| 1. |
- Paschen, Jeannette, et al.
(författare)
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#BuyNothingDay : investigating consumer restraint using hybrid content analysis of Twitter data
- 2020
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Ingår i: European Journal of Marketing. - : Emerald Group Publishing Limited. - 0309-0566 .- 1758-7123. ; 54:2, s. 327-350
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Tidskriftsartikel (refereegranskat)abstract
- Purpose:This study aims to investigate motivations and human values of everyday consumers who participate in the annual day of consumption restraint known as Buy Nothing Day (BND). In addition, this study demonstrates a hybrid content analysis method in which artificial intelligence and human contributions are used in the data analysis. Design/methodology/approach:This research uses a hybrid method of content analysis of a large Twitter data set spanning three years. Findings:Consumer motivations are categorized as relating to consumerism, personal welfare, wastefulness, environment, inequality, anti-capitalism, financial responsibility, financial necessity, health, ethics and resistance to American culture. Of these, consumerism and personal welfare are the most common. Moreover, human values related to “openness to change” and “self-transcendence” were prominent in the BND tweets. Research limitations/implications:This research demonstrates the effectiveness of a hybrid content analysis methodology and uncovers the motivations and human values that average consumers (as opposed to consumer activists) have to restrain their consumption. This research also provides insight for firms wishing to better understand and respond to consumption restraint. Practical implications:This research provides insight for firms wishing to better understand and respond to consumption restraint.Originality/value:The question of why everyday consumers engage in consumption restraint has received little attention in the scholarly discourse; this research provides insight into “everyday” consumer motivations for engaging in restraint using a hybrid content analysis of a large data set spanning over three years.
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| 2. |
- Paschen, Jeannette, et al.
(författare)
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Collaborative intelligence : How human and artificial intelligence create value along the B2B sales funnel
- 2020
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Ingår i: Business Horizons. - : Elsevier. - 0007-6813 .- 1873-6068.
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Tidskriftsartikel (refereegranskat)abstract
- The B2B sales process is undergoing substantial transformations, fuelled by advances in information and communications technology and specifically by artificial intelligence (AI). The premise of AI is to turn vast amounts of data into information for superior knowledge creation and knowledge management in B2B sales. In doing so, AI can significantly alter the traditional human-centric sales process. In this article, we describe how AI impacts the B2B sales funnel. Specifically, for each stage of the funnel, we describe key sales tasks, explicate the specific contributions that AI can bring and clarify the role that human contributions play at each step of the AI-enabled sales funnel. We also outline managerial considerations to maximize the contributions from AI and people in the context of B2B sales.
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| 3. |
- Paschen, Jeannette, 1974-
(författare)
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Creating market knowledge from big data: Artificial intelligence and human resources
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The abundance of social media use and the rise of the Internet-of-Things (IoT) has given rise to big data which offer great potential for enhanced market knowledge for marketers. In the literature, market knowledge has been associated with positive marketing performance. The literature also considers market knowledge as an antecedent to insight which in turn is a strategic asset that can yield a sustained competitive advantage. In summary, market knowledge is important due to its relationship with performance and as a pre-requisite to insight.Market knowledge (as an outcome) results from market knowledge creation processes which encompasses the activities to create market knowledge. Market knowledge is created by integrating resources, specifically information technology and human resources.With respect to information technology, the unique characteristics of big data - volume, variety, veracity, velocity and value (the five V’s) - make traditional information technologies ill-suited to turn big data into information and ultimately market knowledge. Artificial intelligence (AI) has been discussed as one important information technology for creating market knowledge from big data. The literature suggests that AI is having a profound impact on the creation of market knowledge from big data and calls for more research on understanding the value potential of AI.Regarding human resources, the primacy of human contributions to the creation of market knowledge has been established in the literature. However, scholars and practitioners alike suggest that AI will change the nature and role of human contributions to creating market knowledge. The literature also suggests that the aspect of AI and human resources in market knowledge has not been adequately studied to date.Hence, the research problem in this thesis is formulated as “How do marketers create market knowledge from big data using artificial intelligence and human resources?” This research problem is addressed via five research questions (RQs):RQ 1: How does artificial intelligence contribute to creating market knowledge from big data?RQ 2: How does artificial intelligence impact the creation of market knowledge from big data and what are the implications for human resources?RQ 3: How do artificial intelligence and human resources interact in creating market knowledge from big data?RQ 4: What are the mutual contributions of artificial intelligence and human resources in creating market knowledge from big data?RQ 5: What are the contributions of artificial intelligence and human resources to different activities in creating market knowledge from big data?The research in this thesis encompasses two studies and three papers. The three papers are published or forthcoming in peer-reviewed journals. The research adopts an interpretivist paradigm and follows a qualitative research approach. The findings provide three key contributions to the body of knowledge and to theory. First, this thesis provides a non-technical understanding of what AI is, how it works and its implications for market knowledge, thus addressing a gap in the marketing literature.Second, this thesis posits that AI is a resource that meets the criteria of being 'valuable', 'rare', 'in-imitable', and 'organized' (VRIO) postulated by resource-based theory (RBT). The value of AI as a resource occurs in transforming big data into information and also AI transforming information into knowledge. Human resources are an important capability that improve the productivity of AI as a resource. This thesis provides empirical evidence that the nature of contributions offered by AI as a resource and human capabilities differ and explains how they differ.Third, this thesis contributes to resource-based theory. It proposes a conceptual model and puts forward five propositions regarding the relationship of AI as a resource, human capabilities and market knowledge. This model and the propositions can be tested in future scholarly work.This thesis opens with a chapter providing an introduction to the research area, followed by a literature review, a methodology chapter and a chapter discussing the findings and contributions to theory and practice, and outlining opportunities for future research. The papers and studies underpinning this thesis are presented in the last chapter of this thesis.
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| 4. |
- Paschen, Jeannette
(författare)
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Investigating the emotional appeal of fake news using artificial intelligence and human contributions
- 2020
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Ingår i: Journal of Product & Brand Management. - : Emerald Group Publishing Limited. - 1061-0421. ; 29:2, s. 223-233
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Tidskriftsartikel (refereegranskat)abstract
- Purpose:The creation and dissemination of fake news can have severe consequences for a company’s brand. Researchers, policymakers and practitioners are eagerly searching for solutions to get us out of the ‘fake news crisis’. Here, one approach is to use automated tools, such as artificial intelligence (AI) algorithms, in conjunction with human inputs to identify fake news. The study in this article demonstrates how AI and machine learning, with its ability to analyze vast amounts of unstructured data, can help us tell apart fake and real news content. Specifically, this study examines if and how the emotional appeal, i.e., sentiment valence and strength of specific emotions, in fake news content differs from that in real news content. This is important to understand, as messages with a strong emotional appeal can influence how content is consumed, processed and shared by consumers. Design/methodology/approach:The study analyzes a data set of 150 real and fake news articles using an AI application, to test for differences in the emotional appeal in the titles and the text body between fake news and real news content. Findings:The results suggest that titles are a strong differentiator on emotions between fake and real news and that fake news titles are substantially more negative than real news titles. In addition, the results reveal that the text body of fake news is substantially higher in displaying specific negative emotions, such as disgust and anger, and lower in displaying positive emotions, such as joy. Originality/value:This is the first empirical study that examines the emotional appeal of fake and real news content with respect to the prevalence and strength of specific emotion dimensions, thus adding to the literature on fake news identification and marketing communications. In addition, this paper provides marketing communications professionals with a practical approach to identify fake news using AI.
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