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Using Complementary Research Designs: The Example of Effects of Social Embeddedness on Trust and Cooperation. Werner Raub Workshop on Social Theory, Trust, Social Networks, and Social Capital II National Chengchi University – NCCU April 2011. Overview. Theory

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slide1

Using Complementary Research Designs: The Example of Effects of Social Embeddedness on Trust and Cooperation

Werner Raub

Workshop on Social Theory, Trust, Social Networks, and Social Capital IINational Chengchi University – NCCUApril 2011

overview
Overview
  • Theory
    • Trust problems in economic exchange
    • Embeddedness effects on trust

2. Empirical evidence from studies using different and complementary research designs

    • A survey on buyer-supplier relations
    • A vignette study on buyer-supplier relations
background a rational choice perspective on embeddedness effects
Background: a rational choice perspective on embeddedness effects

“… while the assumption of rational action must always be problematic, it is a good working hypothesis that should not easily be abandoned. What looks to the analyst like nonrational behavior may be quite sensible when situational constraints, especially those of embeddedness, are fully appreciated […] My claim here is that however naive that psychology [of rational choice] may be, this is not where the main difficulty lies – it is rather in the neglect of social structure.”

Mark Granovetter (1985) Economic Action and Social Structure: The Problem of Embeddedness

empirical evidence on embeddedness effects from complementary research designs
Empirical evidence on embeddedness effects from complementary research designs
  • Aim: use complementary research designs (survey, vignette study, lab experiment) for multiple tests of the same hypotheses (cf.: triangulation, cross validation)
  • Similar perspective:
    • Sociology: J.H. Goldthorpe (1996) The Quantitative Analysis of Large-scale Data Sets and Rational Action Theory: For a Sociological Alliance, ESR 12
    • Economics: G.W. Harrison & J.L. List (2004) Field Experiments, JEL 42(4)
slide8

Fritz Stern,

Gold and Iron: Bismarck, Bleichröder, and the Building of the German Empire – 1

“In the private banking world of those days, personal ties were of the greatest importance. Common ventures depended on mutual trust, and that trust had to be established by direct personal knowledge.” (p. 9)

slide9

Fritz Stern,

Gold and Iron: Bismarck, Bleichröder, and the Building of the German Empire– 2

“In Bismarck’s world, then, Bleichröder was a welcome supplement to official channels (…) Bismarck used him freely and continuously as a special emissary, as an additional and informal link to foreign powers and statesmen. Statesmen always like to have a multiplicity of contacts – to solicit reactions, to convey hints, to reinforce threats or allay fears.” (p. 311)

trust game as a stylized model of exchange

Trustor (buyer)

Trust

No trust

No trust

Trust

Trustee (supplier)

Honor

Abuse

Abuse

Honor

S1 < P1 < R1

P2 < R2 < T2

Trust Game as a stylized model of exchange
substantive interpretation of the trust game for buyer supplier relations
Substantive interpretation of the Trust Game for buyer-supplier relations

Moves of the trustor (buyer)

  • No trust: safeguard transaction with an extensive but costly contract
  • Trust: use less extensive and less costly contractual safeguards

Moves of the trustee (supplier)

  • Honor trust: deliver appropriate quality and deliver in due time
  • Abuse trust: deliver inferior quality and/or deliver with a delay
extensions of the trust game
Extensions of the Trust Game
  • More than only two moves for trustor and trustee (e.g., Investment Game)
  • Incomplete information of the trustor on incentives or opportunities of trustee for abusing trust
  • Ex post-problems due to incompetence of trustee or unfavorable external contingencies rather than abuse of trust (opportunism)
  • Incentives and opportunities for “defection” of trustor (e.g., delayed payment)
  • Etc.
relation with stern s historical study on bismarck and his banker bleichr der
Relation with Stern’s historical study on Bismarck and his banker Bleichröder
  • Stern claims that interactions between private bankers involve trust problems.
  • Stern likewise claims that interactions between states and between politicians involve trust problems.
research problem
Research problem
  • Distinguish between different embeddedness effects
    • theoretically
    • empirically
  • We neglect:
    • strategic network formation: embeddedness is assumed to be exogenous
    • “non-selfish utility”: focus on trust as a result of “enlightened self-interest”
slide16

Fritz Stern,

Gold and Iron: Bismarck, Bleichröder, and the Building of the German Empire – 1

Trust and Embeddedness

“In the private banking world of those days, personal ties were of the greatest importance. Common ventures depended on mutual trust, and that trust had to be established by direct personal knowledge.” (p. 9)

Note Stern’s claim: dyadic embeddedness affects trust.

slide17

Fritz Stern,

Gold and Iron: Bismarck, Bleichröder, and the Building of the German Empire– 2

Trust and Embeddedness

“In Bismarck’s world, then, Bleichröder was a welcome supplement to official channels (…) Bismarck used him freely and continuously as a special emissary, as an additional and informal link to foreign powers and statesmen. Statesmen always like to have a multiplicity of contacts – to solicit reactions, to convey hints, to reinforce threats or allay fears.” (p. 311)

Note Stern’s claim: Network embeddedness allows for learning and control (informal reciprocity).

examples of hypotheses
Examples of hypotheses

Effects of dyadic embeddedness:

  • Trust increases with positive experiences of trustor with trustee (learning effect)
  • Trust and trustworthiness increase with expected future transactions (control effect)

Effects of network embeddedness:

  • Trust increases with positive information of trustor on trustee from third parties (learning effect)
  • Trust and trustworthiness increase with sanction opportunities of trustor (e.g., “voice”) involving third parties (control effect)
slide22
Evidence on effects of

dyadic embeddedness:

A survey on IT-transactions

More information:

  • Batenburg, Raub & Snijders (2003) Contacts and Contracts: Dyadic Embeddedness and the Contractual Behavior of Firms, Research in the Sociology of Organizations 20: 135-188
a survey on it transactions
A survey on IT-transactions

Data on the purchase of hard- and software, standard and complex products

  • ca 1000 transactions (“Trust Games”)
  • ca 800 buyers (trustors): Dutch SMEs
  • 600+ suppliers (trustees)
  • various replications and extensions of the study in the Netherlands and Germany
slide24

Survey: variables and measurements I

  • Trust of buyer: “Lack of trust” measured by buyer’s costly INVESTMENTS IN CONTRACTING with supplier (person-days and departments involved, financial and legal clauses and technical specifications included in contract)
  • Dyadic learning:(positive) experiences of buyer from previous transactions with supplier – PAST
  • Dyadic control: expectations of buyer on future transactions with supplier – FUTURE
slide25

Survey: variables and measurements II

  • Control variables:
    • Transaction characteristics (e.g., specific investments, uncertainty, volume)
    • Marginal costs of contracting
    • Characteristics of buyer and supplier, including respondent characteristics
slide26

Investments in contracting: assumptions I

  • IT transactions are associated with risks
  • Focus on risks of the buyer (such as delayed delivery, inferior quality etc.) due to:
    • opportunistic behavior of supplier
    • incompetence of supplier
    • external contingencies
  • Contracting as a device to mitigate risks:
    • reduction of incentives for opportunistic behavior of supplier
    • compensation for buyer if risks “materialize”
slide27

Investments in contracting:

assumptions II

Core assumptions for deriving hypotheses:

  • Investments in contracting are costly and actors will economize on these costs
  • Investments in contracting will increase in risks
slide28

Hypotheses on effects of dyadic embeddedness I

Effects of a positive past relationship:

  • Dyadic learning: reduced probability of supplier incompetence
  • Also: mutual relationship specific investments
    • Availability of costless safeguards from prior transactions (e.g., reuse of contract for a prior transaction)
    • Good working relations between employees of the two firms

Decreasing investments in contracting

slide29

Hypotheses on effects of dyadic embeddedness II

Two effects of expected future transactions:

  • Reciprocity effect: conditional cooperation becomes an alternative for costly investments in contracting
    • Decreasing investments in contracting
  • Reusability effect: investments in contracting can be (partly) reused for future transactions (e.g., reuse of adapted version of the contract)

Increasing investments in contracting

Note: reusability effect larger when contractual safeguards are not yet available, i.e., larger for transactions without prior business between buyer and supplier

slide30

Hypotheses on effects of dyadic embeddedness III

Without additional assumptions on strength of reciprocity effect and reusability effect: no hypothesis on main effect of FUTURE on INVESTMENTS IN CONTRACTING

  • Due to larger reusability effect for transactions without prior business between buyer and supplier: negative interaction effect PAST x FUTURE on INVESTMENTS IN CONTRACTING (dyadic control)
survey empirical evidence on effects of dyadic embeddedness
Survey: empirical evidence on effects of dyadic embeddedness
  • Robust result for various statistical models, for alternative operationalizations of variables, and controlling for transaction characteristics, marginal costs of contracting, and characteristics of buyer and supplier:

INVESTMENTS IN CONTRACTING decrease with:

    • PAST: positive past experiences of buyer
    • PAST x FUTURE: expected future transactions if positive past experiences exist

Support for hypotheses on dyadic learning and control effects

slide32
Evidence on effects of

dyadic embeddedness:

A vignette study on

buyer-supplier relations

More information:

  • Buskens & Raub (2002) Embedded Trust, Advances in Group Processes 19: 167-202
slide33

Data: a vignette study on buyer- supplier relations

  • Respondents: 40 purchase managers of Dutch medium-sized and large companies
  • Judgments on 348 virtual transactions (= vignettes; 8-10 vignettes per respondent)
  • Data on
    • transaction management
    • transaction characteristics
    • dyadic embeddedness
    • network embeddedness
    • characteristics of purchase managers
slide36

Buyer-supplier vignettes: variables and measurements I

  • Trust of buyer: “Lack of trust” measured for each vignette by buyer’s costly INVESTMENTS IN CONTRACTING with supplier (duration of negotiation with supplier and departments of buyer involved in negotiations)
  • Dyadic learning: (positive) experiences of buyer from previous transactions with supplier – PAST
  • Dyadic control: expectations of buyer on future transactions with supplier – FUTURE
slide37

Buyer-supplier vignettes: variables and measurements II

  • Control variables:
    • Transaction characteristics (e.g., specific investments, uncertainty, volume)
    • Characteristics of buyer and supplier, including respondent characteristics (e.g., experience with transactions like described on vignettes)
slide38

Buyer-supplier vignettes and survey on IT-transactions

  • Note:
    • Vignette study on buyer-supplier relations and survey on IT-transaction allow to test same hypotheses on effects of dyadic embeddedness with two different but complementary data sets based on different designs
buyer supplier vignettes empirical evidence on effects of dyadic embeddedness
Buyer-supplier vignettes: empirical evidence on effects of dyadic embeddedness
  • Robust result for various statistical models and controlling for transaction characteristics as well as respondent characteristics:

INVESTMENTS IN CONTRACTING decrease with:

    • PAST: positive past experiences of buyer
    • PAST x FUTURE: expected future transactions if positive past experiences exist

Renewed support for hypotheses on dyadic learning and control effects with data based on different research design

slide40
Evidence on effects of

network embeddedness:

survey on IT-transactions

More information:

  • Rooks, Raub & Tazelaar (2006) Ex Post Problems in Buyer-Supplier Transactions, Journal of Management and Governance 10: 239-276
slide41

Survey: variables and measurements III

  • Network embeddedness:
    • DEGREE: ties of buyer with other buyers of supplier
    • SECTOR DENSITY: contacts among firms in business sector of buyer
    • VISIBILITY of supplier in the market (as assessed by buyer)
  • Note: these are indicators for network learning as well as network control opportunities (“voice opportunities”) of buyer
slide42

Survey: variables and measurements IV

  • Trustworthiness of supplier:
    • PERFORMANCE of supplier, e.g., delivery in due time, quality of product, quality of after-sales service (Rooks et al. (2006): EX POST PROBLEMS instead of PERFORMANCE)
survey empirical evidence on effects of network embeddedness on trustworthiness of supplier
Survey: empirical evidence on effects of network embeddedness on trustworthiness of supplier
  • Robust result for various statistical models and controlling for transaction characteristics, buyer’s INVESTMENTS IN CONTRACTING, and characteristics of buyer and supplier:

PERFORMANCE of supplier increases (i.e., EX POST PROBLEMS decrease) with network embeddedness (DEGREE, SECTOR DENSITY, VISIBILITY)

Support for hypotheses on network control effects on supplier (trustee) behavior

survey empirical evidence on effects of network embeddedness on trust of buyer
Survey: empirical evidence on effects of network embeddedness on trust of buyer
  • Robust result for various statistical models, for alternative operationalizations of variables, and controlling for transaction characteristics, marginal costs of contracting, and characteristics of buyer and supplier:

network embeddedness (DEGREE, SECTOR DENSITY, VISIBILITY) has no effect on buyer’s INVESTMENTS IN CONTRACTING

No support for hypotheses on network learning or control effects on buyer (trustor) behavior

puzzle
Puzzle
  • Suppliers seemingly react to incentives from network embeddedness (suppliers seemingly take reputation effects of their performance into account).
  • How to explain that buyers seemingly do not anticipate on this feature?
    • Data and/or measurement problems (including sample selectivity and endogeneity of network embeddedness)?
    • Lack of “strategic rationality”?
another summary of the puzzle
Another summary of the puzzle
  • Trustee reacts to trustor’s opportunities for
    • dyadic control and
    • network control
  • Focal trustor reacts to her own opportunities for dyadic control: she seemingly anticipates that trustee anticipates on effects of his present behavior on future behavior of focal trustor
  • Focal trustor does not react to her own opportunities for network control: she seemingly does not anticipate that trustee also anticipates on effects of his present behavior on future behavior of other trustors
slide47
Another vignette experiment:

buying a used car

More information:

  • Buskens, V. & J. Weesie (2000) An Experiment on the Effects of Embeddedness in Trust Situations: Buying a Used Car, Rationality and Society 12: 227-253
buying a used car
Buying a used car
  • Buyer chooses between trust or no trust (buy or not buy).
  • Trust gives the dealer the opportunity to honor or abuse trust (sell a decent or inferior car). In both situations he is better off than when trust would not be placed
  • Buyer gains from honored trust, but regrets trust if trust is abused.
  • Dealer earns an extra profit from abusing trust (too much money for a bad car).
variables to be manipulated and related hypotheses

Dyadic Embeddedness

Network Embeddedness

Learning

Prior buyer’s experiences

Prior third-party experiences

Control

Buyer’s sanction opportunities

Sanction opportunities w.r.t. third parties

Variables to be manipulated and related hypotheses
  • Trust increases with
    • positive own experiences (dyad)‏
    • expected future transactions (dyad)‏
    • positive information from third parties (network)‏
    • sanction opportunities w.r.t. third parties (network)‏
the set up of the vignettes
The set-up of the vignettes
  • Pairs of vignettes including six variables.
    • Price
    • Past (own experience)‏
    • Future (own expected future interactions)‏
    • Density (general reputation)‏
    • Indegree (friends experiences)‏
    • Outdegree (own third-party sanction opportunities)‏
  • Subjects compare pairs of situations to buy a used car.
  • Some additional questions.
a pair of vignettes
You can buy a car for $4000.

You bought a car from The Autoshop before and you were satisfied.

You do not expect to move out of town soon.

The Autoshop is a well-known garage and has many customers in your neighborhood.

You have friends who bought a car from The Autoshop before and they were satisfied.

The owner of the garage and you are members of the same football team.

A pair of vignettes
  • You can buy a car for $4000.
  • You never bought a car from The Autoshop before.
  • You will move to the other side of the country in a few weeks.
  • The Autoshop is an unknown garage in your neighborhood.
  • As far as you know, none of your friends have bought a car from The Autoshop before.
  • You do not have a close social link with the owner of The Autoshop.
experimental design
Experimental design
  • Choices of pairs
    • Price is constant within a pair (only for interactions)
    • No “ordered” pairs (not too easy)‏
    • Between 2 and 4 variables that differ (not too complex)‏
  • Ten pairs per subject
    • Considerable variation in comparisons within subjects
  • Ultimately 125 students rating 1249 vignettes
statistical model
Statistical model
  • Random utility model.
  • Probit model on choices for vignettes.
  • Independent variables are the differences between the values at the vignettes (0: type of embeddedness to same on two vignettes, 1 or -1: type of embeddedness present on one vignette but not on the other)‏
  • Coefficients are interpretable as indicators for the increase in utility assigned to a vignette related to the given type of embeddedness
  • Standard errors modified for clustering (alternatively multilevel analysis could have been done)‏
additional analyses
Additional analyses
  • No differences between sessions
  • No differences related to timing of a pair of vignettes or time spent on a decision
  • No effects of age, gender, field of study
  • Outdegree has larger effect for subjects who are more concerned about “reputation” issues
  • Subjects with knowledge of game theory tend to value control variables higher
conclusion
Conclusion
  • Support for main effects of embeddedness
    • control and learning
    • dyadic and in networks
  • No support for interaction effects
discussion
Discussion
  • Motivation of behavior (no direct incentives and students might not be experts)‏
  • Interpretation of variables
    • Future: moving has more side-effects than loss of sanction possibilities alone
  • Possible extension
    • Similar experiment with experts
    • Moving to an abstract laboratory experiment
overview of results

Buyer-supplier vignettes

Used-car vignettes

Incentives

+

Not tested

Dyadic learning

+

+

Dyadic control

+

+

Network learning

learning and control hard to disentangle

+

Network control

+

Overview of Results
slide62
Additional slides on

survey on IT-transactions

slide63

Survey on IT-transactions: data collection I

  • Nationally representative survey on the purchase of IT-products (hard- and software) by Dutch SMEs
    • Data collection via buyers
      • CATI-interview (selection of transaction)
      • Structured questionnaire, usually administered via site visit
    • Additional archival information, sometimes including a rough content analysis of contracts
  • Data quality
    • total response rate: 58%
    • non-response not selective
    • low partial non-response
slide64

Survey on IT-transactions: descriptives

  • ‘Average transaction’:
    • a product worth 50,000 US-$
    • purchased by a firm with ca 80 employees
    • negotiating and contracting required ca 5 mendays of buyer’s employees…
    • and involved ca 2 divisions of buyer’s firm
    • for about two thirds of buyers, transaction is of ‘great’ or ‘very great’ importance for their IT-situation
slide66

Survey on IT-transactions: variables I

  • Transaction characteristics (indicators for trust problems, opportunism problems, and other risks associated with the transaction), e.g., monitoring problems, switching costs, importance of durability of product, importance of product for profitability buyer, volume of transaction
  • Costs of contracting
  • E.g., in-house legal expertise
  • Dyadic embeddedness
    • Prior transactions with the supplier (duration, frequency, volume, satisfaction)
    • Expected future business (frequency, volume)
slide67

Survey on IT-transactions: variables II

  • Network embeddedness
    • Indicators for density of buyer’s network of business contacts
    • Relations of buyer with other clients of supplier
    • Availability of and relations of buyer with other suppliers
  • Various control variables
  • E.g., ‘demography’ of buyer and supplier, other suppliers involved in transaction…
slide68

Survey on IT-transactions: variables III

  • Ex ante management: search and selection
    • Investments in search
    • E.g., mendays, departments involved
    • Extensive and intensive search
    • E.g., number and specificity of offers, number and kind of information channels
    • used
  • Ex ante management: contractual planning
    • Investments in negotiating and contracting
    • E.g., mendays, departments involved
    • Legal, financial, and technical issues addressed during negotiations and in the contract
slide69

Survey on IT-transactions: variables IV

  • Ex post management – contract execution
    • Supplier performance, ex post problems (type and seriousness)
    • conflict resolution (type and seriousness of sanctions, ‘legal’ vs. ‘extra-legal’)
  • New business between buyer and supplier after focal transaction
slide70

Independent Variables Hypothesis Coefficient |t-value|

slide71

Standardized Coefficients from the Ordinary Least Squares Regression on management

Independent Variables

Hypothesis

Coefficient

|t-value|

Dyadic Embeddedness

–0.09**

3.12

Past (1 = yes)

?

0.05

1.28

Future

–0.12**

3.33

Past×Future

slide73

TABLE 2

Three-Stage Least Squares Regression Analysis of EX POST PROBLEMS

(1205 transactions of 775 buyers)

Variable

Hypothesis

Model 1

Model 2

Embeddedness characteristics

PAST

?

-0.042

(0.033)

-0.045~

(0.033)

SATISFACTION*

-0.206***

(0.038)

EXPECTED FUTURE

0.054~

(0.031)

0.066*

(0.032)

DEGREE

-0.063*

(0.035)

-0.059*

(0.035)

SECTOR DENSITY

-0.068**

(0.029)

-0.055*

(0.029)

VISIBILITY

-0.052*

(0.028)

-0.054*

(0.028)

EXIT NETWORK

-0.088**

(0.034)

-0.085**

(0.034)

survey on it transactions why no effects of network embeddedness on buyer behavior
Survey on IT-transactions: why no effects of network embeddedness on buyer behavior?
  • Endogeneity of network embeddedness: before buying, buyers search for information on product and supplier, with network embeddedness changing as a by-product of search
  • Unmeasured variance in the trust problem: network embeddedness seemingly measures a part of the trust problem
  • Sample selectivity: buyers avoid transactions with “large” trust problems when network embeddedness is low
  • Supplier effects on investments in contracting: suppliers may contract more “carefully” with well-embedded buyers
slide75
Additional slides on

vignette study on

buyer-supplier relations

slide76

Buyer-supplier vignettes: regression analysis of INVESTMENTS IN CONTRACTING

**,*, and ~ represent two-sided significance at respectively p < 0.01, p < 0.05, and p < 0.10 based on Huber standard errors modified for clustering.