What is the Market Value of Artificial Intelligence and Machine Learning? The Role of Innovativeness and Collaboration for Performance

Fredström, Ashkan; Parida, Vinit; Wincent, Joakim; Sjödin, David; Oghazi, Pejvak

doi:10.1016/j.techfore.2022.121716

Cited by 18 publications

(6 citation statements)

References 33 publications

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“…Among the key benefits of a digital co-creation strategy is the ability to provide companies with access to massive amounts of data, improve their informed decision-making capabilities, and increase their efficiency. Moreover, the use of AI can assist in developing new applications and improving existing products and customer services [69], [80] in the interests of greater sustainability. By fostering a sense of ownership of the cocreated solutions, this strategy not only allows customers to have greater input in forging their own products and services but also leads to a more dynamic relationship between manufacturer and customer.…”

Section: Configurational Strategies Leading To a High Level Of Custom...mentioning

confidence: 99%

Linking Digital Servitization and Industrial Sustainability Performance: A Configurational Perspective on Smart Solution Strategies

Kolagar,

Parida,

Sjödin

2024

IEEE Trans. Eng. Manage.

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Manufacturing companiesare introducing innovative ways to facilitate the sustainable transition of their customers' operations. The emerging literature on digital servitization proposes numerous factors, such as the use of advanced AI analytics, orientation toward outcomes, and aligning ecosystem partnerships, which can potentially influence the sustainable performance of industrial customers. However, there is currently a lack of understanding regarding how these factors interact to result in sustainable outcomes. Hence, this study seeks to shed light on these complex relationships by identifying viable smart solution strategy configurations for achieving customer sustainable performance. Drawing on a dataset of 180 Swedish manufacturing firms, this study uses a configurational comparative methodnamely, fuzzy-set qualitative comparative analysis (fsQCA) -to identify the impact of different configurations of "AI-driven optimization", "outcome orientation", "value co-creation", and "ecosystem orchestration" conditions on the realization of customer sustainable performance. This study has identified five smart solution strategies that empower manufacturers to realize sustainable performance for their customers. Among the five configurational strategies identified, the first configurational strategy appears to be the most prominent, as it is based on an outcome-based approach in which the firm uses its technological expertise and its ecosystem partnerships to take over customer operations. Thus, this study contributes to the ongoing discussion in digital servitization on its enabling role for industrial sustainability practices.

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Section: Configurational Strategies Leading To a High Level Of Custom...mentioning

confidence: 99%

Linking Digital Servitization and Industrial Sustainability Performance: A Configurational Perspective on Smart Solution Strategies

Kolagar,

Parida,

Sjödin

2024

IEEE Trans. Eng. Manage.

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“…Topic Dataset Used Classification Method [33] Negation in Sentence Kaggle Naïve Bayes, SVM, ANN, RNN [34] Market Value of AI and ML Google API Services Naïve Bayes, Decision Tree, Random Forest [35] Indonesian Hotel Review Traveloka Website LSTM [36] Subsistence Marketplace Respondents' Data in Bangladesh Common Method Variance (CMV) [37] Mature Destination TripAdvisor, St. Mark Square, and the Doge's Palace LSTM [38] Indonesian-Based Aspect Sentiment Indonesian Marketplace…”

Section: Refsmentioning

confidence: 99%

Sentiment Analysis on Marketplace in Indonesia using Support Vector Machine and Naïve Bayes Method

Dakwah,

Firdaus,

Furizal

et al. 2024

J. Ilm. Tek. Elektro Komput. Dan Inform

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This research addresses the challenges of marketplace customer feedback, which is an important aspect in today's era of online transactions. Marketplaces often receive many unsatisfactory comments from their customers through social media platforms. One approach that can be used to address this is sentiment analysis. This research contributes new insights as recommendations for marketplaces based on customer opinions on available services and delivery. The sentiment analysis methods used are Naive Bayes and Support Vector Machine because they are considered the best methods in training text-based classification models. Before being classified, the data goes through preprocessing stages such as cleaning, case folding, filtering, stemming, and tokenizing, as well as feature extraction stages using Term Frequency -Inverse Document Frequency (TF-IDF). The objects analyzed are divided into several well-known marketplaces in Indonesia such as Tokopedia, Lazada, and Shopee in discussing services and delivery of goods. The data used in this study comes from Twitter (X) social media accessed on August 27, 2023, using crawling techniques and successfully obtained as much as 2057 Tweet data. The best accuracy is obtained in the SVM method when compared to the Naive Bayes method. Words obtained based on service talks include price, service, application service, feedback, independence, and others. As for the delivery of goods, common words such as COD, delivery, package, courier, cheap, price, and others appear. Both methods used have good accuracy and can be recommended for use in similar research.

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“…, 2011). AI have been deemed novel technology and Fredström et al. (2022) noted the need to examine ML and AI's effects.…”

Section: Literature Reviewmentioning

confidence: 99%

Automation-augmentation paradox in organizational artificial intelligence technology deployment capabilities; an empirical investigation for achieving simultaneous economic and social benefits

Kumar,

Bhattacharyya,

Krishnamoorthy

2023

JEIM

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PurposeThe purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society.Design/methodology/approachThis study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis.FindingsThe results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance.Research limitations/implicationsThis study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored.Practical implicationsOrganizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox.Social implicationsIncreased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study.Originality/valueThis study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level.

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What is the Market Value of Artificial Intelligence and Machine Learning? The Role of Innovativeness and Collaboration for Performance

Cited by 18 publications

References 33 publications

Linking Digital Servitization and Industrial Sustainability Performance: A Configurational Perspective on Smart Solution Strategies

Linking Digital Servitization and Industrial Sustainability Performance: A Configurational Perspective on Smart Solution Strategies

Sentiment Analysis on Marketplace in Indonesia using Support Vector Machine and Naïve Bayes Method

Automation-augmentation paradox in organizational artificial intelligence technology deployment capabilities; an empirical investigation for achieving simultaneous economic and social benefits

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