When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?

Meeran, Sheik; Jahanbin, Semco; Goodwin, Paul; Neto, Joao Quariguasi Frota

doi:10.1016/j.ejor.2016.08.047

Cited by 23 publications

(21 citation statements)

References 42 publications

(42 reference statements)

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“…Goodwin et al (2018) provide different behaviours of how individuals can game the forecasting process, such as enforcing, filtering, hedging and second guessing. The intangibility and perishability of service creates more uncertainties in the service supply chain and forecast bias has attracted the attention of numerous scholars (Baecke et al, 2017;Meeran et al, 2017). In the related research of forecast bias in the service supply chain, overconfidence receives most concern, which can be subdivided into over-placement, overestimation and over-precision.…”

Section: Strategic Behaviourmentioning

confidence: 99%

Service supply chain management: a behavioural operations perspective

Liu

Wang

Long

et al. 2019

MSCRA

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Purpose The purpose of this paper is to provide an overview of the evolution of service supply chain management from a behavioural operations perspective, pointing out future research directions for scholars. Design/methodology/approach This study searched five databases for relevant literature published between 2009 and 2018, selecting 64 papers for this review. The selected literature was categorised according to two dimensions: a service supply chain link perspective and a behavioural factor perspective. Comparative analysis was used to identify gaps in the literature, and five future research agendas were proposed. Findings In terms of the perspective of service supply chain link, extant literature primarily focuses on service supply and service co-ordination management, and less on service demand and integration management. In terms of the behavioural factor’s perspective, most focus on classic behaviour factors, with less attention paid to emerging behaviour factors. This paper thus proposes five research agendas: demand-oriented management and integrated supply chain-oriented behavioural research; broadening the understanding of the scope of behavioural operations; integrating the latest backgrounds and trends of service industry into the research; greater attention to behavioural operations in service sub-industries; and multimethod combination is encouraged to be used to dig into the interesting research problems. Originality/value This study constitutes the first systematic review of service supply chain research from a behavioural perspective. By categorising the literature into two dimensions, the state of existing research is evaluated with an eye towards future research avenues.

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Section: Strategic Behaviourmentioning

confidence: 99%

Service supply chain management: a behavioural operations perspective

Liu

Wang

Long

et al. 2019

MSCRA

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“…9 shows that the DMU 2 should increase the output y 1 (Volume) from 10.46 units (see Table 2) to approximately 17.38 units (see Table 6). One of the ways to increase produced volume would be by expanding the customer base, and also by using advanced demand forecasting techniques (Meeran et al, 2017) in order to get more accurate values. It is worth mentioning that improving previous inputs will also lead to an improvement or increase in this output.…”

Section: Dmumentioning

confidence: 99%

Bi-Objective Multiple Criteria Data Envelopment Analysis combined with the Overall Equipment Effectiveness: An application in an automotive company

Silva

Marins

Tamura

et al. 2017

Journal of Cleaner Production

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The mass production companies need to seek high efficiency in the use of equipment and human resources, as well as in the consumption of their inputs. One of the key methods to address these challenges is the adoption of Overall Equipment Effectiveness, derived from Total Productive Maintenance. This work aims to propose a new efficiency indicator, called Overall Machinery Effectiveness, to be applied in an automotive company in Brazil that adopted Overall Equipment Effectiveness indicator. The studied company made available production data from ten months, associated to two Press machines, generating twenty Decision Making Units for Data Envelopment Analysis and Bi-Objective Multiple Criteria Data Envelopment Analysis models application. As results, Press #2 was identified as being the most critical because, among the first ten DMUs in the efficiency ranking, seven are associated to Press #1. The targets values recommended by the new indicator were considered feasible to be implemented by the company, thus validating in practice the new proposed procedure for the management of machines effectiveness. Moreover, the identification of the relevant variables (input and output) for the Press #1, and Press #2, allowed the decision maker to act in the best way to increase their efficiency.

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“…It should be noted that there are several factors responsible for influencing consumer decision making pattern and a subsequent analysis of these factors would be useful for the online service providers in deducing the consumer pattern. There are studies predicting customer behaviour and preferences across various sectors, creating a gap in understanding why so the behaviour and preference is (McDonald and Wilson, 2016;Meeran et al, 2017). The worthiness of study is twofold; first, when the service provider knows about their consumers well and their changing pattern, definitely that understanding helps them not only to provide the products/services according their changing needs but also help them to make their marketing strategy in a way so that they can maximize their profit and minimize the product/service failure risk.…”

Section: Introductionmentioning

confidence: 99%

Predicting changing pattern: building model for consumer decision making in digital market

Mangla

Luthra²,

Rana

et al. 2018

JEIM

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Purpose Consumers have the multiple options to choose their products and services, which have a significant impact on the pattern of consumer decision making in digital market and further increases the challenges for the service providers to predict their buying pattern. In this sense, the purpose of this paper is to propose a structural hierarchy model for analyzing the changing pattern of consumer decision making in digital market by taking an Indian context. Design/methodology/approach To accomplish the objectives, the research is conducted in two phases. An extensive literature review is performed in the first phase to list the factors related to the changing pattern of consumer decision making in digital market and then fuzzy Delphi method is applied to finalize the factors. In the second phase, fuzzy analytic hierarchy process (AHP) is employed to find the priority weights of finalized factors. The fuzzy set theory allows capturing the vagueness in the data. Findings The findings obtained in this study shows that consumers are much conscious about innovative and trendy products as well as brand and quality; therefore, the service providers must think about these two most important factors so that they can able to retain their consumer in their online portal. Practical implications The analysis shows that “innovative and trendy” is the first priority factor for the consumers followed by “brand and quality” and “fulfilment and time energy.” The proposed model can help the marketers and service providers in predicting customers’ preferences and their changing pattern efficiently under vague surroundings. The outcomes of this research work not only help the service provider to update their products and services according to consumers’ needs but can also help them to increase profit and minimize their risk. Originality/value This work contributes to consumer research literature focusing on problem evaluation in the context of changing pattern of consumer decision making in digital era.

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When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?

Cited by 23 publications

References 42 publications

Service supply chain management: a behavioural operations perspective

Service supply chain management: a behavioural operations perspective

Bi-Objective Multiple Criteria Data Envelopment Analysis combined with the Overall Equipment Effectiveness: An application in an automotive company

Predicting changing pattern: building model for consumer decision making in digital market

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