Title page for ETD etd-101999-192730

Type of Document Dissertation
Author Su, Chenting
Author's Email Address schentin@vt.edu
URN etd-101999-192730
Title Modeling Spousal Family Purchase Decision Behavior: A Dynamic Simultaneous Equations Approach
Degree PhD
Department Marketing
Advisory Committee
Advisor Name Title
Fern, Edward F. Committee Co-Chair
Ye, Keying Committee Co-Chair
Brinberg, David L. Committee Member
Littlefield, James E. Committee Member
Sirgy, M. Joseph Committee Member
  • Relative Influence
  • Behavioral Interaction
  • Spousal Decision Behavior
  • Dynamic Simultaneous Equations
Date of Defense 1999-08-10
Availability restricted

This dissertation represented an initial effort to model spousal family purchase decision behavior in terms of spousal coercion propensity. Two major issues concerning how spouses resolve conflicts were investigated: (1) What are the spousal behavioral interactions in household conflict resolution processes? (2) What are the temporal aspects of spousal family decision behaviors? It was hypothesized that spouses tend to not reciprocate their partners' uses of coercive influence strategies in a decision, given their avoidance of conflict. Also, spouses who used more power in the past tend to use less power in order to maintain equity in the long-term marital relationship. It was also hypothesized that spousal coercion propensity are contingent upon marital power, love, and preference intensity. Marital power and preference intensity are positively related to spousal coercion propensity while love predicts weaker coercive decision behavior. Consistently, it was proposed that coercive influence strategies are more effective in the short run, given the spouses' conflict avoidance and sense of equity in marriage. Thus, spouses who used coercive strategies are more satisfied with the decision outcome but less satisfied with the decision process.

A dynamic simultaneous equations model (DSE) was developed to test the major hypotheses of this dissertation. The model was calibrated by means of an Autoregressive Two-Stage Least Square (A2SLS) approach. MANOVAs and a set of binary logistic regressions and linear multiple regressions were used to test the other hypotheses. The empirical study involving a random sample provided adequate support for the model. The implications of the findings, theoretical and managerial alike, limitations of the study, and future research directions were discussed.

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