Abstract
Many companies pay a great deal of attention to existing customers' referral intentions when they attempt to attract new customers. However, little is known about the necessary level of satisfaction of existing customers to understand when existing customers are likely to complete a referral and by what mechanism. This study assumed referral routes and established a model for predicting referral intentions based on the satisfaction level as described by the disconfirmation of expectation theory and the net promoter score theory. Then, the routes were verified by surveying 103 construction management (CM) clients using structural equation modeling, and the prediction model was tested by applying it to 194 CM clients using multinomial logistic regression. The results indicated that the accuracy rate of the prediction model was 79.3%. This model can be used effectively to attract new clients, particularly in fields where long-term services are provided, such as CM, because it allows service providers to predict customers' referral intentions depending on their satisfaction levels.
| Original language | English |
|---|---|
| Article number | 05014023 |
| Journal | Journal of Management in Engineering |
| Volume | 31 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2015 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Industrial relations
- Engineering(all)
- Strategy and Management
- Management Science and Operations Research
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Quantitative model for predicting the referential intention of construction management services. / Jeong, Min; Lee, Ghang.
In: Journal of Management in Engineering, Vol. 31, No. 5, 05014023, 01.01.2015.Research output: Contribution to journal › Article
TY - JOUR
T1 - Quantitative model for predicting the referential intention of construction management services
AU - Jeong, Min
AU - Lee, Ghang
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Many companies pay a great deal of attention to existing customers' referral intentions when they attempt to attract new customers. However, little is known about the necessary level of satisfaction of existing customers to understand when existing customers are likely to complete a referral and by what mechanism. This study assumed referral routes and established a model for predicting referral intentions based on the satisfaction level as described by the disconfirmation of expectation theory and the net promoter score theory. Then, the routes were verified by surveying 103 construction management (CM) clients using structural equation modeling, and the prediction model was tested by applying it to 194 CM clients using multinomial logistic regression. The results indicated that the accuracy rate of the prediction model was 79.3%. This model can be used effectively to attract new clients, particularly in fields where long-term services are provided, such as CM, because it allows service providers to predict customers' referral intentions depending on their satisfaction levels.
AB - Many companies pay a great deal of attention to existing customers' referral intentions when they attempt to attract new customers. However, little is known about the necessary level of satisfaction of existing customers to understand when existing customers are likely to complete a referral and by what mechanism. This study assumed referral routes and established a model for predicting referral intentions based on the satisfaction level as described by the disconfirmation of expectation theory and the net promoter score theory. Then, the routes were verified by surveying 103 construction management (CM) clients using structural equation modeling, and the prediction model was tested by applying it to 194 CM clients using multinomial logistic regression. The results indicated that the accuracy rate of the prediction model was 79.3%. This model can be used effectively to attract new clients, particularly in fields where long-term services are provided, such as CM, because it allows service providers to predict customers' referral intentions depending on their satisfaction levels.
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U2 - 10.1061/(ASCE)ME.1943-5479.0000333
DO - 10.1061/(ASCE)ME.1943-5479.0000333
M3 - Article
VL - 31
JO - Journal of Management in Engineering - ASCE
JF - Journal of Management in Engineering - ASCE
SN - 0742-597X
IS - 5
M1 - 05014023
ER -