power, relationship commitment and supply chain integration

Power, Relationship Commitment and Supply Chain Integration with Customers in China

Baofeng Huo

Xiande Zhao

Jeff Hoi Yan Yeung

Jan. 28, 2005

Agenda

Introduction

Literature Review

Research Methodology

Discussion

Conclusions & Limitations

Introduction

• Many papers identified different types of SCI (Markham, 2001, Narasimhan, 2001, Stank, 2001, Johnson, 1999, Morash, 1998).

• Some papers analyzed the relationship between SCI and SC performance (Narasimhan, 2002, Armistead, 1993).

• Beth et al. (2003) advocated that trust and relationship commitment are placed in the highest priorities in achieving SCI.

• Morgan and Hunt (1994) proposed that relationship commitment is crucial for integrating SC partners into their key customers’ business processes and established goals.

• Maloni and Benton (2000) found that power plays a significant role in SCM, and the different sources of power have different impacts on inter-firm relationship in the SC.

• Brown, et al., (1995) empirically tested the impact of power and relationship commitment on marketing channel member performance.

• Few papers examined the factors that influence SCI and how these factors influence SCI.

Research Objectives

• To offer a comprehensive review of power, relationship commitment, SCI with customer, and manufacturer performance.

• To identify the key factors (power, and relationship commitments) that influence SC customer integration.

• To propose and test a model that represents the relationships among power, relationship commitment, customer integration, and firm performance.

Literature review

• Supply Chain Integration • Power

• Relationship Commitment

• Performance

Supply Chain Integration

• Much literature has stated the importance of SCI for achieving competitive advantages (McGinnis & Kohn, 1993; Clinton & Closs, 1997), and operational performance (Ahmad & Schroeder, 2001; Frohlich & Westbrook, 2001; Stank, et al., 2001).

• SCI could be defined as the degree to which the firm can strategically collaborate with their SC partners and collaboratively manage the intra- and inter-organization processes to achieve the effective and efficient flows of product and services, information, money and decisions with the objective of providing the maximum value to the customer at low cost and high speed (Bowersox, Closs & Stank, 1999; Towill & McCullen, 1999; Frohlich & Westbrook 2001; Vaart & Donk, 2003).

• Morash & Clinton (1998) investigated and compared two types of SCI: external (customer and supplier) and internal (process reengineering) integrations for about two thousand global firms.

• Markham (2001) investigated supplier and customer integration strategies in a global sample of 322 manufacturers.

• Stank, Keller & Daugherty (2001) developed and tested an instrument for measuring SCI competences as well as evaluating their relative importance to developing logistic distinctiveness.

Power

• Dapiran & Scott (2003) suggested that power is an element of any relationship. Power can be defined as the ability of one channel member to influence the marketing decisions of another channel member.

• Cox (2001) illustrated that power is at the heart of the trans-organizational relationships.

• Benton and Maloni (2005) investigated the supplier satisfaction in power driven buyer–supplier relationships.

• Maloni and Benton (2000) examined the detrimental and beneficial effects of power on the ability to build integrated, high-performance buyer-supplier relationships in the supply chain.

• Brown, et al., (1995) empirically investigated the impact of power and relationship commitment on marketing channel member performance from the relationship marketing perspective.

• Goodman and Dion (2001) argued that power was becoming one of the important determinants of relationship commitment in the distributor-manufacturer relationship.

Relationship Commitment

• Barber (1983) and Morgan & Hunt (1994) suggested that the propensity for relational continuity and the establishment of long-term relationship are primarily in the theme of “relationship commitment”.

• Relationship commitment can be defined as the willingness of a party to invest resources into a relationship (Dion et al. 1992; Morgan & Hunt, 1994).

• Gundlach, et al. (1995) pinpointed its importance for developing and sustaining successful relational exchange.

• Relationship commitment can be identified into two levels: interpersonal commitment and organizational commitment (Hornby, 1995).

• Organizational commitment could be categorized into Intra-organizational (Porter et al., 1974; Mowday et al., 1982) and inter-organizational commitment (Cheng et al., 2004).

• Handfield & Bechtel (2002) studied the role of trust and relationship structure in improving SC responsiveness using data from North American manufacturing firms.

Performance

• Financial performance has been widely used as a key measure of firm performance (Boyer et al, 1997; Boyer, 1999, Chen & Paulraj, 2004). However, much literature (e.g. Dixon et al., 1990; Eccles & Pyburn, 1992) has pinpointed the limitations in relying solely on financial performance measures in SC.

• Some recent SCI studies (Tan et al., 1998; Vickery et al., 2003) have used both operational and financial performances as indicators for the organizational performance.

• However, many SCI studies have measured either operational (Scannell et al., 2000; Stank, et al., 2001) or financial performance outcomes (Ross, 2002).

Research methodology

CMP

CNRC

CIRC

CNMP Cperf

CI

Fperf

Proposed model

Construct Definition

Perceived Customer Mediated Power

(Cmp)

Mediated power, which includes reward, coercive, and legal legitimate, involves influence strategies that the source (buyer) specifically administers to the target (seller). The intention is to bring about some direct action. Mediated bases represent the competitive and negative uses of power traditionally associated with organizational theory (modified from Brown et al., 1995 and Maloni and Benton, 2000).

Perceived Customer Non-mediated Power (Cnmp)

Compared with mediated powers, Non-mediated power which includes expert, referent, and traditional legitimate are more relational and positive in power orientation. (Brown et al., 1995 and Maloni and Benton, 2000)

Normative Relationship Commitment to Customer (Cnrc)

Normative relationship commitment refers to one member’s identification with another member and its internalization of common norms and values with another member. (modified from Brown, et al 1995)

Instrumental Relationship Commitment to Customer (Circ)

Instrumental relationship commitment is based on compliance (driven by rewards or punishment, etc.) and distinct from normative commitment. (modified from Brown, et al 1995)

Customer integration (Ci)

Customer integration is defined as the core competence derived from better coordination of all the critical customers in a company's supply chain to jointly achieve improved service capabilities at lower total supply chain cost (modified from Bowersox, Closs, and Stank, 1999).

Customer performance (Cperf)

Customer performance is defined as the company’s customer operational outcome, such the level of quality, flexibility, delivery, and customer service. It should be noted that customer performance is concerning the company’s performance with respect to its major customer. It is not the performance of the customer companies.

Financial performance (Fperf)

Financial performance is defined as financial and market measures to evaluate the firm’s efficiency and effectiveness.

HypothesesBrown, et al., (1995) found that in marketing channels for farm equipment, the supplier's use of different power may bring different retailer's commitment to the channel relationship and relationship commitment brings both supplier and retailer performance within the channel. Goodman and Dion (2001) argued power was becoming one of the important determinants of relationship commitment in the distributor-manufacturer relationship. So, we proposed :

H1: Companies with a greater level of perceived customer mediated power are more likely to have a stronger normative relationship commitment to customers.H2: Companies with a greater level of perceived customer mediated power are more likely to have a stronger instrumental relationship commitment to customers.H3: Companies with a greater level of perceived customer non-mediated power are more likely to have to a stronger normative relationship commitment to customers.H4: Companies with a greater level of perceived customer non-mediated power are more likely to have a stronger instrumental relationship commitment to customers.

With relationship commitment, supply chain partners become integrated into their key customers’ business processes and more tied to established goals (Morgan and Hunt, 1994; Chen and Paulaj, 2004). Johnson (1999) investigated the strategic role of inter-firm relationships through the concept of strategic integration. Daugherty (2002) empirically examined that the role of relationship commitment is frequently associated with more positive relationships between information systems support and reverse logistics program performance. Brown, et al., (1995) found that relationship commitment brings both supplier and retailer performance within the channel.

H5: Companies with a stronger normative relationship commitment to customers are more likely to have to a greater extent of customer integration.

H6: Companies with a stronger instrumental relationship commitment to customers are more likely to have to a greater extent of customer integration.

H7: Companies with a stronger normative relationship commitment to customers are more likely to have to a greater extent of customer performance.

H8: Companies with a stronger instrumental relationship commitment to customers are more likely to have to a greater extent of customer performance.

The relationship between SCI and performance outcomes is discussed over a vast body of supply chain and operations literature (Colin and Mapes, 1993; Daugherty et. al., 1996 and Tan et al., 1998). For example, Stank et al. (2001) developed and tested an instrument for measuring supply chain integration competences as well as determining their relative importance to developing logistics distinctiveness. Stank, Keller and Daugherty (2001) suggested that collaboration with supply chain partners facilitates internal collaboration, which in turn enhances logistics performance. Frohlich and Westbrook (2001) demonstrated that supply chain companies with the widest degree of the arcs of integration achieve the highest level of performance improvement involving the customer service, on-time delivery, delivery lead time, productivity, quality, and cost, in addition to the market share and profitability.

H9: Companies with a greater extent of customer integration are more likely to have a greater extent of customer performance.H10: Companies with a greater extent of customer integration more likely to have a greater extent of financial performance.H11: Companies with a greater extent of customer performance more likely to have a greater extent of financial performance.

Data collection_ Mail survey

• Target samples: Chongqing, Tianjin, Guangzhou, Shanghai, and HK. • Pilot test: using a sample of 15 companies. • Key informant: knowledgeable about SCM within the manufacturers • Sampling process: Companies in the yellow pages or Directory--- (A) Randomly selected in A to be Contacted by telephone--- (B) Companies contacted by telephone successfully--- (C) Manufacturer included in C--- (D) Questionnaire accepted by D---(E)• 617 usable questionnaires from contacted 4569 companies (13.5%) or 1356 questionnaires (45.5 %) sent out.

Results and Discussion

• Company ProfileCompany Profile

• More than 25.49% of the companies are from metal, mechanical and engineering,

• 17.86% of the companies produce textiles or/and apparel, • 13.15% of the respondents are electronics and electrical co

mpanies.• Over 32% of the respondents have the annual sales of less

than HK$5 million, and • 14.99% of the respondents have the annual sales of more

than HK$100 million.

Measurement and Reliability

Construct No. Of questions Cronbach’s Alpha

Financial performance (Fperf) 5 .905


5 .864


3 .667

Perceived Customer Non-mediated Power (Cnmp)

5 .811

Perceived Customer Mediated Power (Cmp)

6 .791

Customer integration (Ci) 8 .864

Customer performance (Cperf) 5 .851

Excellent factor analysis results!!!

Please refer to the draft paper

Convergent validity

Construct validity is the extent to which the items in a scale measure the abstract or theoretical construct (Carmines and Zeller, 1979 and Churchill, 1987).

In EFA, a construct is considered to have convergent validity if its eigen value exceeds 1.0 ( Hair et al., 1995). In addition, all the factor loadings must exceed the minimum value of 0.30.

In CFA, convergent validity can be assessed by testing whether or not each individual item’s coefficient is greater than twice its standard error ( Anderson and Gerbing, 1988). The proportion of variance (R2 ) in the observed variables, accounted for by the theoretical constructs influencing them, can be used to estimate the reliability of an indicator. R2 values above 0.30 were considered acceptable (e.g., Carr and Pearson, 1999).

This study: Factor loadings >0.465 with eigen values >1.28. R2 >=0.84.

Discriminant validity

Discriminant validity is established using CFA. Models were constructed for all possible pairs of latent constructs. These models were run on each selected pair, (1) allowing for correlation between the two constructs, and (2) fixing the correlation between the constructs at 1.0. A significant difference in chi-square values for the fixed and free solutions indicates the distinctiveness of the two constructs (Bagozzi and Phillips, 1982 and Bagozzi et al., 1991).

SEM_Estimates

SEM_Goodness of Fit

• Chi-Square (617)=2571.12,

• NFI = 0.99,

• NNFI = 0.99,

• CFI = 1.00,

• RMSEA = 0.072,

• Standardized RMR = 0.009

Discussion

• Customer integration and performance

• Customer integration significantly impacts customer performance and financial performance of the manufactures.

• Manufactures’ customer performance has a positive influence on financial performance of the manufactures.

• Customer integration can significantly influence manufacturers’ financial performance directly and indirectly.

• So, customer integration is very important for supply chain management.

• Relationship Commitment and supply chain customer integration

• Manufacturers’ normative relationship commitment to customers significantly impacts supply chain customer integration and customer performance.

• Manufacturers’ instrumental relationship commitment to customers significantly impacts customer performance, but it does not influence customer integration significantly.

• manufacturers’ normative relationship commitment to customers has a much high influence on customer performance (the standardized coefficient is 0.53+0.91*0.31=0.81)/financial performance than manufacturers’ instrumental relationship commitment to customers does (coefficient is 0.16).

• Power and relationship commitment

• Power has the positive influence on relationship commitment. • the influence of customers’ use of mediated power on manufacturers instrumental r

elationship commitment to customers (coefficient is 0.67) is much higher than the influence of customers’ use of mediated power on manufacturers’ normative relationship commitment to customers (coefficient is 0.25).

• The influence of customers’ use of non-mediated power on manufacturers’ instrumental relationship commitment to customers (coefficient is 0.31) is much lower than the influence of customers’ use of non-mediated power on manufacturers’ normative relationship commitment to customers (coefficient is 0.74).

• The customers’ use of mediated power has a much higher impact on manufacturers’ instrumental relationship commitment to customers (coefficient is 0.67) than customers’ use of non-mediated power does (coefficient is 0.31).

• The customers’ use of non-mediated power has a much higher impact on manufacturers’ normative relationship commitment to customers (coefficient is 0.74) than customers’ use of mediated power does (coefficient is 0.25).

Conclusions and LimitationsLimitations

This study firstly examined the relationships between power, relationship commitment, SC customer integration and manufacturer performance based on the empirical data from China.

This study identified the factors of the supply chain customer integration and the relationship between the factors and customer integration.

It investigated the relationship between two types of power and two types of relationship commitment and find that two types of power and two types of relationship commitment have the positive influence on SC customer integration.

Our model also revealed that manufacture performance is dependent on the extent of the SC customer integration.

Limitations

• Environmental factors that can have a differential influence on SC customer integration and relationship commitment are not included. Such as that trust is regards as one important factor impacts relationship commitment by many researches.

• There should be a classification of the companies from Mainland China and Hong Kong though they share the same Chinese culture and industry background.

• Future research directions: • The initiators of the SC customer integration demand further

research.• Other factors that impact power and relationship commitment

Q & A

Thank you!

Power, Relationship Commitment and Supply Chain Integration with Suppliers in China

Baofeng Huo

Xiande Zhao

Jeff Hoi Yan Yeung

Jan. 28, 2005

Agenda

Research Methodology

Discussion

Research methodology

SMP

SIRC

SNRC

SNMP Sperf

SI

Fperf

Proposed model

Measurement and ReliabilityConstruct No. Of questions Cronbach’s

AlphaFinancial performance (Fperf) 5 .905

Normative Relationship Commitment to Supplier (Snrc)

5 .900

Instrumental Relationship Commitment to Supplier (Sirc)

3 .694

Perceived Supplier Non-mediated Power (Snmp)

5 .822

Perceived Supplier Mediated Power (Smp)

6 .883

Supplier integration (Si) 8 .904

Supplier performance (Sperf) 5 .875

Convergent validity

Construct validity is the extent to which the items in a scale measure the abstract or theoretical construct (Carmines and Zeller, 1979 and Churchill, 1987).

In EFA, a construct is considered to have convergent validity if its eigen value exceeds 1.0 ( Hair et al., 1995). In addition, all the factor loadings must exceed the minimum value of 0.30.

In CFA, convergent validity can be assessed by testing whether or not each individual item’s coefficient is greater than twice its standard error ( Anderson and Gerbing, 1988). The proportion of variance (R2 ) in the observed variables, accounted for by the theoretical constructs influencing them, can be used to estimate the reliability of an indicator. R2 values above 0.30 were considered acceptable (e.g., Carr and Pearson, 1999).

This study: Factor loadings >0.472 with eigen values >1.21. R2 >=0.86.

Discriminant validity

Discriminant validity is established using CFA. Models were constructed for all possible pairs of latent constructs. These models were run on each selected pair, (1) allowing for correlation between the two constructs, and (2) fixing the correlation between the constructs at 1.0. A significant difference in chi-square values for the fixed and free solutions indicates the distinctiveness of the two constructs (Bagozzi and Phillips, 1982 and Bagozzi et al., 1991).

SEM_Estimates

SEM_Goodness of Fit

• Chi-Square (612)= 2631.78,

• NFI = 0.99,

• NNFI = 0.99,

• CFI = 0.99,

• RMSEA = 0.073,

• Standardized RMR = 0.016.

Discussion

• Supplier integration and performance

• Supplier integration significantly impacts financial performance of the manufactures.

• Manufactures’ supplier performance has a positive influence on financial performance of the manufactures.

• Supplier integration has no significant influence on supplier performance.

• Relationship Commitment and supply chain supplier integration

• Both manufacturers’ normative and instrumental relationship commitment to suppliers significantly impact supply chain supplier integration and supplier performance.

• Manufacturers’ normative relationship commitment to suppliers has the same influence on supplier integration as manufacturers’ instrumental relationship commitment to suppliers does (both coefficients are 0.48).

• Manufacturers’ instrumental relationship commitment to suppliers has a higher influence on supplier performance (coefficient is 0.57) than manufacturers’ normative relationship commitment to suppliers does (coefficient is 0.40).

• Power and relationship commitment

• The suppliers’ use of mediated power can positively and significantly influence both manufactures’ instrumental and normative relationship commitment to suppliers.

• But the suppliers’ use of non-mediated power has little impact on manufactures relationship commitment to suppliers.

• For relationship commitment, manufacturers instrumental and normative relationship commitment to the supplier has the equal impact.

• But manufacturers instrumental relationship commitment has a higher influence on supplier performance than manufacturers normative relationship commitment does.

• That means that, in China, the suppliers have a relative lower power on manufacturers than the power used by the manufacturers on the suppliers.

Q & A

Thank you!

Other Models Using Different

Power Constructs

5 Power_Customer

Cexpert

CIRC

CNRC

Ccoercive

Cperf

CI

Fperf

Proposed model

Creferent

Clegimitate

Creward




6 .897


3 .667

Perceived Customer Expert Power (Cexpert)

4 .813

Perceived Customer Referent Power (Creferent)

3 .875

Perceived Customer Legitimate Power (Clegimitate)

4 .825

Perceived Customer Reward Power (Creward)

4 .831

Perceived Customer Coercive Power (Ccoercive)

4 .915

Customer integration (Ci) 9 .884

Customer performance (Cperf) 6 .861

Chi-Square=4532.52, df=1053, P-value=0.00000, RMSEA=0.073

Normed Fit Index (NFI) = 0.99 Non-Normed Fit Index (NNFI) = 0.99 Comparative Fit Index (CFI) = 0.99 Incremental Fit Index (IFI) = 0.99 Relative Fit Index (RFI) = 0.99 Standardized RMR = 0.010

5 Power_Supplier

Sexpert

SIRC

SNRC

Scoercive

Sperf

SI

Fperf

Proposed model

Sreferent

Slegimitate

Sreward



Normative Relationship Commitment to Supplier (Snrc)

6 .923

Instrumental Relationship Commitment to Supplier (Sirc)

3 .694

Perceived Supplier Expert Power (Sexpert)

4 .803

Perceived Supplier Referent Power (Sreferent)

3 .904

Perceived Supplier Legitimate Power (Slegimitate)

4 .892

Perceived Supplier Reward Power (Sreward)

4 .894

Perceived Supplier Coercive Power (Scoercive)

4 .943

Supplier integration (Si) 13 .944

Supplier performance (Sperf)

6 .897

Chi-Square=5226.52, df=1242, P-value=0.00000, RMSEA=0.072

Normed Fit Index (NFI) = 0.99 Non-Normed Fit Index (NNFI) = 0.99 Parsimony Normed Fit Index (PNFI) = 0.93 Comparative Fit Index (CFI) = 0.99 Incremental Fit Index (IFI) = 0.99 Relative Fit Index (RFI) = 0.99 Standardized RMR = 0.016

Q & A

Thank you!

power, relationship commitment and supply chain integration

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