AI Adoption and Firm Performance: Management versus IT

Alekseeva, Liudmila; Giné, Mireia; Samila, Sampsa; Taska, Bledi

doi:10.2139/ssrn.3677237

Cited by 13 publications

(12 citation statements)

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“…The ability of artificial intelligence to improve forecasting can help firms to target customers better, optimize costs and improve their productivity (Kim et al, 2011). However, before adopting artificial intelligence, firms need to identify the key areas where an improved prediction by artificial intelligence will generate positive outcomes (Alekseeva et al, 2020). They need to provide answers to the following questions: what problem will artificial intelligence solves?…”

Section: Eriksson Et Al Inmentioning

confidence: 99%

“…What expertise is required to complement the artificial intelligence? And how the algorithm's output will be used to make decisions (Alekseeva et al, 2020). The answers to these questions will determine the successful adoption of artificial intelligence.…”

Section: Eriksson Et Al Inmentioning

confidence: 99%

“…Artificial intelligence adoption facilitates better targeting of customers, thereby attracting more customers and improving the marketing performance of firms (Lichtenthaler, 2019). Alekseeva et al (2020) opined that adoption of artificial intelligence can increase forecasting which will in turn facilitate better decision making and cost reduction such as the reduction in the cost of handling customer orders and inventory management costs. Kuzey et al (2014) posited that artificial intelligence adoption can help a firm to increase its operational efficiency, improve its supply chain and maintenance operations, optimize costs, product improvement and provide a positive and pleasant customer experience which will in turn boost sales and improve the marketing performance of firms.…”

Section: Eriksson Et Al Inmentioning

confidence: 99%

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Journal on Artificial intelligence .pdf

Asemota,

Ifekanandu

2024

Preprint

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<p> This study examined artificial intelligence adoption and marketing performance of quoted manufacturing firms in Nigeria. The study adopted the positivism research philosophy and correlational research design. The population of the study consisted of 426 managers drawn from the 71 quoted manufacturing firms in Nigeria. The managers include branch managers, operational managers, production managers, marketing managers and sales managers of the firms. A sample size of 206 managers was used for the study. The sample size was determined mathematically using the Taro Yamene’s formula. A structured questionnaire was used to obtain data from the respondents. The data collected were analyzed statistically while the hypotheses were tested using Spearman Rank Order Correlation Coefficient (rho). The SPSS version 23.0 was used to perform the bivariate analysis. The findings revealed that the application of artificial intelligence technologies in marketing operations has a significant relationship with sales growth of quoted manufacturing firms in Nigeria. The study also revealed that the application of artificial intelligence technologies in marketing operations has a strong and significant relationship with market share growth of quoted manufacturing firms in Nigeria. The study equally confirmed that artificial intelligence capabilities have a strong and significant relationship with sales growth of quoted manufacturing firms in Nigeria. The study also reported that artificial intelligence capabilities has a strong and significant relationship with market share growth of quoted manufacturing firms in Nigeria. Based on these findings, it was concluded that artificial intelligence adoption significantly relate to marketing performance of quoted manufacturing firms in Nigeria. Based on these findings and conclusion, it was recommended that quoted manufacturing firms in Nigeria especially those that are experiencing poor marketing performance should adopt artificial intelligence technologies in their marketing operations as it would improve their marketing performance. </p>

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Section: Eriksson Et Al Inmentioning

confidence: 99%

Section: Eriksson Et Al Inmentioning

confidence: 99%

Section: Eriksson Et Al Inmentioning

confidence: 99%

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Journal on Artificial intelligence .pdf

Asemota,

Ifekanandu

2024

Preprint

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“…Hence, looking at labour featuring AI-related skills may help shed light on the use of AI technologies (Tambe, 2013[20]). Using tools like ML algorithms and AI-related keywords, such as the ones found in Baruffaldi et al (2020[2]), a number of papers including Alekseeva et al (2020[21]; 2021 [22]), Babina et al (2020[23]), Squicciarini and Nachtigall (2021 [24]) and Samek et al (2021[25]) identify AI-related skills and jobs in online job postings data from Lightcast™ (formerly known as Burning Glass Technologies). Part of this analysis focuses on job postings rather than firms and highlights that, as AI permeates the economy, it moves into new sectors and AI skills are demanded in relation to a wider range of occupations, firms and industries.…”

Section: Existing Evidence On Ai Adoption By Firmsmentioning

confidence: 99%

“…The analysis focuses on AI adopters for which information on their characteristics is available in 2019, the latest available year with comprehensive coverage in Orbis©. 22 This is aimed at providing the most recent available picture about their characteristics. 23 Some basic descriptive statistics are reported in Table 4.1 below.…”

Section: Matching the Data In Practicementioning

confidence: 99%

Identifying and characterising AI adopters

2022

OECD Science, Technology and Industry Working Papers

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Working PapersOECD Working Papers should not be reported as representing the official views of the OECD or of its member countries. The opinions expressed and arguments employed are those of the authors. Working Papers describe preliminary results or research in progress by the author(s) and are published to stimulate discussion on a broad range of issues on which the OECD works. Comments on Working Papers are welcomed, and may be sent to Directorate for

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The automated sustainability auditor: Does artificial intelligence curtail greenwashing behavior in Chinese firms?

Khan,

Huo,

Zahid

et al. 2024

Bus Strat Env

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Corporate stakeholders are intrigued by the potential collaboration between AI and environmental reporting to maintain competitiveness in the digital and sustainable economy. This exploration is crucial given the persistent pressures driving companies to engage in greenwashing practices for legitimacy. Aiming to shed light on the function of AI in reducing the prevalence of greenwashing by Chinese businesses, the findings, derived from panel data estimation of Chinese A‐share firms, suggest that the implementation of AI has a positive impact on the fight against greenwashing. The investigation presents compelling evidence that greenwashing mechanism control can be accelerated by AI technologies. Organizations that invest strategically in artificial intelligence exhibit a diminished propensity to obfuscate environmental performance by means of AI‐enabled automation and enhanced data‐driven decision‐making. Intriguingly, the study demonstrates that the substantial disparate greenwashing impact of AI depends on ownership structure. In comparison to non‐SOEs, state‐owned enterprises (SOEs) demonstrate diminished AI control over greenwashing. Significantly, the research utilizes a variety of validation methods, such as instrumental variable approach, propensity score matching, and two‐stage least squares, to ensure the validity of the primary findings.

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AI Adoption and Firm Performance: Management versus IT

Cited by 13 publications

References 0 publications

Journal on Artificial intelligence .pdf

Journal on Artificial intelligence .pdf

Identifying and characterising AI adopters

The automated sustainability auditor: Does artificial intelligence curtail greenwashing behavior in Chinese firms?

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