Service quality dimensions in the manufacturer–distributor dyadic relationship: manufacturer-to-distributor context

Trivedi, Sunil Dutt; Nigam, Abhinav; Pareek, Ashutosh

doi:10.1108/ijrdm-08-2022-0283

Cited by 2 publications

(3 citation statements)

References 103 publications

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“…The performance of marketing channel members depends on how efficiently they move products or services to the final customers (Shipley et al, 1991). The most common metric for measuring channel system performance is the sales volume (Ishii, 2020), with service quality being a less common metric (Trivedi et al, 2023). Greater profits can be achieved by improving the information about channel members' demand (Chu and Messinger, 1997).…”

Section: Introductionmentioning

confidence: 99%

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Satellite imagery and machine learning for channel member selection

Brei,

Rech,

Bozkaya

et al. 2023

IJRDM

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PurposeThis study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.Design/methodology/approachThe authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.FindingsThe relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.Research limitations/implicationsThe proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.Originality/valueThis research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.

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Section: Introductionmentioning

confidence: 99%

“…, 1991). The most common metric for measuring channel system performance is the sales volume (Ishii, 2020), with service quality being a less common metric (Trivedi et al. , 2023).…”

Section: Introductionmentioning

confidence: 99%

Satellite imagery and machine learning for channel member selection

Brei,

Rech,

Bozkaya

et al. 2023

IJRDM

View full text Add to dashboard Cite

show abstract

“…Apesar dos esforços, os modelos desenvolvidos revelaram-se insuficientes até que Gounaris (2005a) propôs o modelo INDSERV, desenvolvido especificamente para o setor da consultoria. Apesar de continuarem a surgir adaptações do modelo SERVQUAL ao contexto B-to-B (Trivedi et al, 2023) Este estudo pretende avaliar a qualidade de um serviço de consultoria, através do modelo INDSERV, como antecedente da satisfação do cliente, e perceber se esta se traduz numa intenção de relacionamento, nomeadamente através de lealdade, confiança e compromisso do cliente em relação ao consultor.…”

Section: Introductionunclassified

A influencia da qualidade do serviço na relação de consultoria

2023

IJMCNM

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This study aims to understand the influence of the quality of service in the consulting relationship and the interaction between the service provider and the client. Specifically, we aim to understand whether the quality of the service provided affects customer satisfaction and whether, consequently, a long-term relationship develops between the parties, reflecting on customer loyalty, trust, and commitment. Data collected from 107 client companies of consulting services were analyzed using the structural equation model and SmartPLS software. Results allow us to understand that the process's soft quality and the results' quality are the main determinants of customer satisfaction. The satisfaction derived from the quality of the consultancy service proves to be a great driver for the client of the consultancy relationship in terms of loyalty, trust, and commitment. This study uses the INDSERV model as a basis for quality analysis and includes several dimensions treated in other studies separately as determinants of satisfaction. It is suggested that managers promote the development of positive interactions during the service provided and facilitate the service alignment with the specific customer requirements. An empathetic attitude on the part of the consultants, advice adjusted to the client's needs, and the prompt resolution of any constraints allow an optimistic perspective about the consultancy firm, favoring the long-term relationship with the client.

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Service quality dimensions in the manufacturer–distributor dyadic relationship: manufacturer-to-distributor context

Cited by 2 publications

References 103 publications

Satellite imagery and machine learning for channel member selection

Satellite imagery and machine learning for channel member selection

A influencia da qualidade do serviço na relação de consultoria

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