Continuous advances in information technologies, such as Artificial intelligence (AI), are opening up new and exciting opportunities for value co-creation between economic actors. However, little is known about the mechanisms and the process of value co-creation enabled by AI. While scholars agree that AI technology significantly changes human activities and human resources, currently we do not have an adequate understanding of how humans and AI technology interact in value co-creation. This is the central phenomenon investigated in this article. Specifically, using Service-Dominant Logic (S-DL) as a lens, this study investigates the activities, roles and resources that are exchanged in AI-enabled value co-creation, using the creation of competitive intelligence as a research context. The analysis suggests that AI-enabled value co-creation processes are complex interactions between human and non-human actors who perform any of six different roles either jointly or independently. This article contributes to SD-L and provides a deeper understanding of the activities (the ‘how’), the actors (the ‘who’), and the resources (the ‘what’) in AI-enabled value co-creation, thus helping to close an identified gap in the literature

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.

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.

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.

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.

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.

Purpose The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research. Design/methodology/approach The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge. Findings The paper explains AI from an input-processes-output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge. Practical implications - Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions. Originality/value The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.

Attracting and retaining IT talent remains challenging for IT ecutives. The limited supply of highly skilled candidates, combined th high workforce mobility, results in considerable hiring, training, d developing costs. To help IT employers overcome these challenges, e authors discuss employer branding as one strategy to manage firms' putations as "great places to work." Based on a content analysis of arly 15 000 employee reviews, this article identifies and describes ght values that IT professionals care about when evaluating IT ployers, highlights which values are most important, and provides commendations for how IT firms can use employer brand intelligence to tract and retain IT talent to remain competitive.

Much of the attention surrounding blockchain today is focused on financial services, with very little discussion about nonfinancial services firms and how blockchain technology may affect organizations, their business models, and how they create and deliver value. In addition, some confusion remains between the block chain (with definite article) and blockchain (no article), distributed ledger technologies, and their applications. Our article offers a primer on blockchain technology aimed at general managers and executives. The key contributions of this article lie in providing an explanation of blockchain, including how a blockchain transaction works and a clarification of terms, and outlining different types of blockchain technologies. We also discuss how different types of blockchain impact business models. Building on the well-established business model framework by Osterwalder and Pigneur, we outline the effect that blockchain technologies can have on each element of the business model, along with illustrations from firms developing blockchain technology. 

A large body of literature exists on understanding various consumer resistance movements. However, the question of why everyday consumers engage in consumption restraint has received little attention in the scholarly discourse to date. In this study, we investigate the motivations of people who participate in “Buy Nothing Day,” the annual day of consumption restraint that corresponds with Black Friday. To do this, we examine 1813 consumer tweets referring to this event. Consumer motivations were categorized as relating to consumerism, spiritual welfare, wastefulness, environment, inequality, anti-capitalism, financial responsibility, and financial necessity. Of these, consumerism and spiritual welfare were the most common motivators. In addition, most consumers conveyed positive sentiments toward this event. Our findings shed light on motivations that average consumers may have to restrain their consumption and provide insight for firms wishing to better understand and respond to this type of individual. References Available Upon Request

Givers of luxury gifts face recipients with different levels of expertise and have choices of gifts that can range from experiential to enduring in nature. Inspired by a study undertaken by Belk (1982), the current research seeks to develop a framework that allows the classification of different levels of involvement of the gift giver, based on their conjectures about the expertise of the recipients and the lasting or ephemeral nature of the gift. Following a precedent set by Paschen et al. (2016), we modify Berthon et al.’s (2009) aesthetics and ontology framework. The latter classifies luxury brands based on their aesthetic and ontological modes and is defined by the aesthetic end points of novice and expert and the ontological dichotomy of transience vs. enduring. In our modification, we develop four specific recipient categories based on the perceived expertise of the intended recipient representing the aesthetic mode and the endurance or ephemerality of the gift described in the ontological mode. The resultant typology identifies the “classic collector,” “skillful user,” “neophyte consumer,” and “paying magpie,” assigning different levels of product and task involvement to each category. In doing this, we add detail to the perspective taken in Belk’s original study on the separate aspects of involvement, where product involvement represents an enduring construct, whereas task involvement is situationally oriented and thus temporary rather than ongoing. We also present numerous implications to practice, providing insights into modifications to the marketing mix that luxury goods marketers may consider, depending on the different consumer group they are targeting. Marketing for expert gift recipients is well aligned with classic traits of luxury—emphasizing the exclusivity permeating through the price, the purchase experience, and the product itself. Gifts intended for novices, on the other hand, have to be universally known and widely available without diluting the exclusive premise of luxury. Enduring gifts generally increase the task involvement of the gift giver and therefore require marketing efforts that reduce the perceived risk. We conclude with several suggestions for further validation of the framework and related research that may arise out of this work. References Available Upon Request