of Vol. 68 (Ser. B), No. 1, 86-93, 1995

Spatial Competition and Marketing Strategy of Chains in Tokyo

Kenji ISHIZAKI*

Key words: spatial competition,marketing strategy, nearest-neighbor spatial associationanalysis, fast food chain, Tokyo

distributions of points, while Clark and Evans

I. INTRODUCTION (1954) describe a statistic utilizing only a single set of points. This measure is suitable for de One of the important tasks of retail and res scribing the store distribution of competing taurant chain expansion is the development of firms such as retail chains. a suitable marketing strategy. In particular, a The nearest neighbor spatial association location strategy needs to be carefully deter value, R, is obtained as follows: mined for such chains, where store location is often the key to the successful corporate's R=ƒÁ0/ƒÁE (1) growth (Ghosh and McLafferty, 1987; Jones and The average nearest-neighbor distance, ƒÁ0, is Simmons, 1990). A number of chain entries into given by the same market can cause strong spatial com petition. As a result of this, retail and (2) chains are anxious to develop more accessible and well-defined location strategies. In restau where dAi is the distance between point i in type rant location, various patterns are respectively A distribution and its nearest-neighbor point in presented according to some types of restau type B distribution and dBj is the distance from rants (i.e. general full service, ethnic, fast food point j of type B to its nearest neighbor point of and so on) and stores of the same type are type A. NA and NB are the numbers of points in clustered along roads or in downtown areas each of the two types, and N is the total number (Pillsbury, 1987). It is inevitable that one chain of points (hence N=NA+NB). store will compete with another chain store, The expected mean distance is given by both attempting to scramble for a "good loca tion". However, it is also possible that the segre (3) gation of store distributions appear in conse where nA and nB are the proportions of the total quence of spatial avoidance between chains. points in type A and type B, and ƒÏA and ƒÏB are The purpose of this paper is to analyze spatial the densities of points in each of the two types. competitions of the store distributions in fast R-value less than unity indicates spatial food () chains and to examine the clustering while a value greater than unity indi relationships between spatial competition and cates spatial avoidance. location strategies. This method has been applied to the store distributions of retail chains (see Lee, 1979; Lee II. MEASURE OF SPATIAL COMPETITION and Schmidt, 1980), and the spatial patterns of banking offices (Lord and Wright, 1981), who The nearest-neighbor spatial association a suggest that this measure provides some in nalysis described by Lee (1979) is capable of sights into the competitive location strategies measuring the spatial competition between two of firms (Lord and Wright,1981, p. 192).

* Graduate student , Tokyo Metropolitan University, Hachioji, Tokyo 192-03, Japan Spatial Competition of Fast Food Chains 87

neighbor analysis. III. SETTING There is, however, a problem known as "edge effects" (Boots and Getis, 1988) in that the The empirical setting is identical to that out nearest-neighbor value depends on delimitation lined in Ishizaki (1990), which examined the of the study area. Tokyo (23 wards) is bounded store distributions of four fast food chains for with physical barriers: the southeastern part of Tokyo in 1987, which consisted of 23 wards. the edge is contiguous to Tokyo Bay and the This data is also updated by using additional southwestern and the northeastern parts are information on chain store locations for 1994, limited along large rivers. There is not any obtained using a telephone directory (Yellow barriers in the northwestern part, but nearest Page) as a source. neighbor distances for those points within the This study area has experienced a rapid study area would exhibit little change even growth of fast food stores since the appearance though stores outside of the study area are of McDonald's in 1971. McDonald's and Mos taken into account, due to a higher density Burger have continuously developed to today, store distribution in the northwestern part. while the expansion of two other chains, Lotte ria and Morinaga Love, have been stable (see IV. ANALYSIS Table 1). In particular, the growth of Mos Burger has outstripped that of McDonald's The results of applications of the nearest during seven years from 1987. One reason for neighbor analysis (Clark and Evans, 1954) and this is that Mos Burger has mainly developed a the nearest-neighbor spatial association analy franchising chain operation which can grow sis are shown by Table 21) and Table 3. rapidly in a short time because less capital in Table 2 shows the clustered store distribution vestment is required. of McDonald's and Morinaga Love in 1987. Figure 1 shows the distribution of fast food Both of the nearest-neighbor statistics are sig stores in 1987 and 1994. Fast food stores have nificantly different from unity at the 0.05 level mainly been located along railroads or sub or the 0.01 level. Morinaga Love has a particu ways, especially clustering at terminal stations larly clustering tendency because of a concen or shopping cores such as Shibuya, tration to the south of the city center (see Fig. l ). and Ikebukuro. The concentration of stores on This is perhaps because the stores are agglo western Tokyo also implies that some chains merated around the head office of Morinaga have targeted younger customers because of confectionery, the company which established the relatively high density of population aged Morinaga Love. from 15 to 34 in western Tokyo. The nearest-neighbor spatial association It is clearly indicated that all chains have values in Table 2 indicate that there is some expanded ubiquitously in the whole study area. variance in spatial competition among the dif Therefore the study area definition and the dis ferent pairs of fast food chains. Since all near persion of selected fast food chain stores can be est-neighbor spatial association values for considered an ideal setting to apply nearest McDonald's with other chains are significantly different from unity, these spatial associations Table 1. Number of fast food stores in Tokyo are more clustered than random. This implies that McDonald's stores are actively competing with other chain stores, especially strongly competing with . On the other hand, the store distribution of Mos Burger is relatively weakly competing with each of chains. These results suggest that the different location strat egy of each chain results in a varied store distri bution and also determines the degree of spatial

The figure in parenthesis is the percentage. competition. Ishizaki (1990) recognized the ex 88 K. Ishiaaki Figure 1. Store distributions of four fast food chains in 1987 and 1994 . Spatial Competition of Fast Food Chains 89

Table 2. Nearest-neighbor statistics and nearest-neighbor spatial association values for the fast food stores in 1987

*: Significantly different from unity at the 0 .01 level. **: Significantly different from unity at the 0 .05 level. Nearest-neighbor statistics are on diagonal line.

Table 3. Nearest-neighbor statistics and nearest-neighbor spatial association values for the fast food stores in 1994

*: Significantly different from unity at the 0 .01 level. ** Significantly different from unity at the 0 .05 level. Nearest-neighbor statistics are on diagonal line. istence of different location decisions in each of (see Fig. 1). These results implicitly indicates McDonald's and Mos Burger. McDonald's has that location strategy and site selection of each clustered to large shopping cores as a result of chain have changed since 1987. seeking to capture customers shopping or at work. Thus the stores have competed severely V. DISCUSSION with other chains which prefer a higher density of customers during the day. However, Mos It is often necessary for a retail chain to adopt Burger has favored the residential area with a a new strategy, when market conditions, com higher density of night-time activity and also petitors behavior and corporate environment selected sites with low-priced land values. are changed. The successful retail chain may Therefore the pattern of Mos Burger stores ex explore a number of possible strategies (Jones hibits characteristics of spatial avoidance com and Simmons, 1990, p. 388). Location strategy is pared to the patterns of other chain stores. the most important marketing strategy for Table 3 shows that the spatial patterns of fast retail chain, reflecting information from two food stores in 1994 are different from those in areas: firstly, the internal environment such as 1987. The nearest-neighbor spatial association corporate markets, objectives and capital re values represent different conditions of spatial sources and secondly, the external environ competition, while the nearest-neighbor statis ment, including demographic characteristics, tics slightly differ (see Table 2). The nearest consumer expenditure, and competition (Mer neighbor spatial association values for curio, 1984). It seems that demographic charac McDonald's against Lotteria and McDonald's teristics and consumer expenditure in the exter against Morinaga Love are somewhat greater nal environment are similar for each of fast than those in 1987. On the other hand, the food chains because of similar characteristics values between Mos Burger and McDonald's or such as taste and price. However, the distribu Lotteria indicate clustering tendencies. In fact, tion of population during the day or night McDonald's has located stores dispersedly and differs in a large city area like Tokyo. Each fast Mos Burger has expanded into the shopping food chain would have adopted a specific loca cores where other chain stores have also ag tion strategy which reflects both the internal glomerated during the seven years from 1987 and external environments. 90 K. Ishizaki

In order to examine location strategies of fast from 15 to 34, which are the demographic char food chain stores in the study area, concentra acteristics mainly targeted by almost all chains tion of the store distribution is reported in (Table 4a and Fig. 2)2) and secondly, the coeffi Table 4. The coefficients in Table 4b are ob cient, ci, is obtained by tained in the following manner: firstly, the study area is segmented by the average of day (4) time and night-time population densities aged

Table 4. Relationship between market segmentation and store distribution a. Segmentation of regions by day-time and night-time population densities aged from 15 to 34 Day-time

Night-time Number of wards

-: under the average +: over the average The figure in parethesis is area, sq. km. Data source: Population Census of Japan in 1990

b. Concentration of the store distribution of each fast food chain

Each segment shown by the sign of "+" or "-" follows the above table.

Figure 2. Market segmentation. Spatial Competition of Fast Food Chains 91 where nt is the number of stores in the seg which tends to compete actively with other mented regions i, N is the total number of chain stores. stores, si is an area of the segmented regions i, and S is a total area. VI. CONCLUDING REMARKS The results in Table 4 suggest the following points: (1) McDonald's and Lotteria have The results of this paper suggest some vari adopted location strategies which mainly tar ance and change in spatial competitions among geted day-time population as the demand; (2) fast food chains. Firstly, each of the nearest the location strategy of Mos Burger has neighbor values for all chains shows little changed from that mainly targeting night-time changes through 1987 to 1994, whilst the pat population to both night-time and day-time terns of McDonald's and Morinaga Love exhibit populations and (3) the target market of Mos typical spatial clustering tendencies. Secondly, Burger is apparently different from that of Mo the nearest-neighbor spatial association values rinaga Love. According to the classification of suggest spatial clustering for McDonald's competitive status suggested by Kotler (1994), against Lotteria and spatial avoidance for Mos Lotteria as a "follower" has imitated the strate Burger with other chains in 1987. Thirdly, Mos gy of McDonald's as a "leader" and Mos Burger Burger tends to compete with other chains in has targeted markets (regions) which have few 1994. stores of other chains present as a "nicher" until Various spatial competitions indicate differ 1987, whilst adopting an offensive strategy as a "challenger" afterwards ent location strategies, such as selecting target . This strategic change markets and competitive status for each chain. of Mos Burger was already confirmed in Ishi When markets are segmented by day and night zaki (1990) and the tendency would have been activity in the study area, McDonald's, Lotteria increasingly accelerated until 1994. and Morinaga Love have targeted the markets Thus the difference of target markets se with predominantly day-time activity, while lected by each chain reflects distinct location Mos Burger has favored those with night-time strategies and leads to varied spatial competi activity in 1987. However, Mos Burger has also tion. For example, the nearest-neighbor spatial penetrated the markets with day-time activity association values for Mos Burger against Mo with their modified location strategy in 1994. rinaga Love in Tables 2 and 3 indicate spatial Strategic change, as can be seen in Mos avoidance or spatial segregation. In contrast, Burger's behavior, has given rise to the new the spatial competition between chain stores condition of spatial competition. Fast food mar targeting the similar markets tends to spatial kets can change dramatically with interaction clustering. Selecting target market may be de between spatial competition and marketing termined by the condition of competition in the strategy. external environment and the corporate objec tives within the internal environment for each Acknowledgments chain. Location strategy needs to be modified in The author wishes to thank Professor Yoshio Sugi response to changes in the internal and external ura of the Department of Geography, Tokyo Metro environments. It is suggested that Mos Burger politan University, for his advice and encouragement. has altered location strategy markedly because (Received May 21, 1995) of changes in the internal environment, such as (Accepted June 17, 1995) modified target market (see Table 4) and in creased capital resources from rapid growth. Notes Competitive status shift can often result from capital accumulation (Shimaguchi, 1984, p. 1) Nearest-neighbor spatial association values in Table 2 are slightly different from those in pre 248). The change of spatial competition for Mos vious paper (Ishizaki, 1990). This reason is that Burger as described in the previous section may the distance between stores was calculated by be due to the new location strategy undertaken, mesh units (about 500 m square) in Ishizaki 92 K. Ishizaki

(1990) as opposed to store sites in this paper. ment. Routledge, 492 p. 2) The data for day-time and night-time population Kotler, P. (1994): Marketing management: analysis, densities are obtained from Population Census of planning, implementation, and control. 8th ed., Japan in 1990, the middle year between 1987 Prentice-Hall, 801 p. and 1994. Selecting an age group between 15 Lee, Y. (1979): A nearest-neighbor spatial association and 34 is based upon a hearing survey in Ishi measure for the analysis of firm interdependence. zaki (1990). Environment and Planning A, 11, 169-176. Lee, Y. and Schmidt, C. G. (1980): A comparative References location analysis of a retail activity: the gasoline service station. The Annals of Regional Science, 14, Boots, B. N. and Getis, A. (1988): Point pattern analysis. 65-76. SAGE Publications, 93 p. Lord, J. D, and Wright, D. B. (1981): Competition and Clark, P. J. and Evans, F. C. (1954): Distance to nearest location strategy in branch banking: spatial avoid neighbor as a measure of spatial relationships in ance or clustering. Urban Geography, 2, 189-200. Mercurio, J. (1984): Store location strategies. Davies, populations. Ecology, 35, 445-453. Ghosh, A, and McLafferty, S. L. (1987): Location strat R. L. and Rogers, D. S. eds.: Store location and store egies for retail and service firms. Lexington Books, assessment research. John Wiley & Sons, 237-262. 212 p. Pillsbury, R. (1987): From hamburger alley to hedge Ishizaki, K. (1990): The geographical development of rose heights: toward a model of restaurant location fast food stores in Tokyo city area from viewpoint dynamics. Professional Geographer, 39, 326-344. of locational policy. Annals of the Japan Association Shimaguchi, M. (1984): Senryakuteki maketingu no of Economic Geographers, 36, 129-140. (J) ronri (Logic of strategic marketing). Seibundo shinkosha, Tokyo, 308 p. (J) Jones, K. and Simmons, J. (1990): The retail environ Spatial Competition of Fast Food Chains 93

ファース トフー ド・チェーンにおける空間的競合とマーケティング戦略

石 崎 研 二*

本 稿 で は,東 京 都23区 に お け る フ ァ ー ス トフー ド店 して,各 チ ェー ンが いか な る市 場 を タ ーゲ ッ トと して い の 空 間 的 競 合 の 分 析 を 行 な い,そ の 立 地 戦 略 との 関係 に る か を検 討 した。そ の結 果, 1987年 で は,マ ク ドナル ド つ い て 考 察 した 。 最 近 隣 尺 度 お よび 最 近 隣 随伴 尺度 を用 お よ び ロ ッテ リア は昼 間 人 口 を指 向 し,モ ス バ ー ガ ー は い て, 1987年 と1994年 に お け る店 舗 の 分 布 を 判 定 し 夜 間 人 口 を指 向 す るが, 1994年 に お いて は,モ ス バ ー た 結 果,次 の よ う な こ とが わ か っ た。(1)最 近 隣 尺 度 は ガ ー は他 の チ ェ ー ン店が タ ー ゲ ッ トとす る昼 間人 口密 度 1987年 と1994年 で さ ほ ど変 わ りが な く,マ ク ドナ ル が 卓 越 した 市 場 へ も進 出 を始 め て い る。 この よ うな 各 ドと森永 ラ ブに は特 徴 的 な 集 積 パ ター ンが み られ る。(2) チ ェ ー ンにお け る タ ーゲ ッ ト市場 の 相違 は,競 争 上 の地 最 近 隣 随伴 尺 度 に よ れ ば,各 チ ェー ン間 の組 み合 わ せ に 位 や 企 業 の 目標 な どの 違 い に起 因 す る立 地戦 略 の差 異 を よ って ば らつ きが あ り,特 に マ ク ドナル ドと ロ ッテ リア 反 映 して い る。 ま た,モ スバ ー ガー に み られ る よ うに, の空 間 的競 合,モ ス バ ー ガ ー と他 の チ ェー ン との 回避 が タ ーゲ ッ ト市場 の 変 更 や 資 金 の 増 大 に伴 い,立 地 戦 略 を 顕 著 で あ る。(3)特 に モ スバ ー ガ ー は, 1994年 で は,他 変 更 す る企 業 もあ る。 先 に検 証 した 様 々 な 空 間 的 競 合 の チ ェー ン と集積 す る傾 向 に あ り,店 舗 の立 地 変 化 が確 は,タ ーゲ ッ ト市場 の 選 定 に 代 表 され る立地 戦 略 の相 違 認 で きる。 次 に,フ ァー ス トフー ドが対象 とす る,年 齢 を 表 わ して い る と いえ よ う。 15～34歳 の 昼 間 人 口密 度 と夜 間 人 口密 度 で 地 域 を分 割

* 〒192-03東 京都八王子市南大沢1-1東 京都立大学大学院