emerald_apjml_apjml558046 344..364

The current issue and full text archive of this journal is available at www.emeraldinsight.com/1355-5855.htm

APJML 26,3

344

The performance implications of perceptual differences of dependence in marketing channels

Received 20 December 2013 Revised 20 December 2013 Accepted 6 March 2014

The mediating role of trust Zhilin Yang and Fang Jia Department of Marketing, City University of Hong Kong, Kowloon Tong, Hong Kong, and

Shaohan Cai School of Business, Carleton University, Ottawa, Canada Abstract Purpose – The purpose of this paper is to address two essential questions: do perceptual differences regarding dependence matter in determining channel performance, and if so, how? Design/methodology/approach – The paper conducted an empirical study of 347 cellular telephone supplier-retailer dyads in China. A questionnaire survey was employed. Findings – The results reveal that a retailer’s perceptual difference of dependence exerts a significant effect on its evaluation of supplier performance only. Retailer trust partially mediates the effect of the perceptual differences on supplier performance and retailer performance. Therefore, the particular side of a dyadic relationship that researchers choose to study matters in an unbalanced dependence relationship. Practical implications – Managers, depending on their side, should pay close attention to perceptual differences and their consequences and deliberately employ different strategies to ensure effective channel management. Originality/value – Do differences in parties’ perceptions of dependence influence channel performance? If they do, how do these perceived differences exert direct and indirect impacts? By answering these questions, the authors contribute not only to an understanding of the unique nature of dyadic channel relationships but also to methodological notions about whether to study one side in a dyad. Keywords B2B marketing, Channel relationships, Marketing in China Paper type Research paper

Dependence is a fundamental construct in prominent organizational theories, including exchange theory, resource dependency, and embeddedness; it is particularly crucial for understanding interorganizational relationships and performance (Bucklin and Sengupta, 1993; Hibbard et al., 2001). Extensive research focusses on how organizations benefit from being in a position of power and claiming greater value in a distributive process (Casciaro and Piskorski, 2005; Kim, 2000). Yet managers remain Asia Pacific Journal of Marketing and Logistics Vol. 26 No. 3, 2014 pp. 344-364 r Emerald Group Publishing Limited 1355-5855 DOI 10.1108/APJML-12-2013-0154

The authors thank the participants of research seminars at City University of Hong Kong and Southwest University of Finance & Economics for their helpful comments on previous versions of this paper. The authors gratefully acknowledge a grant from the Research Grant Council of Hong Kong SAR (Project No. 9041618, CityU 152110) and a grant from City University of Hong Kong (CityU SRG Project No. 7008124).

amazingly inaccurate in gauging their relationships with partners, even when both sides know the other well (Davis et al., 1986). For example, in dyadic business relationships, key informants diverge in their perceptions of goal compatibility, evaluations of accomplishments, and even norms of evaluation ( John and Reve, 1982). Similarly, channel managers from different sides tend to express perceptual differences of their relative dependence on each other, mainly caused by their own needs and circumstances, as well as by the reality of the situation. Previous studies confirm that these perceptual differences play important roles in shaping the image of counterparts and thus affect interfirm trust and relational performance (Vosgerau et al., 2008). Such perceptual differences, according to Vosgerau et al. (2008), can also hinder the functioning of the relationship by worsening the framing of a counterpart, which leads to weakened images and goal incongruence. Most studies that consider perceptual differences and their consequences focus on relationship dimensions, such as commitment and trust (Ross et al., 1997; Vosgerau et al., 2008), without addressing the dependence structure of a dyad or its impacts on channel relationships and performance. Instead, previous studies examine perceived interdependence and dependence asymmetry on either the buyer side (Geyskens et al., 1996; Kumar et al., 1995; Palmatier et al., 2007) or the supplier side (Gulati and Sytch, 2007). In turn, we examine two essential research questions for this context: RQ1. Do differences in parties’ perceptions of dependence influence channel performance? RQ2. If they do, how do these perceived differences exert direct and indirect impacts? By answering these questions, we contribute not only to an understanding of the unique nature of dyadic channel relationships but also to methodological notions about whether to study one side in a dyad. We begin by providing theoretical rationales for our hypotheses, followed by an empirical test with a sample of 347 retailer-supplier dyads from China. We discuss our findings and provide managerial and theoretical implications to conclude. Theory and hypothesis Perceptual difference of dependence Theorists working on resource dependence, social exchange, and social embeddedness use both interdependence and dependence asymmetry to capture the dependence structure of an exchange (Casciaro and Piskorski, 2005; Gulati and Sytch, 2007). Empirical results show that interdependence enhances relational outcomes and firm performance, because partners must work together to maintain their relationships and avoid destructive actions (Hibbard et al., 2001; Kumar et al., 1995). Dependence asymmetry instead undermines interfirm relationships, because powerful firms have few structural barriers to the use of coercive influences (Gundlach and Cadotte, 1994; Jap and Ganesan, 2000). To measure these two constructs, most studies rely on perceptions of channel members from one side only, even though both psychologists and sociologists confirm that people often make errors in their perceptions of partners and themselves. For example, Kenny and Acitelli (2001) note that partners in a close relationship can be very biased in their perceptions, reflecting both the needs and

Perceptual differences of dependence 345

APJML 26,3

346

circumstances of the judge, as well as the reality of the situation (Kenny and Albright, 1987). According to this logic, channel managers in a dyad likely express perceptual differences regarding their dependence on each other (we thereafter coin the term as “perceptual differences of dependence”). But do these perceptual differences matter? Put another way, is it sufficient to use measures reported by managers from one side, without taking perceptual differences into account? Despite the significant implications of this question for organizational theory and research methodology, little research has examined the consequences of perceptual differences for relational outcomes and firm performance. In one exception, Ross et al. (1997) explore the causes and consequences of perceived commitment asymmetry and find that each party’s perception of asymmetric commitment only partially reflects their actual asymmetry. Perceivers rate the performance outcomes from the dyad higher when they believe that they are less committed than their counterpart but lower when they believe that they are more committed. With matching levels of perceived commitment, channel conflicts decline, and performance outcomes improve. In another study, Vosgerau et al. (2008) find that erroneously overstating a counterpart’s relational closeness strongly improves the image that the judge holds of that counterpart, as well as its perception of goal congruence; erroneously understating this relational closeness has a strong negative effect. Both studies thus demonstrate that perceptual differences have distinct effects on each side’s perception of its partner and the relationship overall. To extend this research stream, we focus on perceptual differences pertaining to the dependence structure between retailers and suppliers, as we show in Figure 1. We define the perceptual difference of retailer dependence (PDRD) as the difference between a retailer’s perception of its dependence on its supplier and the supplier’ perception of the retailer’s dependence on it. Similarly, the perceptual difference of supplier dependence (PDSD) refers to the difference between a retailer’s perception of its supplier’s dependence on it and the supplier’s perception of its dependence on the

(1) Retailer perceives

Perceptual Differences of Retailer Dependence

dependence on

(PDRD)

supplier (Andaleeb, 1996)

(2) Suppliers perceives retailer’s dependence on it

(This research)

Figure 1. Perceived difference of dependence

Retailer perceives interdependence

Manufacturer perceived

and dependence

interdependence and

asymmetry (Geyskens et al., 1996;

dependence asymmetry

Kumar, et al., 1995; Palmatier et al., 2007)

(Gulati and Sytch, 2007)

(3) Retailer perceives supplier’s dependence on it

Perceptual Differences of Supplier Dependence (PDSD) (This research)

(4) Supplier perceives dependence on retailer

retailer. Both PDRD and PDSD should have direct effects on channel performance, and we anticipate that trust may play a mediating role. To discuss the performance implications of perceptual differences, we take the perspective of retailers, for simplicity. We also rely on two key assertions from prior research to develop our hypotheses. First, the more dependent party should make more effort than the less dependent party to maintain the business relationship. Monczka et al. (1995) find that a supplier’s greater dependence on a buying firm results in greater information sharing behaviors. A more dependent channel party also tends to exert more effort to build relational bonds (Ganesan, 1994). Second, we anticipate that any effort made by a party can be perceived and appreciated by its counterpart (Brown and Leigh, 1996). Efforts by a more dependent party should prompt positive reactions from its partner (Bello et al., 2010), leading to better performance. In contrast, lesser dependence leads a party and its partner both to exert less effort. Thus both sides perform poorly. Does the perceptual difference of dependence matter? Theoretically, a positive PDRD exists in five scenarios (see Figure 2(a)): (1)

a retailer overestimates its dependence, but its supplier does not (assuming an objective level);

(2)

a supplier underestimates its retailer’s dependence, but its retailer has no perceptual error;

(3)

a retailer overestimates its dependence, and its supplier underestimates the retailer’s dependence;

(4)

both the retailer and its supplier overestimate dependence, but the retailer’s overestimation is greater than its supplier’s; and

(5)

both the retailer and its supplier underestimate dependence, but the retailer’s underestimation is smaller than its supplier’s.

In scenarios 1, 3, and 4, the retailer’s relative overestimation of its dependence on the supplier may motivate it to leverage its firm capabilities and resources to compete effectively and maintain the business relationship (Buchanan, 1992; Ganesan, 1994). For example, it might engage in more cooperation and communication with its supplier, which make it more aware of and better able to satisfy the supplier’s needs. More frequent interactions with the supplier and shared customer information also lead better price controls. If it faces intensive competition, the retailer also must continuously improve its product or service quality if it hopes to maintain its relationship with a less dependent supplier (Hunt, 1997). Close cooperation with the supplier, better price controls, and efforts to improve service quality then eventually improve the retailer’s financial performance. In scenarios 2 and 5, the supplier instead underestimates its retailer’s dependence, so it is inclined to commit more resources to maintaining the relationship. It might offer in-time delivery, strong technical and after-sales support, or more discounts, which then lead to better supplier performance. However, if a retailer underestimates its dependence on the supplier or a supplier overestimates the retailer’s dependence, the motivation to cooperate declines. These scenarios therefore imply diminished retailer and supplier performance, respectively, due to their minimal efforts to improve their own performance in the dyad.


APJML 26,3

(a)

Retailer Dependence on Supplier (PDRD) (1)

(2)

(3)

(4)

(5)

Retailer’s PDAD

348

Objective retailer dependence

Retailer perceived its dependence on supplier Supplier perceived retailer’s dependence

(b)

Supplier Dependence on Retailer (PDSD) (1)

(2)

(3)

(4)

(5)

Retailer’s PDPD Objective supplier dependence

Figure 2. Five possible scenarios of positive perceived differences

Retailer perceived supplier’s dependence Supplier perceived its dependence on retailer

Because we predict that any added efforts by a channel member lead to concomitant input from business partners, and therefore improved performance for both sides, we anticipate that in all five scenarios, higher PDRD leads to better channel performance for both sides. Therefore: H1. Retailers’ PDRD has a positive effect on retailer performance and supplier performance.

We outline five parallel scenarios for the perceptual difference of supplier’s dependence on the retailer (PDSD) (see Figure 2(b)): (1)

a retailer overestimates its supplier’s dependence, but the supplier does not;

(2)

a supplier underestimates its dependence, but the retailer does not;

(3)

a retailer overestimates supplier dependence, and its supplier underestimates its dependence;

(4)

the retailer’s overestimation of supplier dependence is greater than the supplier’s overestimation of its dependence; and

(5)

the supplier’s underestimation of its dependence is greater than the retailer’s underestimation of supplier dependence.

When a retailer overestimates its supplier’s dependence on it (scenarios 1, 3, and 4), it has less motivation to be cooperative or supportive in its dealings with the supplier (Brass and Burkhardt, 1993). As the opportunity for this retailer to use coercive influence strategies increases, so does conflict (Kim, 2000). In the resulting noncooperative relationships, retailers cannot be innovative, expand their markets, or reduce their costs (Cannon and Homburg, 2001; Rindfleisch and Moorman, 2001). Furthermore, a retailer with a dependence advantage is less willing to exert effort on service quality and price coordination. As a result, retailer performance suffers. In contrast, if a retailer underestimates its supplier’s dependence, it feels less capable of recovering from the potential loss of an imbalanced relationship, which gives it motivation to build good relationships with its supplier. If a supplier underestimates its dependence (scenarios 2 and 5), it is less willing to provide after-sales services, technical support, or on-time delivery to the retailer. Such behaviors reduce the efficiency and effectiveness of cooperation; thus, supplier performance should suffer. In line with our notion of a vicious circle of reduced effort by a less dependent party, we logically predict that in all scenarios, higher PDRD results in poorer channel performance for both sides. That is: H2. Retailers’ PDSD has a negative effect on retailer performance, and supplier performance. The role of trust In addition to a direct effect on channel performance, perceptual differences might affect performance in that they distort the effects of actual interdependence and dependence asymmetry. In particular, retailers’ PDRD should increase their trust in the supplier, whereas PDSD should decrease retailer trust. In addition, trust is an immediate precursor and important driver of exchange performance; therefore, retailer trust may mediate the relationship between perceptual differences and channel performance. Positive PDRD resulting from a retailer’s overestimation of its dependence on the supplier leads that dependent retailer to desire more extensive personal interactions (Brass and Burkhardt, 1993), information exchange, and resource integration. Increasing these interactions make channel members more aware of each other’s capabilities and benevolence, thus encouraging mutual trust. If one member’s task dependence on another increases, it also leads to interpersonal trust


APJML 26,3

350

(de Jong et al., 2007). High dependence induces channel members to work closely and exert more effort to meet partner’s needs (Anderson and Williams, 1996), such that they are more willing to trust each other. A supplier’s underestimation of retailer dependence also could lead to positive PDRD, because the supplier tends to underestimate its power. Its perceived dependence disadvantage makes the supplier less likely to use coercive power; in turn, the retailer is more willing to trust it (Anderson and Barton, 1989; Casciaro and Piskorski, 2005; Kim, 2000). Therefore, a supplier’s underestimation of retailer dependence increases the retailer’s trust in the supplier. When PDRD is negative, the retailer underestimates its power and overestimates its dependence. According to social exchange theory, exchanges vary directly with dependence, so more dependent dyad members trust in each other more than do less dependent ones. Using resource dependence theory (Pfeffer and Salancik, 1978), Casciaro and Piskorski (2005) also show that power imbalances can block mergers and acquisitions. Whereas interdependence increases the manufacturer’s performance, a manufacturer with a dependence advantage often suffers poorer performance (Gulati and Sytch, 2007). The less dependent member, by using its power and coercion, can reduce the total value generated in the relationship. Furthermore, the use of coercion can inflict irrevocable damage on the cooperative spirit of the relationship (Casciaro and Piskorski, 2005). In line with resource dependence theory, we therefore propose: H3a. Retailers’ PDRD increases retailers’ trust in suppliers. Positive PDSD also can exert negative effects on retailers’ trust, because their overestimation of their partner’s dependence leads those retailers to believe they have a dependence advantage. Dependence advantage and interdependence have opposite effects; for example, Kumar et al. (1995) indicate that interdependence positively affects commitment and trust by reducing relationship problems and interest convergence, whereas dependence asymmetry undermines them as partners’ interests diverge and they consider using coercive power. In a distribution channel with dependence imbalance, the more dependent party is vulnerable to the whims of the less dependent party; it faces higher switching costs and is less able to recover from the loss of an unsuccessful effort. Channel members with a dependence advantage also may prefer coercive control over trust. A retailer that overestimates its supplier dependence perceives an even greater dependence advantage. This low level of interdependence gives neither party a reason to invest time or effort on close interactions (Anderson and Weitz, 1992). Thus, with an erroneously perceived dependence advantage, a retailer may cooperate less with its supplier and prevent the development of mutual trust. When the supplier underestimates its own dependence, it similarly becomes less cooperative and more coercive. Such behaviors destroy the closeness of the relationship and damage retailer trust. Thus, we propose: H3b. Retailers’ PDSD decreases their trust in suppliers. Finally, according to the modern contractual relation paradigm (Macneil, 1980), buyer-seller relationship models (Dwyer et al., 1987), and social exchange theory (Blau, 1968), trust is a focal determinant of interorganizational relationship performance (Morgan and Hunt, 1994). Interorganizational trust, as a governance mechanism

(Bradach and Eccles, 1989), also encourages appropriate behaviors in exchange relationships characterized by uncertainty and dependence (Doney and Cannon, 1997; Madhok, 2006), lowers transaction costs (Gulati, 1995), reduces the need for formal contracts (Larson, 1992), facilitates dispute resolution (Ring and Ven, 1994), and ultimately leads to increased performance outcomes (Dyer and Singh, 1998; Morgan and Hunt, 1994). In a longitudinal study, Palmatier et al. (2007) show that trust positively affects both financial and relational outcomes. In marketing channels, trust enhances performance too, because customers are more likely to act in the best interest of a committed, well-trusted supplier (Anderson and Weitz, 1992; Hibbard et al., 2001). Thus, we propose: H4. Retailers’ trust in a supplier has a positive effect on retailer performance and supplier performance. Methodology Sample and data collection We test our hypotheses by collecting dyadic data from purchasing managers from the retailer side and sales managers from a corresponding supplier in the cellular telephone industry in Mainland China. The sample of 1,000 retailers, located in various cities in China, was randomly selected from a list provided by a marketing research firm specializing in telecommunication services. For each retailer, the purchasing manager who interacts directly with suppliers served as the key informant. These managers indicated whether they were willing to participate in our study and identify suppliers; we then contacted the sales managers responsible for those retailers. The questionnaires were mailed directly to the purchasing and sales managers, who completed the questionnaire independently and mailed it directly back to us. After pairing the questionnaires for each dyad, we determined we had received responses from 386 dyads. After excluding incomplete questionnaires and respondents with insufficient knowledge of the relationship (less than the mean of 3), we retained 347 dyads for further analysis, for an effective response rate of 34.7 percent. The respondents were located in 28 different provinces in Mainland China, and 83 percent of sales managers and 78 percent of purchasing managers were women. Most of the respondents were between 25 and 34 years of age (75 percent of sales managers, 58 percent of purchasing managers). Regarding the types of retailers, 12 percent were wholesalers that sell the cellular devices to retailers, and 88 percent sell directly to consumers. On average, the sales managers had worked 2.1 years with their company and 2.7 years in the industry; purchasing managers had worked an average of 4.9 years with their companies and 5.7 years in the industry. Finally, 8 percent of the dyads had existed for less than a year; 42 percent lasted for one to three years; 38 percent for three to five years; 11 percent for five to eight years; and 1 percent persisted for more than eight years. We assess possible nonresponse bias in two ways. First, we conduct tests comparing early and late respondents in terms of demographic information and study constructs. The results indicate that early versus late respondents constitute the same populations ( p40.05). Second, we compare the respondent pools with the total sampling frames. Again, we find no significant differences ( p40.05). The results of these three tests suggest that nonresponse bias is not a concern.


APJML 26,3

352

Measures All the measures in our research model appear in the Appendix. To develop the questionnaire, we first attempted to obtain validated scales from previous studies. If such scales were not available (e.g. retailer and supplier performance), we developed our own through five field interviews and two focus groups. The survey questionnaires for sales managers and purchasing managers both were prepared in English first then translated to Chinese; two independent experts fluent in both English and Chinese translated and back-translated them to ensure accuracy and consistency. Three marketing experts then reviewed both questionnaires. In a pretest with 32 sales and purchasing manager dyads, we obtained feedback about the design and wording of the questionnaire items. The scales for all variables, except the scale for performance (anchored at 1 ¼ “worst” to 5 ¼ “best”), anchored at 1 (“strongly disagree”) to 5 (“strongly agree”) anchors. Dependence refers to the degree to which a partner is irreplaceable in the trading area (Frazier and Rody, 1991). A retailer’s dependence on its supplier and a supplier’s dependence on its retailer thus can be measured by the opportunity costs of any value that would be lost if the relationship ended, together with the switching costs associated with termination and replacement. We averaged each retailer’s dependence responses, omitting any that indicated the item was “not applicable.” The resulting formative indicator indicates the retailer’s perception of its dependence on supplier; we followed similar approaches to obtain the formative indicators of the retailer’s perception of supplier dependence, the supplier’s perception of its dependence on the retailer, and the supplier’s perception of retailer dependence. Then the retailer’s PDRD and PDSD can be calculated as the relevant differences between the scores. Consistent with prior research (Kumar et al., 1995; Morgan and Hunt, 1994), we measured retailers’ trust as benevolence and adapt three items from Palmatier et al. (2007). Specifically, we asked the retailer whether its supplier stands by its word, keeps its promises, and is sincere in their business relationship. To make our measure specific to the research setting, we asked about channel performance in field interviews and focus groups with both purchasing and sales managers. Of the various indicators of channel performance, we chose those most frequently mentioned by the managers. Supplier performance is measured by on-time delivery, meeting target costs (i.e. actual costs of the purchased items versus expected costs and sales), and service or technical support. Retailer performance, as rated by the supplier, also contains three items: service quality, price control (i.e. degree to which retail prices are within price ranges), and profits from selling the supplier’s products. To minimize bias, the corresponding purchasing managers evaluated supplier performance, while sales managers rated retailer performance (Podsakoff et al., 2003). We also included several important control variables. First, personal relationships and contact frequency can significantly affect business relationships (Frazier and Rody, 1991; Mohr and Nevin, 1990). Second, the company’s size, type, and capabilities could play critical roles for performance. We measured firm size as the number of employees. We asked the sales managers to evaluate the retailers’ capability; the retailer managers reported their firm type (wholesaler vs retailer). Third, asset specificity may affect a partner’s trust and interdependence (Zaheer and Venkatraman, 1995), so we controlled for the supplier’s asset specificity in our data analysis process.

Common method variance Most of the firms in our sample are small, so we had difficulty finding duplicate key informants, and one respondent provides all the answers for each firm. Thus common method variance may exist (Podsakoff et al., 2003). To minimize this bias, we carefully designed the procedural controls for the survey, including anonymous submissions and minimal ambiguity in the measurement items. We conducted two tests of common method variance (Poppo et al., 2008). Harmon’s one-factor test (Podsakoff et al., 2003) reveals that a one-factor model achieves a poor fit (w2(377) ¼ 1638, po0.001; comparative fit index (CFI) ¼ 0.37), and the first factor derived from an oblique factor analysis of the 29 items explains 15 percent of the total variance, while seven7 factors explain 55 percent. These results suggest common method bias is not a significant concern for our study (Podsakoff et al., 2003). Furthermore, of the 36 correlations between the nine constructs, 18 were not significant, and six of the 18 were negative, which implies the validity of the other correlations (Lindell and Whitney, 2001). The collected evidence thus suggests the common method variance effect is unlikely to be significant. Reliability and validity We refined the measures and assessed their construct validity following the guidelines suggested by Anderson and Gerbing (1988). Reliability analyses show that these measures possessed satisfactory coefficient reliability. Except for retailer performance as evaluated by sales managers – at 0.67, slightly below the recommended level – all composite reliabilities are 40.7, in support of internal reliability (Nunnally and Bermstein, 1994). We ran confirmatory factor analyses for an overall model with all the variables. The confirmatory models fit the data satisfactorily (overall: w2(254) ¼ 416.4, po0.001; CFI ¼ 0.91; Tucker-Lewis index ¼ 0.89; and root mean square error of approximation ¼ 0.05), indicating the unidimensionality of the measures (Anderson and Gerbing, 1988). All factor loadings are significant and in the predicted direction ( po0.001), in support of convergent validity. To assess the discriminant validity of the measures, we examined the cross-construct correlations and constructed 95 percent confidence intervals for each correlation coefficient. The intercorrelations were significantly ( po0.05) o1, in support of the measures’ discriminant validity. Overall, our results show that the study measures possess satisfactory reliability and validity. We present the means, standard deviations, and correlations of the constructs in Table I. Results Consistent with prior arguments, the results in Figure 3 confirm that perceptual differences about dependence are common. More than two-thirds of retailers’ PDRD is negative (i.e. the difference score is o0), which means that most retailers underestimate their dependence on their supplier. The mean PDSD score is significantly 40; retailers tend to overestimate suppliers’ dependence on them. Compared with suppliers then, retailers tend to underestimate their own dependence but overestimate the supplier’s dependence on them. As a result, most retailers perceive a greater dependence advantage than do suppliers. We employ three series of hierarchical multiple regression analyses to test our hypotheses, with retailer performance, supplier performance, and retailer trust as the dependent variables (see Table II). The results do not support H1a, because a retailer’s


3.16 3.52 3.02 3.84 0.67 0.49 3.84 3.65 3.74

1. 2. 3. 4. 5. 6. 7. 8. 9.

0.79 0.72 0.70 0.73 – – 0.71 0.71 0.67

Reliability 1 0.01 0.01 0.30** 0.69** 0.01 0.40** 0.39** 0.19**

1

1 0.15* 0.02 0.02 0.62** 0.13* 0.19* 0.10

2

1 0.02 0.01 0.69** 0.02 0.00 0.11

3

1 0.48** 0.00 0.05 0.09 0.13*

4

1 0.01 0.33** 0.29** 0.04

5

1 0.11 0.14* 0.01

6

1 0.57** 0.18*

7

1 0.14*

8

1

9

Notes: PDRD, perceptual differences of retailer dependence on supplier; PDSD, perceptual differences of supplier dependence on retailer. *,**Correlation significant at 0.05 and 0.01 level (two-tailed), respectively

0.80 0.71 0.77 0.74 0.98 0.97 0.76 0.64 0.51

SD

354

Retailer’s dependence on supplier Retailer perceived supplier’s dependence Supplier’s dependence on retailer Supplier perceived retailer’s dependence PDRD PDSD Retailer trust Supplier performance Retailer performance

Mean

Table I. Descriptive statistics and correlation

Variable

APJML 26,3

PDRD

PDSD

Total

0

98

194

Mean

–0.47

0.38

1.07

1.03

Std. Deviation


40.0

Percent

30.0

20.0

10.0

[–

) >= 1. 5

,1 [0

.5

5, 0.

5, 1.

[–

.5

5) 0.

.5

)

–0

.5

) [–

2.

5, 3.

5,

–2

–1

.5

5 3. = 2. 5

.5 ,2 .

5)

) [1

1. 5 [0 .5 ,

)

)

0. 5 [– 0. 5,

,– 0. 5

[– 1. 5