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P. Green, J. Carroll, S. Goldberg (1981)
A General Approach to Product Design Optimization via Conjoint AnalysisJournal of Marketing, 45
B.K. Orme
The Adaptive Choice‐based Conjoint (ACBC) Technical Paper
Amit Sachan, S. Datta (2005)
Review of supply chain management and logistics researchInternational Journal of Physical Distribution & Logistics Management, 35
Timothy Gilbride, Greg Allenby (2004)
A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening RulesMarketing Science, 23
J. Holmström, M. Ketokivi, A. Hameri (2009)
Bridging Practice and Theory: A Design Science ApproachDecis. Sci., 40
J. Sterling, D. Lambert (1987)
ESTABLISHING CUSTOMER SERVICE STRATEGIES WITHIN THE MARKETING MIXJournal of Business Logistics, 8
P. Green, V. Srinivasan (1978)
Conjoint Analysis in Consumer Research: Issues and OutlookJournal of Consumer Research, 5
E. Anderson, T. Coltman, T. Devinney, B. Keating (2008)
What Drives the Choice of Third Party Logistics Provider?Organizations & Markets eJournal
Thomas Reutterer, H. Kotzab (2000)
The Use of Conjoint-analysis for Measuring Preferences in Supply Chain DesignIndustrial Marketing Management, 29
A.S. Fowkes, G. Tweddle
Validation of stated preference forecasting: a case study involving Anglo‐continental freight, transportation planning methods
Jari Collin, E. Eloranta, J. Holmström (2009)
How to design the right supply chains for your customersSupply Chain Management, 14
Bart Bronnenberg, Wilfried Vanhonacker (1996)
Limited Choice Sets, Local Price Response, and Implied Measures of Price CompetitionJournal of Marketing Research, 33
W.E. Hoover, E. Eloranta, K. Huttunen
Managing the Demand‐supply Chain
Michael Garver (2003)
Best practices in identifying customer-driven improvement opportunitiesIndustrial Marketing Management, 32
D.M. Lambert, T.C. Harrington
Establishing customer service strategies within the marketing mix: more empirical evidence
D. Bowersox, D. Closs, T. Stank (2000)
TEN-MEGA TRENDS THAT WILL REVOLUTIONIZE SUPPLY CHAIN LOGISTICSJournal of Business Logistics, 21
Arun Sharma, D. Lambert (1990)
Segmentation of Markets Based on Customer ServiceInternational Journal of Physical Distribution & Logistics Management, 20
R.M. Johnson
A New Approach to Adaptive CBC
K. Granzin, Kenneth Bahn (1989)
Consumer logistics: Conceptualization, pertinent issues and a proposed program for researchJournal of the Academy of Marketing Science, 17
M. Christopher (2005)
Logistics and supply chain management : creating value-adding networks
D. Lambert (1992)
Developing a Customer‐focused Logistics StrategyInternational Journal of Physical Distribution & Logistics Management, 22
J. Wind, P. Green, Douglas Shifflet, M. Scarbrough (1989)
Courtyard by Marriott: Designing a Hotel Facility with Consumer-Based Marketing ModelsInterfaces, 19
A. Hartmann, Jasper Caerteling (2010)
Subcontractor procurement in construction: the interplay of price and trustSupply Chain Management, 15
R.M. Johnson, B. Orme
Perspective on Adaptive CBC (What can We Expect from Respondents?)
Michael Garver, Zachary Williams, S. LeMay (2010)
Measuring the importance of attributes in logistics researchThe International Journal of Logistics Management, 21
R. Danielis, E. Marcucci, Lucia Rotaris (2005)
Logistics managers’ stated preferences for freight service attributesTransportation Research Part E-logistics and Transportation Review, 41
J. Hauser, Min Ding, Steve Gaskin (2009)
NON-COMPENSATORY (AND COMPENSATORY) MODELS OF CONSIDERATION-SET DECISIONS
K.G. Corely, D.A. Gioia
Building theory about theory building: what constitutes a theoretical contribution?
M.S. Garver
Customer‐driven improvement model: best practices in identifying improvement opportunities
P. Naudé, F. Buttle (2000)
Assessing Relationship QualityIndustrial Marketing Management, 29
M. Guerrero, J. Egea, María González (2007)
Application of the latent class regression methodology to the analysis of Internet use for banking transactions in the European UnionJournal of Business Research, 60
J. Hauser, B. Wernerfelt (1990)
An Evaluation Cost Model of Consideration SetsJournal of Consumer Research, 16
Kamel Jedidi, R. Kohli, W. DeSarbo (1996)
Consideration Sets in Conjoint AnalysisJournal of Marketing Research, 33
Peter Rossi, Greg Allenby (2005)
Bayesian Statistics and Marketing
J. Mentzer, D. Flint, T. Hult (2001)
Logistics Service Quality as a Segment-Customized ProcessJournal of Marketing, 65
M.S. Garver, Z. Williams, S. LeMay
Measuring the importance of attributes and creating meaningful segments in logistics research: a new research method and application
S. Bolis, R. Maggi
Logistics strategy and transport service choices: an adaptive stated preference experiment
M. Voss, David Closs, R. Calantone, O. Helferich, C. Speier (2009)
THE ROLE OF SECURITY IN THE FOOD SUPPLIER SELECTION DECISIONJournal of Business Logistics, 30
P. Green, V. Srinivasan (1990)
Conjoint Analysis in Marketing: New Developments with Implications for Research and PracticeJournal of Marketing, 54
R.M. Johnson
Tradeoff analysis of consumer values
Kevin Corley, Dennis Gioia (2011)
Building theory about theory building: What constitutes a theoretical contribution? Academy of Management Review, , ., 36
Michael Garver, Zachary Williams, G. Taylor (2008)
Employing Latent Class Regression Analysis to Examine Logistics Theory: An Application of Truck Driver RetentionJournal of Business Logistics, 29
G. Maier, E. Bergman, Patrick Lehner (2002)
Modelling preferences and stability among transport alternativesTransportation Research Part E-logistics and Transportation Review, 38
Purpose – Much of the research conducted in logistics/SCM has focused on satisfaction/retention of customers. This has left a critical gap for managers: before customers can be satisfied and ultimately retained, a purchase choice of logistics services has to occur. To date, very little research has addressed how logistics customers make purchase choice decisions about logistics services. The purpose of this paper, using logistics research methods, is to introduce adaptive choice modelling (ACM) to address this gap and put forth a research method that is useful for academic researchers and logistics/SCM managers. Design/methodology/approach – This paper provides an overview of ACM, along with a discussion of its important research advantages, limitations, and practical applications. Additionally, an empirical demonstration of this research technique is provided to illustrate how academic researchers and logistics managers can use ACM to better understand the decision‐making process of customers when selecting logistics services. Findings – In order to demonstrate this research technique, a research project was designed and implemented that analyzed the choice process of consumers selecting parcel carriers to ship a textbook. The results show that price, speed of delivery, and tracking are the three most important variables in the selection decision. The results also show that consumers are not homogeneous, but can be divided into five distinct need‐based segments. Recognizing and understanding the nature of these segments should help managers better meet the needs of parcel shippers. Research limitations/implications – The main research limitation with this study is that it is based on a convenience sample; thus future research will need to replicate this study to confirm the research findings. However, the ultimate purpose of the study is to present a new research method and discuss how to apply this method, so that logistics/SCM practitioners and academic researchers can better understand customers of logistics/SCM services. Thus, while the nature of the sample is a limitation, it should be viewed in this context. Originality/value – While conjoint analysis has existed for decades, this technique has rarely been implemented by logistics/SCM researchers and practitioners. Instead, logistics/SCM researchers and practitioners have focused more on retention methods and have virtually ignored modelling the actual purchase choice of logistics/SCM services. New advancements in conjoint analysis, specifically the ACM approach, have many important and unique advantages and applications for logistics/SCM researchers and practitioners. ACM has not been used in a logistics/SCM context.
International Journal of Physical Distribution & Logistics Management – Emerald Publishing
Published: Mar 9, 2012
Keywords: Supply chain management; Distribution management; Consumer behaviour; Parcel delivery services; Conjoint analysis; Adaptive choice based conjoint analysis; Discrete choice; Consumer logistics; Need‐based segmentation; Latent class
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