Investigating the Acceptance Factors of Marketing Systems Based on Artificial Intelligence in Small Industrial Companies

Purpose This study investigates the impact of individual characteristics and organizational factors on career outcomes in the digital workplace, with a focus on the mediating roles of artificial intelligence (AI) adoption and remote working. Design/methodology/approach A quantitative research design was employed, using data collected from 378 respondents through an online survey. Structural equation modeling (SEM) using AMOS was applied to test the hypothesized relationships among individual characteristics, organizational factors, AI adoption, remote working and career-related outcomes such as employee productivity, perceived employability and career sustainability. Findings The results reveal that individual characteristics significantly influence both AI adoption and remote working. Conversely, organizational factors do not significantly affect either. AI adoption positively impacts career sustainability, employee productivity and perceived employability. Remote work has a significant positive effect on employee productivity. Mediation analysis indicates that AI adoption partially mediates the relationship between individual characteristics and employee productivity, while remote working does not mediate the relationship between organizational factors and productivity. Practical implications The findings offer insights for organizations aiming to enhance employee career outcomes through digital transformation. Emphasis should be placed on supporting AI adoption and flexible work practices, especially for individuals with strong personal adaptability and digital skills. Originality/value This study contributes to the growing body of research on digital workplace transformation by integrating AI adoption and remote working as mediators between individual/organizational factors and career outcomes. It provides empirical evidence from an emerging market context, highlighting the central role of individual agency in leveraging digital tools for career development.

Purpose This study investigates the interplay between artificial intelligence (AI) adoption, ethical capitalistic orientation and the moderating role of human–AI synergy, a new construct developed via grounded theory. This study aims to understand how these variables influence organizational decision-making in the AI era, specifically promoting ethical practices and mitigating negative outcomes such as employee displacement. Design/methodology/approach Using both qualitative and quantitative regression analysis, this study examines the relationships among AI adoption, ethical capitalistic orientation and human–AI synergy. Statistical tools were used to test hypotheses, identify AI adoption’s impact on organizational ethics and assess human–AI synergy’s moderating role. This research also explores how combining AI and human collaboration can foster a more ethical, socially responsible business model. Findings Findings show AI adoption positively impacts ethical capitalistic orientation (β1 = 0.45, p = 0.0001). Human–AI synergy significantly enhances this effect (β2 = 0.30, p = 0.0005) and moderates the relationship between AI adoption and ethical practices (β4 = 0.15, p = 0.0030). The study emphasizes focusing on human–AI collaboration over workforce displacement to maintain ethical practices and achieve efficiency. These results highlight the importance of ethical AI adoption and social responsibility. Originality/value This study introduces human–AI synergy as a novel construct, demonstrating its critical moderating role in the relationship between AI adoption and ethical capitalistic orientation. It offers new insights into how businesses can leverage AI without workforce reduction or unethical practices. This research emphasizes building ethical frameworks for AI adoption, presenting a novel perspective integrating technology with human-centered decision-making.

Background: With growth in Artificial Intelligence (AI) adoption, challenges and hurdles are also becoming evident. Organizations implementing AI are challenged to find ways to leverage AI to produce optimum results and benefits for the organization. Understanding other organizations’ AI implementation journeys will help them start and implement AI. By understanding the different facets of AI implementation, they can strategize AI to gain business value. Though several studies have examined AI adoption, there are few studies on how firms implement it. We close this gap by studying AI adoption and implementations in various firms. Method: Using a qualitative approach of semi-structured interviews, we studied twenty global organizations of various sizes that have implemented AI. Results: The study categorizes the results into four major themes – facilitators, barriers, trends, and strategies for implementing AI. Our study reinforces the relevance of the TOE framework and Roger’s DOI theory in studying AI adoption. Organizational factors such as top management support, strategic roadmap, availability of skilled resources, and corporate culture influenced AI adoption. Their lack of data or poor data quality is a primary challenge. The privacy laws concerning data, as well as regulatory bottlenecks, further exacerbate this problem. We also identified and mapped the standard AI implementations to their AI technologies. We found that most of them exploit AI’s image and natural language processing capabilities to automate their processes. Regarding implementation, firms work with partners to obtain customer data and use federated learning. Conclusion: Understanding firms’ AI implementation journey will help us promote further adoption and experimentation. Organizations can identify areas where they can leverage AI to enhance value, prepare themselves for the future, start and proceed with AI implementation efforts and overcome barriers they might encounter.

Purpose The purpose of this study is to examine how AI adoption enhances operational performance in manufacturing firms and to investigate how variations in firms’ strategic focus moderate this relationship. Specifically, this study explores how AI adoption functions as a dynamic capability that enhances operational performance in manufacturing firms and investigates how different strategic orientations—exploration, exploitation, or ambidexterity—moderate this effect from the perspective of the Attention-Based View (ABV) of the firm. Design/methodology/approach This study employs a quantitative research method, using a sample of 426 Chinese manufacturing firms to examine the impact of artificial intelligence (AI) adoption on operational performance and to analyze the moderating role of firms’ strategic focus (exploration, exploitation, and ambidexterity). Data were collected through a structured questionnaire survey. To ensure the robustness of the findings and address potential endogeneity issues, hierarchical regression analysis and the two-stage least squares (2SLS) method were employed. Findings This study reveals that AI adoption significantly enhances the operational performance of manufacturing enterprises, but this effect is moderated by firms’ strategic focus. A high exploration tendency weakens the performance-enhancing effect of AI adoption due to implementation instability caused by excessive experimentation. A high exploitation tendency also reduces the positive impact of AI adoption, as over-reliance on existing processes constrains AI’s transformative potential. Furthermore, an ambidextrous strategy (coexistence of high exploration and high exploitation) further diminishes the positive effect of AI adoption, indicating that resource dispersion and increased coordination costs may offset its benefits. Originality/value From the perspective of Dynamic Capabilities Theory, this study empirically examines the impact of AI adoption—as a dynamic capability—on operational performance. Additionally, drawing on the Attention-Based View (ABV) of the firm, it uncovers the moderating role of firms’ strategic focus, addressing an existing research gap concerning strategic-level decision-making in AI adoption. The findings offer theoretical insights that guide enterprise managers in optimizing AI adoption strategies, helping them strike a balance between innovation and efficiency to maximize the benefits of digital transformation.

This study aims to systematically review the literature related to the adoption of Artificial Intelligence (AI) in digital marketing strategies for the period 2019–2025. The research method uses a Systematic Literature Review (SLR) approach based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. A total of 10 international scientific articles published during that period were analyzed to identify research trends, main themes, and future research directions. The results of the study show that AI plays a central role in transforming marketing strategies, especially in the context of customer personalization, operational efficiency, and predictive analytics. However, there are significant challenges, particularly related to data privacy, the ethics of AI use, and organizational and human resource readiness. This research contributes academically in the form of a conceptual framework for AI implementation in digital marketing, as well as strategic recommendations for industry players to utilize AI ethically and sustainably

Our paper analyzes the current state of research on artificial intelligence (AI) adoption from a production perspective. We represent a holistic view on the topic which is necessary to get a first understanding of AI in a production-context and to build a comprehensive view on the different dimensions as well as factors influencing its adoption. We review the scientific literature published between 2010 and May 2024 to analyze the current state of research on AI in production. Following a systematic approach to select relevant studies, our literature review is based on a sample of articles that contribute to production-specific AI adoption. Our results reveal that the topic has been emerging within the last years and that AI adoption research in production is to date still in an early stage. We are able to systematize and explain 35 factors with a significant role for AI adoption in production and classify the results in a framework. Based on the factor analysis, we establish a future research agenda that serves as a basis for future research and addresses open questions. Our paper provides an overview of the current state of the research on the adoption of AI in a production-specific context, which forms a basis for further studies as well as a starting point for a better understanding of the implementation of AI in practice.

The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning.

This study investigates the relationship between artificial intelligence (AI) adoption, knowledge sharing, and employee performance in Malaysian small and medium-sized enterprises (SMEs), with knowledge sharing examined as a mediating mechanism. SMEs represent a vital component of Malaysia’s economy, yet many face resource limitations that affect their readiness to fully harness AI technologies. While AI is recognized for its potential to enhance efficiency and innovation, its impact on employee performance is not always straightforward. This research therefore, explores whether knowledge sharing acts as the bridge through which AI adoption translates into performance outcomes. Data were collected through a survey of SME employees across service and manufacturing sectors, and the responses were analysed using partial least squares structural equation modelling (PLS-SEM). Measurement model results confirmed strong reliability and validity for the constructs of AI adoption, knowledge sharing, and employee performance. Structural model assessment revealed that AI adoption significantly and positively influences knowledge sharing but does not directly affect employee performance. Meanwhile, knowledge sharing revealed a strong and significant relationship with employee performance and was also found to partially mediate the relationship between AI adoption and performance. The study findings highlight that AI’s value in SMEs lies not in the technology itself but in its ability to foster knowledge exchange, learning, and collaboration. In addition, employee performance improves when AI is embedded into organizational practices that encourage knowledge sharing, thereby complementing human creativity and expertise. Theoretically, this study integrated the Knowledge-Based View (KBV) and the Technology–Organization–Environment (TOE) framework to explain how AI adoption and knowledge sharing practices together influence employee performance. Practically, the results underscore the need for SME leaders to move beyond technology acquisition and focus on building collaborative cultures that enable knowledge sharing. Overall, this research contributes both theoretical and practical insights into how SMEs can strategically leverage AI adoption to enhance employee performance through the mediating mechanism of knowledge sharing.

The integration of Artificial Intelligence (AI) into personal finance and wealth management has fundamentally reshaped financial behaviors and decision-making processes. The primary objective of this study is to evaluate the role of AI in influencing personal financial behaviors and wealth management outcomes. Specifically, it aims to determine how AI adoption, investment, and usage impact personal savings and net worth. This study adopts a quantitative approach, utilizing secondary data from trusted sources such as Our World in Data and the Federal Reserve Bank of St. Louis. The dataset spans from 2010 to 2022, capturing trends over a significant period of AI development and adoption. A multivariate regression model is employed to examine the relationships between the dependent variables, Personal Savings Rate and Change in Net Worth, and independent variables such as AI adoption rate, AI investment, and household debt-to-income ratio. Descriptive statistics, correlation analysis, and stationarity tests are conducted to ensure data reliability and model validity. Diagnostic checks, including heteroskedasticity tests and Durbin-Watson statistics, further validate the robustness of the results. The study reveals that AI adoption positively influences personal savings by encouraging disciplined financial behaviors, consistent with the findings of prior research. However, its impact on wealth accumulation is less direct, with AI investment showing a surprising negative association with changes in net worth. This indicates inefficiencies in resource allocation or lag effects in the benefits of large-scale AI investments. Traditional economic factors, such as household debt and spending habits, continue to play significant roles in shaping financial outcomes, highlighting the enduring influence of non-technological determinants. The study also underscores the role of macroeconomic variables, such as unemployment, in moderating AI’s impact, with precautionary savings behaviors emerging during periods of economic uncertainty. Based on the findings, several actionable recommendations emerge. For individuals, the adoption of AI-driven tools that promote financial literacy and track spending can enhance savings and improve overall financial health. Financial institutions should prioritize user-centric designs in AI platforms, ensuring accessibility and functionality for diverse demographics. Policymakers are encouraged to support initiatives that bridge disparities in AI adoption, such as digital literacy programs and affordable access to financial technologies. Moreover, strategic investment in AI tools that address wealth management complexities, such as portfolio optimization and risk assessment, is critical for improving long-term financial outcomes. Originality This study contributes to the growing body of literature on AI in finance by offering a dual focus on personal savings and wealth management. Unlike previous studies that often treat these domains independently, this research provides an integrated perspective, highlighting both the synergies and divergences in AI’s impact. The findings on the nuanced relationship between AI investment and financial outcomes offer a fresh lens for evaluating the effectiveness of technological advancements. Furthermore, the study’s emphasis on traditional economic factors alongside AI-related variables underscores its originality in bridging the gap between technological innovation and foundational economic principles. This approach provides a robust framework for future research and practical applications in finance.

This study investigates the impact of Artificial Intelligence (AI) adoption on the sustainable performance of small and medium-sized enterprises (SMEs). Employing a hybrid quantitative approach, this research combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) to examine the influence of various organizational, technological, and external factors on AI adoption. Key factors considered include top management support, employee capability, customer pressure, complexity, vendor support, and relative advantage. Data collected from 305 SMEs across multiple sectors were analyzed. The results reveal that all the proposed factors significantly and positively affect AI adoption, with top management support, employee capability, and relative advantage being the most influential predictors. Additionally, the adoption of AI technologies substantially enhances the economic, social, and environmental performance of SMEs, reflecting improvements in operational efficiency, cost reduction, and social value creation. The ANN results confirm the robustness of the SEM findings, highlighting the critical role of AI in driving sustainability outcomes. Furthermore, the study emphasizes the positive mediation effects of AI adoption on organizational performance, indicating that AI adoption serves as a key enabler in achieving both short-term operational gains and long-term sustainability objectives. This research contributes to the understanding of AI’s transformative role in enhancing the sustainable performance of SMEs in developing economies, offering strategic insights for both policymakers and business leaders.

Purpose This study investigates the complex interplay between technological and ethical factors influencing artificial intelligence (AI) adoption in entrepreneurship and startup ecosystems, with a particular focus on how these dynamics impact innovation outcomes and organizational performance. Design/methodology/approach Employing a comprehensive analytical framework, the research examines quantitative data to assess the relationships among technology related factors (such as interactivity, relative advantage, and perceived intelligence), ethical principles (including fairness, accountability, transparency, accuracy, and autonomy), ethical dilemma, and their collective influence on AI adoption and exploitative innovation within entrepreneurial contexts. Data was collected using a self-administrated questionnaire to 207 respondents, in the Iran entrepreneurship and startup ecosystem. The Partial Least Square-Structural Equation Modeling (PLS-SEM) technique was used to examine the proposed hypotheses of the study. Findings The findings reveal that technology related factors specifically interactivity, relative advantage, perceived intelligence, transparency, and accuracy significantly drive AI adoption among entrepreneurs. In contrast, ethical considerations such as fairness, accountability, and autonomy do not exhibit a direct influence on adoption decisions. Also, the moderating relationship of ethical dilemma between exploitative innovation and organizational performance by AI adaptation was rejected. Notably, the study highlights the pivotal mediating role of exploitative innovation, AI adoption enhances exploitative innovation, which in turn improves organizational performance; however, there is no direct relationship between AI adoption and organizational performance. Practical implications Entrepreneurs and startup leaders should prioritize AI technologies that offer clear interactive capabilities, relative advantages, and transparent, accurate operations to maximize adoption and performance benefits. While ethical principles remain important, their influence may be more pronounced at later stages of implementation or in highly regulated sectors. Policymakers and ecosystem builders are encouraged to focus on fostering environments that support the practical integration of AI, particularly in ways that enhance exploitative innovation and organizational scalability. Originality/value This research provides novel insights by disentangling the relative importance of technological versus ethical factors in AI adoption within entrepreneurial settings. It advances the literature by empirically demonstrating the limited direct impact of certain ethical considerations on adoption decisions and by highlighting the central role of exploitative innovation as a mediator between AI implementation and organizational outcomes.

PurposeThis paper aims to explore factors impacting citizen intention toward artificial intelligence (AI) adoption, considering government regulation as a moderating variable. It focuses on the Palestinian Cellular Communications Sector in Gaza Strip, providing insights into the citizen-AI relationship dynamics. The research contributes to enhancing comprehension of AI technology from clients’ perspective.Design/methodology/approachTo test the hypotheses, a questionnaire was used in an empirical study to collect primary data. In total, 347 Palestinian citizens responded to the survey.FindingsThe findings of this paper reveal that perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns significantly influence citizen intention toward AI adoption. Furthermore, government regulations as a moderating variable strengthen the impact of perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns on citizen intention toward AI acceptance and adoption. Thus, further research should explore specific domains and cultural contexts to gain a more comprehensive understanding of the factors shaping acceptance and adoption.Research limitations/implicationsThe findings of the study should be understood in the context of their limitations. First, the study ignored cultural or domain-specific subtleties in favor of generic characteristics, which calls for more research in these particular circumstances. Second, relying on self-reported data might result in biases and limitations due to subjectivity in reporting, indicating the necessity for alternate data gathering methods and approaches in future research.Practical implicationsPolicymakers, developers and organizations working to promote the acceptability and implementation of AI applications should consider the practical implications of this study’s results. To secure the long-term use of AI technologies in a responsible and user-centric way, policymakers should give priority to public education and awareness, user-centered design and ethical AI development techniques. They should also stimulate partnerships and create monitoring systems.Originality/valueThis paper investigates the originality of factors that influence citizen intention toward AI acceptance and adoption. It uniquely examines the moderating role of government regulations in shaping this intention. By addressing this novel aspect, the paper contributes to advancing our understanding of the complex dynamics surrounding citizen intentions toward AI applications.

Artificial intelligence (AI) offers organizations much potential. Considering the manifold application areas, AI’s inherent complexity, and new organizational necessities, companies encounter pitfalls when adopting AI. An informed decision regarding an organization’s readiness increases the probability of successful AI adoption and is important to successfully leverage AI’s business value. Thus, companies need to assess whether their assets, capabilities, and commitment are ready for the individual AI adoption purpose. Research on AI readiness and AI adoption is still in its infancy. Consequently, researchers and practitioners lack guidance on the adoption of AI. The paper presents five categories of AI readiness factors and their illustrative actionable indicators. The AI readiness factors are deduced from an in-depth interview study with 25 AI experts and triangulated with both scientific and practitioner literature. Thus, the paper provides a sound set of organizational AI readiness factors, derives corresponding indicators for AI readiness assessments, and discusses the general implications for AI adoption. This is a first step toward conceptualizing relevant organizational AI readiness factors and guiding purposeful decisions in the entire AI adoption process for both research and practice.

This study examines the impact of artificial intelligence (AI) adoption on economic growth and unemployment across G7 countries and India. As AI emerges as a transformative technology, there is a need to understand its effects on labour markets and develop appropriate policy frameworks. The research analyses historical patterns of automation and technological change to provide context for the current AI revolution. The study employs a mixed-methods approach, combining quantitative statistical analyses with qualitative assessments. Variables examined include GDP growth rates, R&D investment levels, AI adoption rates, and productivity gains. Descriptive statistics, correlation analyses, regression models, and cluster analyses were conducted to identify relationships between key variables. Results reveal a significant technology adoption gap between high-tech leaders and developing economies. A growth paradox was observed in developing tech economies, where rapid AI adoption did not necessarily translate to proportional economic gains. The research found complex relationships between technological advancement, unemployment rates, and investment levels across countries. Cluster analysis identified three distinct groups: Advanced Economies, Technology Leaders, and Developing Tech Economies. ANOVA and chi-square tests confirmed statistically significant differences between these clusters. Multiple regression analysis on unemployment rates provided insights into the factors influencing job displacement. Key findings include: 1) AI adoption shows potential to significantly boost GDP growth and productivity, though effects vary by country. 2) The relationship between technological progress and unemployment is nuanced, defying simplistic narratives. 3) Investment levels strongly correlate with technological advancement, but other factors also play important roles. 4) Leaders in AI adoption exhibit certain common characteristics, offering potential lessons for other nations.The study concludes that while AI presents substantial opportunities for economic growth, its benefits are not uniformly distributed. Policymakers must develop strategies for inclusive growth, equitable access to technological advancements, and robust safety nets to address potential economic stratification. Future research directions are suggested to further explore the long-term implications of AI on global economies and labour markets. This research contributes to the ongoing dialogue on AI's societal impact and provides evidence-based insights to inform policy decisions in an era of rapid technological change. The findings underscore the need for nuanced, context-specific approaches to AI adoption and regulation across different economic contexts.

This study examines the impact of artificial intelligence (AI) adoption on economic growth and unemployment across G7 countries and India. As AI emerges as a transformative technology, there is a need to understand its effects on labour markets and develop appropriate policy frameworks. The research analyses historical patterns of automation and technological change to provide context for the current AI revolution. The study employs a mixed-methods approach, combining quantitative statistical analyses with qualitative assessments. Variables examined include GDP growth rates, R&D investment levels, AI adoption rates, and productivity gains. Descriptive statistics, correlation analyses, regression models, and cluster analyses were conducted to identify relationships between key variables. Results reveal a significant technology adoption gap between high-tech leaders and developing economies. A growth paradox was observed in developing tech economies, where rapid AI adoption did not necessarily translate to proportional economic gains. The research found complex relationships between technological advancement, unemployment rates, and investment levels across countries. Cluster analysis identified three distinct groups: Advanced Economies, Technology Leaders, and Developing Tech Economies. ANOVA and chi-square tests confirmed statistically significant differences between these clusters. Multiple regression analysis on unemployment rates provided insights into the factors influencing job displacement. Key findings include: 1) AI adoption shows potential to significantly boost GDP growth and productivity, though effects vary by country. 2) The relationship between technological progress and unemployment is nuanced, defying simplistic narratives. 3) Investment levels strongly correlate with technological advancement, but other factors also play important roles. 4) Leaders in AI adoption exhibit certain common characteristics, offering potential lessons for other nations. The study concludes that while AI presents substantial opportunities for economic growth, its benefits are not uniformly distributed. Policymakers must develop strategies for inclusive growth, equitable access to technological advancements, and robust safety nets to address potential economic stratification. Future research directions are suggested to further explore the long-term implications of AI on global economies and labour markets. This research contributes to the ongoing dialogue on AI's societal impact and provides evidence-based insights to inform policy decisions in an era of rapid technological change. The findings underscore the need for nuanced, context-specific approaches to AI adoption and regulation across different economic contexts.