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Creating market knowledge from big data: Artificial intelligence and human resources
KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).ORCID iD: 0000-0002-4557-7407
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Abstract [sv]

Utbredd användning av sociala medier och större tillgång till Internet-of-Things (IoT) har skapat så kallad Big Data, vilket erbjuder stor potential för ökad marknadskunskap för marknadsförare. I litteraturen har marknadskunskap associerats med positiva marknadsföringsresultat. Dessutom föreslår litteraturen att marknadskunskap kan leda till insikt. Insikt är en strategisk tillgång som kan ge varaktiga konkurrensfördelar. Sammanfattningsvis är marknadskunskap viktig på grund av dess relation till resultat och som ett underlag för insikt.

Marknadskunskap (som ett resultat) kommer från skapandeprocesser som inkluderar de aktiviteter som krävs för att uppnå marknadskunskap. Marknadskunskap skapas genom att integrera resurser, särskilt informationsteknologi och mänskliga resurser.

Med avseende på informationstekniska resurser gör de unika egenskaperna hos Big Data – volume (volym), variety (variation), veracity (veracitet), velocity (hastighet) och value (värde) (vilket på engelska kallas de fem V: erna) - traditionella informationsteknologier olämpliga för att omvandla Big Data till information och slutligen till marknadskunskap. Artificiell Intelligens (AI) har diskuterats som en viktig informationsteknologi för att skapa marknadskunskap från Big Data. Litteraturen föreslår att AI i hög grad kan påverka skapande av marknadskunskap från Big Data och erfordrar mer forskning för att förstå AI potential.

Mänskliga resursers bidrag till skapande av marknadskunskap har tidigare fastställts i litteraturen. Men både forskare och utövare antyder att AI kommer att förändra hur människor bidrar till marknadskunskap. Litteraturen antyder också att skapande av marknadskunskap ännu inte har studerats tillräckligt från synvinkel av AI och mänskliga resurser.

Forskningsfrågan i denna avhandling är ”Hur skapar marknadsförare marknadskunskap från Big Data med hjälp av Artificiell Intelligens och mänskliga resurser?”

Denna forskningsfråga behandlas via fem delfrågor:

Fråga 1: Hur bidrar Artificiell Intelligens till att skapa marknadskunskap från Big Data?

Fråga 2: Hur påverkar Artificiell Intelligens skapandet av marknadskunskap från Big Data och vilka konsekvenser har det för mänskliga resurser?

Fråga 3: Hur samverkar Artificiell Intelligens och mänskliga resurser för att skapa marknadskunskap från Big Data?

Fråga 4: Vilka är ömsesidiga bidrag från Artificiell Intelligens och mänskliga resurser för att skapa marknadskunskap från Big Data?

Fråga 5: Vad bidrar Artificiell Intelligens och mänskliga resurser till olika aktiviteter för att skapa marknadskunskap från Big Data?

Forskningen som presenteras i denna avhandling omfattar två studier och tre artiklar. De tre artiklarna har redan eller kommer at publiceras i peer-review-tidskrifter. Forskningen följer en interpretivistisk paradigm med en kvalitativ forskningstrategi. Resultaten från studierna och artiklarna ger tre viktiga övergripande bidrag till kunskap och teori. För det första ger denna avhandling en icke-teknisk överblick av vad AI är, hur den fungerar och dess konsekvenser för att skapa marknadskunskap, därmed fyller den en lucka i marknadslitteraturen.

För det andra postulerar avhandlingen att AI är en resurs som uppfyller kriterierna för att vara ’valuable’ (värdefull), ’rare’ (sällsynt), in-imitable (imiterbar) och ’organized’ (organiserad) (VRIO) i enlighet med resursbaserad teori (RBT). Värdet på AI som en resurs uppstår delvis när AI omvandlar Big Data till information och även när AI omvandlar informationen till kunskap. Mänskliga resurser är en viktig tillgång för att skapa marknadskunskap från Big Data och förbättrar produktiviteten för AI som en resurs. Denna avhandling ger empiriska bevis på att den typ av bidrag som AI tillhandahåller som resurs skiljer sig från mänskliga förmågor. Specifikt ger AI-resurser främst bidrag av analytisk karaktär. Den analytiska beskaffenheten av AI omfattar behandling av data och information för att lösa komplexa, väldefinierade problem, lagrande av resultat från dessa behandlingsaktiviteter, och inlärning, d.v.s. gradvis förbättrande av dess behandlingseffektivitet och verkningsgrad.

Människans förmåga är i första hand av intuitiv och empatisk natur. Den intuitiva rollen omfattar människors förmåga att tänka kreativt och anpassa sig till nya situationer med hjälp av kreativ problemlösning, expertis och intuition. Människans empatiska natur omfattar deras förmåga att förstå det AI matar ut ur ett socialt, interpersonellt eller emotionellt perspektiv. Det omfattar en medvetenhet om ens egna känslomässiga tillstånd, empati, förmåga att bygga relationer och svara med känslomässig lämplighet i marknadsförings- eller försäljningssituationer. Medan AI-system blir alltmer sofistikerade när det gäller att känna igen, tolka och till och med svara på känslor, spelar mänskliga förmågor fortfarande en viktig roll i dessa uppgifter.

För det tredje bidrar denna avhandling till resource-based theory (resursbaserad teori). Den föreslår en konceptuell modell och lägger fram fem propositioner om relationen mellan AI som en resurs, mänskliga förmågor och marknadskunskap. Denna modell och propositionerna kan testas i framtida vetenskapligt arbete.

Denna avhandling är organiserad för att ge en övergripande introduktion till forskningsberättelsen. Första kapitlet ger en introduktion till forskningsområdet, följt av en litteraturöversikt, ett metodikapitel och ett kapitel som diskuterar resultat och bidrag till teori och praktik, samt redogör för möjligheter för framtida forskning. Uppsatserna och studierna som ligger till grund för denna avhandling presenteras i det sista kapitlet i denna avhandling.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2020. , p. 286
Series
TRITA-ITM-AVL ; 2020:10
Keywords [en]
Market knowledge, insights, big data, artificial intelligence, resource-based theory, resources, capabilities, machine learning, natural language processing
Keywords [sv]
Marknadskunskap, insikter, big data, artificiell intelligens, resursbaserad teori, resurser, kapacitet, machine learning (maskininlärning), natural language processing (naturlig språkbearbetning).
National Category
Business Administration
Research subject
Industrial Economics and Management
Identifiers
URN: urn:nbn:se:kth:diva-268989ISBN: 978-91-7873-470-2 (print)OAI: oai:DiVA.org:kth-268989DiVA, id: diva2:1400704
Public defence
2020-04-22, Stockholm, 09:00 (English)
Opponent
Supervisors
Available from: 2020-03-23 Created: 2020-02-28 Last updated: 2020-03-23Bibliographically approved
List of papers
1. Unpacking artificial intelligence – How the building blocks of artificial intelligence (AI) contribute to creating market knowledge from big data
Open this publication in new window or tab >>Unpacking artificial intelligence – How the building blocks of artificial intelligence (AI) contribute to creating market knowledge from big data
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Purpose:

This study explains artificial intelligence (AI) and its contributions to creating market knowledge from big data. Specifically, this study describes the foundational building blocks of any AI technology, their interrelationships and the implications of different building blocks with respect to creating market knowledge, along with illustrative examples.

 

Design/methodology/approach:

The study is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a model of how AI contributes to creating market knowledge from big data.

 

Findings:

The study explains AI from an input–processes–output lens and explicates the six foundational building blocks of AI. It discusses how the use of different building blocks transforms data into information and knowledge. It proposes a conceptual model to explicate the role of AI in creating market knowledge and suggests avenues for future research.

 

Practical implications:

This study explains the phenomenon artificial intelligence, how it works and its relevance for creating market knowledge for B2B firms.

 

Originality/value:

The study contributes to the literature on market knowledge and addresses calls for more scholarly research to understand AI and its implication for creating market knowledge.

Keywords
Market knowledge, B2B marketing, Artificial intelligence, Machine learning, Natural language processing, Big data
National Category
Business Administration
Identifiers
urn:nbn:se:kth:diva-268970 (URN)
Note

QC 20200228

Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-02-28Bibliographically approved
2. Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel
Open this publication in new window or tab >>Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel
2020 (English)In: Business Horizons, ISSN 0007-6813, E-ISSN 1873-6068Article in journal (Refereed) In press
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Artificial intelligence, natural language processing, machine learning, B2B, sales process, sales funnel, market knowledge
National Category
Business Administration
Identifiers
urn:nbn:se:kth:diva-268854 (URN)10.1016/j.bushor.2020.01.003 (DOI)
Note

QC 20200226

Available from: 2020-02-24 Created: 2020-02-24 Last updated: 2020-02-28Bibliographically approved
3. Artificial intelligence (AI) and value co-creation in B2B sales: Activities, actors and resources
Open this publication in new window or tab >>Artificial intelligence (AI) and value co-creation in B2B sales: Activities, actors and resources
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Artificial intelligence (AI) allows business actors to exchange resources, particularly information and knowledge, to strengthen their businesses. These AI-enabled value co-creation processes are playing a substantial role in the business-to-business (B2B) sales context. However, little is known about the mechanisms and the process of value co-creation enabled by AI. On this basis, this study addresses this gap by employing Service-Dominant Logic to understand value co-creation with AI. This study identifies the value co-creation process and provides an understanding of the actors, activities and resources during the usage of AI to create value in B2B sales. The study also identifies several limitations of AI, such as value co-creation is heavily dependent on human activities and resources. Lastly, we suggest that managers continue to manage customer expectations when using AI for value co- creation and highlight the necessity of human actors and resources in the value co-creation process.

National Category
Business Administration
Identifiers
urn:nbn:se:kth:diva-268987 (URN)
Note

QC 20200228

Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-02-28Bibliographically approved
4. Investigating the emotional appeal of fake news using artificial intelligence and human contributions
Open this publication in new window or tab >>Investigating the emotional appeal of fake news using artificial intelligence and human contributions
2019 (English)In: Journal of Product & Brand Management, ISSN 1061-0421Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2019
Keywords
Brand communication, Message framing, Machine learning, Emotional appeal, Natural language processing, Emotional branding, Communication model, Real news, Fake news, Artificial intelligence (AI)
National Category
Economics and Business
Identifiers
urn:nbn:se:kth:diva-268853 (URN)
Note

QC 20200226

Available from: 2020-02-24 Created: 2020-02-24 Last updated: 2020-02-28Bibliographically approved
5. #BuyNothingDay: investigating consumer restraint using hybrid content analysis of Twitter data
Open this publication in new window or tab >>#BuyNothingDay: investigating consumer restraint using hybrid content analysis of Twitter data
2020 (English)In: European Journal of Marketing, ISSN 0309-0566, E-ISSN 1758-7123, Vol. 54, no 2, p. 327-350Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2020
Keywords
Content analysis, Anti-consumption, Consumerism, BlackFriday, BuyNothingDay, Consumer restraint, Hybrid method, Twitter data
National Category
Economics and Business
Identifiers
urn:nbn:se:kth:diva-268852 (URN)10.1108/EJM-01-2019-0063 (DOI)000511131000001 ()
Note

QC 20200224

Available from: 2020-02-24 Created: 2020-02-24 Last updated: 2020-02-28Bibliographically approved

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