Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Business

Program Name/Specialization

Marketing

Faculty/School

Lazaridis School of Business and Economics

First Advisor

Dr. Sarah Wilner

Advisor Role

Supervisor

Abstract

The increasing supply of peer-reviewed marketing publications indicates that the scholarly community’s understanding of marketing phenomena has increased significantly. However, this significant increase also prevents individual scholars from gaining insight into marketing phenomena simply because the sheer number of publications make a manual review of the literature impossible. This is an important limitation mainly because the validity of knowledge development processes in marketing partially depend on the scholars’ comprehensive understanding of extant knowledge. In this dissertation, I develop a methodological process to address this limitation and enable scholars to gain insight into large samples of peer-reviewed publications. I then apply the proposed process to the publications in the sales digital transformation literature to demonstrate how this process can facilitate knowledge development.

Towards these objectives, in Paper One, I first examine the knowledge development process in the marketing discipline. I propose and develop an alternative methodology for the analysis of populations of peer-reviewed publications. The proposed methodology utilizes deep learning language models to extract nomological networks from manuscripts and organizes the results in an undirected multigraph that is subsequently studied using social network analysis techniques. Using the proposed methodology and by analyzing bibliometric data in the form of abstracts, I construct and analyze the nomological network of findings in a group of marketing journals. I then discuss how marketing knowledge has evolved in the past three decades, which constructs and variables have been most influential in marketing research, and which constructs and variables have a high influence on the structural properties of marketing knowledge.

In Paper Two, I apply my methodological process to a sample of 731 sales digital transformation articles and analyze the resulting nomological network using social network analysis techniques. By reviewing the structural properties of the network over time and examining sub-networks representing B2B and B2C findings, I provide insight into research themes over time, and the differences and potential synergies between B2B and B2C findings in the area. My results illustrate that the sets of constructs utilized in the area’s B2B and B2C studies overlap significantly. The overlapping constructs and variables are related to a wide range of themes consisting of organization characteristics, management, digital elements, product/service offerings, organizational processes, psychological factors, and sales teams. I further analyze the positioning of nodes within the nomological network of sales digital transformation literature and identify constructs and variables that facilitate conceptual development and study design by making efficient conceptual connections in B2B and B2C contexts.

Convocation Year

2023

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Marketing Commons

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