Abstract
Purpose
Using AI to strengthen creativity and problem-solving capabilities of professionals involved in innovation management holds huge potential for improving organizational decision-making. However, there is a lack of research on the use of AI technologies by innovation managers. The study uses the theory of appropriation to explore how specific factors – agile leadership (AL), innovation orientation (IO) and individual creativity (IC) – impact innovation managers' use of generative AI tools, such as ChatGPT (CGA).
Design/methodology/approach
The research model is tested through a large-scale survey of 222 Italian innovation managers. Data have been analyzed using structural equation modeling following a two-step approach. First, the measurement model was assessed to ensure the constructs reliability. Subsequently, the structural model was analyzed to draw the conclusions on theorized model relationships and their statistical significance.
Findings
The research findings reveal positive associations between IO and IC with CGA, demonstrating that innovation managers who exhibit strong innovation orientations and higher Individual Creativity are more likely to adopt and personalize ChatGPT. However, the study did not confirm a significant association between AL and CGA.
Originality/value
Our findings have important implications for organizations seeking to maximize the potential of generative AI in innovation management. Understanding the factors that drive the adoption and customization of generative AI tools can inform strategies for better integration into the innovation process, thereby leading to enhanced innovation outcomes and improved decision-making processes.
Keywords
- Innovation
- Innovation managers
- Generative artificial intelligence
- ChatGPT
- Appropriation theory
Citation
Cimino, A., Felicetti, A.M., Corvello, V., Ndou, V. and Longo, F. (2024), "Generative artificial intelligence (AI) tools in innovation management: a study on the appropriation of ChatGPT by innovation managers", Management Decision, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MD-10-2023-1968
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited
1. Introduction
Over the years, the evolution of technology has significantly shaped and impacted operational work (Beer and Mulder, 2020; Bagnoli etal., 2019). Traditionally, technological advancements have primarily focused on supporting production processes flexibility (Zhong etal., 2017), optimizing production flow (Vaidya etal., 2018), increasing productivity (Oztemel and Gursev, 2020) and enhancing efficiency (Sahoo and Lo, 2020) across a diverse range of industries. These advancements have definitely driven progress and effectiveness, fundamentally by acting on the operational landscapes of organizations. However, the transformative potential of technology is not limited to the operational framework. In fact, with the advent of generative artificial intelligence (AI), a new frontier emerges, where technology goes beyond the boundaries of operational tasks and extends its impact to the intellectual domain (Sadaf etal., 2023). Generative AI, characterized by its creative feature and capacity to ideate, has the potential to reshape intellectual work and revolutionize the field of innovation management (AL-Sa’di and Miller, 2023). In today’s business environment, innovation management is a key driver for success (AL-Sa’di and Miller, 2023); in fact, organizations that excel in innovation are not just surviving but maintain a competitive edge standing at the forefront of their industries (Manohar and Pandit, 2014). Innovation management’s success relies on generating new ideas (Standing etal., 2015), creative problem-solving (Proctor, 2010) and innovative firms’ capabilities (Schewe, 1994). Within the innovation management literature, various approaches have been explored to effectively manage innovation processes or broader R&D initiatives (Cooper, 1986; Rothwell, 1994; Rogers, 2003; Tidd and Bessant, 2020). In this context, innovation managers become vital drivers, guiding organizations toward innovative horizons and using technology to bring about transformative change (Maier and Brem, 2018). It is within the implementation of innovation processes that generative AI, with its intrinsic creative power (Chubb etal., 2022), emerges as a formidable ally for the innovation managers (Haefner etal., 2021). With generative AI tools, such as ChatGPT, innovation managers access to a unique source of creativity potential (Mondal etal., 2023) as well as they can tackle tasks that were recently accomplished exclusively by humans (Füller etal., 2022). In this regard, generative AIsupports in overcoming human information processing constraints during identification and idea generation phases (Mamoshina etal., 2016; Brynjolfsson and McAfee, 2017; Veit etal., 2017; Tran and Ulissi, 2018). Moreover, generative AI demonstrates the capacity to identify problems, opportunities and threats beyond local search routines and knowledge domains, thereby facilitating the discovery and generation of innovative ideas (Tsh*toyan etal., 2019; Haefner etal., 2021). Generative AI extends its support to information processing and decision-making, with the potential to even assume these roles (Verganti etal., 2020). Inthe context of idea selection, generative AI emerges as a powerful ally, providing objective information and insights, enhancing innovation managers' ability to select ideas that could lead companies’ competitive advantages (Haefner etal., 2021). Furthermore, generative AI can facilitate the launch and implementation phase of new products and services (Kumar etal., 2019). Finally, the dynamic contribution of generative AI to innovation management is evident also in team activities, where it stimulates discussions in thinking processes (Bouschery etal., 2023). While the practical applications of AI in enhancing creativity and decision-making are well-documented, there is limited empirical research focusing on the appropriation process by innovation managers, particularly in the context of generative AI tools like ChatGPT. In order to fully leverage these potentials and maximize their effectiveness, innovation managers must possess the capability to customize generative AI tools to their specific needs (Gkinko and Elbanna, 2023). In this context, this paper aims to study the appropriation process of generative AI tools by innovation managers to drive and facilitate innovation processes.
To accomplish this objective, the authors apply the appropriation theory to investigate how specific factors, such as agile leadership (AL), innovation orientation (IO) and individual creativity (IC), influence the appropriation of generative AI tools, such as ChatGPT (CGA), among innovation managers.
From a theoretical perspective, our study intends to enrich the understanding of how innovation managers adopt and effectively utilize generative AI, underscoring the pivotal role of innovation manager capabilities in this process. Preliminary findings suggest that these factors play a crucial role in how innovation managers integrate ChatGPT into their workflow, providing new insights into the theoretical understanding of technology adoption in the field of innovation management.
The remainder of the paper is organized as follows. Section 2 presents the study’s theoretical framework, including the literature review, the authors' developed theoretical model and the formulation of hypotheses to guide the research. Proceeding to Section 3, the methodology employed is reported with a detailed description of the empirical analysis carried out. Section 4 is dedicated to presenting the research results, while Section 5 discusses the main findings of the study. Section 6 examines the principal implications of the work, and Section 7 points out the limitations and potential future research challenges. Finally, Section 8 summarizes the conclusions of the work.
2. Theory
2.1 Theoretical background
The interplay between technological utilization and organizational outcomes has garnered broad acknowledgment within the academic milieu. However, it is pivotal to note that such outcomes are contingent upon the way the organization, and the individuals therein, not only adopt but also adeptly modify technological structures to align with their intrinsic requirements (Kummitha, 2020). Technologies can be used in expected and sometimes even unexpected ways, with outcomes that can in some cases be positive (Scott etal., 1998).
In this sense, many scholars employed Adaptive Structuration Theory (AST) to scrutinize the implementation of emergent technologies within organizations (Dennis and Wixom, 2002). AST provides a theoretical lens to describe how technologies are structured and adapted to align with organizational nuances and to explain different impact of the introduction of the same technology in diverse organizations (DeSanctis and Poole, 1994). Different impact results from different ways people within an organization use technologies, consistently with their work requirements (Kang etal., 2012; Wang etal., 2023). The apt use of specific technologies, when tailored to the distinct environment and organizational features, can yield positive outcomes for both employees and the organization as a whole (DeLone and McLean, 2003). AST posits the core concept of appropriation, namely the ways in which individuals, groups or organizations adopt and adapt technologies to meet their specific needs or to fit into their social practices (Ko etal., 2021). According to Nguyen etal. (2021, p.3) “appropriation reflects IT use as either ‘faithful’ based on the designer’s intention or ‘unfaithful’ as based on a user’s accidental experiences that eventually result in an outcome.”
The AST emphasizes the social dimension in the interaction between people and technologies, and in particular on the co-evolution of agents and technologies (Orlikowski and Iacono, 2001; Jones and Karsten, 2008). The concepts of Appropriation and AST intersect at the crux of understanding the dynamic interplay between technology and social constructs within organizational settings (DeSanctis and Poole, 1994). These concepts find their origins in sociotechnical studies, which have long been interested in examining how users interact with and modify technologies within their social environments (Suchman, 1987). Additionally, these studies explore how social systems and structures are formed (Giddens, 1979). The theory of appropriation is closely linked to AST, focusing on the appropriation of technologies by individuals and organizations. It dwells on how people “own” technologies in their daily activities, viewing technologies not as mere neutral tools but social artifacts shaped by the social and cultural context they are used in Orlikowski (1992). The essence of appropriation lies in customization, where users tailor a technology to better suit their needs, and reinterpretation, where users discover new meanings or uses for a technology.
Prior research on AST mainly focuses on information and communication technologies, with a specific attention toward IT and IS appropriation (Ko etal., 2021; Nguyen etal., 2021; Wang etal., 2023). Many works deal with team-based technologies and group support systems. In this context the social structure is formed through the interaction among group tasks and depends on several factors such as the organizational environment, group style and conventions (Kang etal., 2012; Romanow etal., 2018). AST has been applied to study the evolution-in-use of advanced IT by groups and teams, extending the framework to individuals and explaining the adaptation of malleable IT by non-technical users (Schmitz etal., 2016). Starting from this point, other works addressed the study of individual-based IT (Janson etal., 2017; Nguyen etal., 2021).
More recently, AI epitomized a pinnacle in recent ICTs advancements, catalysing a shift toward more intelligent and autonomous digital systems. Appropriation theory in the domain of AI encapsulates an evolving field of inquiry exploring how individuals or groups adopt, modify and integrate AI technologies within their specific contexts. The lens of appropriation theory assists in delving into the nuances of how AI technologies are made one’s own, especially in ways unforeseen by the developers. In the burgeoning landscape of AI, the theory’s applicability is underscored as AI technologies permeate various sectors with a plethora of potential applications. A first attempt to study the actual use of conversational AI, in particular AI chatbot in workplaces has been proposed by Gkinko and Elbanna (2023). They provided a classification of users according to their AI appropriation behavior. Booyse and Scheepers (2023) investigated the barriers to the adoption of AI as automated organizational decision-making tools. Dolata etal. (2023) studied appropriation of those technologies in educational practice.
Despite these studies collectively build a vibrant tapestry of insights, weaving together the threads of AI technology, human interaction and the appropriation process, research on appropriation of AI technologies is far to be consolidated.
In this work, we aim to investigate which factors impact on the adoption and customization of Generative AI chatbots technology for more effective and efficient customized utilization for work purposes, namely Generative AI Appropriation (CGA). This represents an individual’s ability to tailor generative chatbots in the execution of their work tasks, thereby enhancing their performance. The process encompasses several aspects, including understanding the technology’s functionalities, optimizing performance and integrating it into work routines and business processes. The appropriation of generative chatbots by Innovation Managers enables several advantages. AI systems are adept at analyzing and synthesizing vast amounts of data, thus providing valuable insights and suggestions that might be otherwise overlooked. This capability is crucial for leveraging the technology’s potential in innovation management (Kanbach etal., 2023). Moreover, generative AI tools like ChatGPT play a significant role in improving knowledge sharing within organizations. They act as interfaces to organizational knowledge bases, thereby enhancing communication and information dissemination across different departments (Budhwar etal., 2023). Generative AI has a huge impact on the acceleration of innovative process. By managing routine queries and tasks, these tools free up human resources for more complex and creative tasks. This shift not only enhances overall productivity but also fosters a more innovative work environment (Mariani etal., 2023). Lastly, fostering creativity within the company is an essential advantage of employing generative AI tools. By presenting diverse perspectives and challenging conventional thinking, AI can inspire creativity and innovation (Tredinnick and Laybats, 2023).
In this context, CGA refers to the nuanced process by which innovation managers adapt and integrate generative AI chatbot technologies, into their work routines and organizational processes. This appropriation extends beyond mere usage; it embodies a profound engagement with the technology’s capabilities and limitations, paired with a proactive approach to meld these tools to serve specific innovation oriented organizational needs and objectives. This entails using the chatbot in innovative ways to improve decision-making, creativity and productivity within the context of innovation management. CGA is characterized by Continual Adaptation. It is not a static event but a dynamic process that involves regularly reassessing and readjusting the use of the technology in response to changing organizational needs, technological advancements and the evolving landscape of innovation management. This variable is designated as the effect variable of the model proposed in this study. By defining CGA in this comprehensive manner, the study aims to delve into the factors that impact the adoption and effective customization of generative AI chatbots.
2.2 Research model and hypothesis
2.2.1 Antecedents of CGA
Studies based on technology appropriation theory have considered both organizational and individual factors to explain the ways in which professionals adapt technological tools as they use them. Among the organizational aspects, an important role is played by leadership. The leader is capable of motivating the use of a technological tool and also of shaping new practices (Dennis and Garfield, 2003). In particular, in the case of characterize malleable IT solutions such as GAI, an agile leadership style or agile leadership (AL), have been found able to favor a customized and innovative use of technology (Schmitz etal., 2016; Shao and Li, 2022). As regards individual factors, an important role is played by the cognitive characteristics of the individuals who use the new technology. Individuals' innovation orientation, in particular, pushes them to question the technologies currently in use (van Wijk etal., 2013). In particular, in the case of malleable technologies, IO influences the behavior of users and the way in which they adapt tools to tasks and vice versa (Schmitz etal., 2016). According to Miranda etal. (2015) the vision that people develop of the new technology in use influences how it is actually used. In turn this vision is influenced by cognitive factors such as the user’s Individual Creativity (IC). IC has been found to affect the modes of use of technology in several studies (Kolade and Owoseni, 2022). IC, indeed, is regarded by scholars as a factor affecting the inclination of individuals to explore different modes of use of new tools, thereby influencing the appropriation of innovation (Slåtten etal., 2020).
Based on the above discussion, three factors seem to have a relevant influence on the appropriation of new, malleable technologies by users: agile leadership, innovation orientation and individual creativity. We hypothesize that these three factors are antecedents predicting innovation managers’ appropriation of Generative AI tools, such as ChatGPT. In order to accomplish our research objective, we construct and validate a research model as shown in Figure1.
2.2.2 Hypotheses development
Agile leadership encompasses the capacity to embrace an adaptively flexible approach and utilizes broader perspectives to identify and scrutinize diverse circ*mstances (Akkaya etal., 2022). Agile leaders are endowed with the traits of adaptability and versatility, essential for leading effectively in a myriad of challenging and unpredictable situations (Guzmán etal., 2020). In the realm of innovation management, agility serves as a driver for fostering a more adaptive and responsive front end to the innovation process, aligning with the broader organizational pursuit of agile transformation (Brand etal., 2021).
Agile leadership has been linked to enhanced project performance, with a notable positive effect on leadership competencies over project outcomes, suggesting a conducive environment for adopting innovative IT solutions (Muhammad etal., 2021). Agile leaders have shown increased productivity and effectiveness, signifying a favorable ground for IT appropriation within agile-led environments (Hofman etal., 2023).
The traits of interactivity and flexibility that characterize malleable IT solutions offer much room for alternative use and potential innovation (Schmitz etal., 2016; Shao and Li, 2022). Generative AI is regarded as a malleable IT solution due to its inherent flexibility and adaptability. Generative AI models, such as those based on the GPT architectures, can be fine-tuned or adapted to various tasks and domains, making them versatile tools (Haefner etal., 2023). Agile leaders possess charismatic personalities and usually seek novel solutions in face of difficulties or problems at work (Bass etal., 2003). As such, they are innately motivated to explore new ways of using malleable IT in the attempt to fully exploit its potential according to their business-related purposes (Shao etal., 2017).
The transformation ushered in by AI is reshaping leadership tasks, particularly in fostering constructive interaction between employees and AI technologies (Peifer etal., 2022). Agile leaders, known for their adaptability, openness to learning and responsiveness to changing circ*mstances, are well-positioned to embrace the dynamic capabilities of Generative AI. The appropriation of Generative AI necessitates a flexible and adaptive approach, given the technology’s evolving nature and the need for an iterative learning process to maximize its potential (Bag etal., 2021). Moreover, the visionary and strategic orientation of agile leaders enables a clear articulation of the value proposition of Generative AI, fostering organizational buy-in and facilitating the integration of Generative AI into existing work processes and routines (Akkaya etal., 2022).
Hence, we propose the following hypothesis:
H1.
Agile leadership has a positive impact on Generative AI Appropriation
Innovation Orientation is a widely used concept in innovation management literature (Siguaw etal., 2006).
This concept encapsulates an individual’s propensity toward adopting new ideas, practices or technologies, and is a critical quality in many professional settings, especially those that are rapidly evolving or highly competitive (Aldahdouh etal., 2019). Various research efforts have delved into understanding the nuances of innovation orientation from different perspectives. Scholars refers to similar concepts using the term individual innovativeness (Aldahdouh etal., 2019), actualized innovativeness (Lee etal., 2013) or innovative job performance (De Jong and Den Hartog, 2007). Innovation orientation has been portrayed as a persistent individual characteristic or an underlying personality trait that orchestrates an individual’s inclination to initiate and embrace changes (Yi etal., 2006). In line with this, personal innovativeness is conceptualized as a personality trait that is closely associated with an individual’s openness to taking chances, and is recognized as a pivotal factor in the process of innovation and technology adoption (Setiawan etal., 2021).
Various studies have delved into exploring the relationship between innovation orientation and technology Appropriation. In the realm of information technology, it is observed that individuals with a higher degree of innovation orientation tend to have a positive perception of technological innovations and are more adept at adopting them (Ciftci etal., 2021). Personal Innovativeness in Information Technology underscores an individual’s willingness to experiment with new computer technologies, indicating a direct link between innovativeness and technology appropriation (Paganin and Simbula, 2021). Another study showcased a strong positive relationship between individual innovativeness and e-learning readiness among students, signifying how innovativeness can drive the effective appropriation of e-learning technologies (Bubou and Job, 2022). People with a high degree of innovation orientation have traits characterized by great open-mindedness, strong intrinsic motivation and significant intellectual curiosity. This kind of individuals examined is inclined to embrace new technologies and new possible uses: they are more likely to identify new ways of appropriating generative AI Chatbots discovering new opportunities and applications. As a consequence, the following hypothesis is formulated:
H2.
Innovation Orientation has a positive impact on Generative AI Appropriation
Individual creativity is a concept that has been widely addressed in the Creativity and Innovation Management research domain (Açıkgöz and Günsel, 2016). Individual creativity in organizational settings can be defined as the employee’s capability to generate novel and useful ideas or solutions within their organizational roles, thereby enhancing problem-solving, decision-making and innovation (Amabile, 1988; Zhou and Shalley, 2003). Chen etal., (2011) defined individual creativity as the personal attitude to take an active role to work, going beyond what is required in a given task. Some works refer to individual creativity in managerial contexts as a multifaceted concept intertwining personal attributes and the features of the organizational environment t (e.g. autonomy, challenge and encouragement, challenge) (Sternberg and Lubart, 1996; Faullant etal., 2012). Extant research argues that some creativity-relevant skills can be learned, while other aspects might be rooted in personality traits (Faullant etal., 2012). Managers with a high level of creativity are often characterized by their cognitive flexibility, openness to new experiences and a proclivity for divergent thinking (Sternberg, 2005). Individual creativity is often regarded as a cornerstone for fostering innovation adoption within organizations (Amabile, 1988). The uniqueness and originality inherent in creative thinking contribute to the exploration of new technologies and methods, thereby facilitating the adoption of innovation (Slåtten etal., 2020).
The association between individual creativity and technology appropriation is rooted in several theoretical and empirical studies. Individual creativity, characterized by traits like creative ability, intrinsic motivation and prior knowledge, significantly influences the perception and acceptance of new technologies (Chae etal., 2023). The extent to which individuals can creatively interact with technology profoundly impacts how they adopt and adapt these technologies to meet their needs (Saghafian etal., 2021). Individual openness to new experiences and ability to envision the potential benefits of novel solutions often catalyze the process of innovation appropriation within their organizational milieu (Cui etal., 2023). Creative individuals are more adept at recognizing and exploiting the potential of such technologies. Thanks to their ability to envision novel solutions and approaches, they are likely to find malleable IT particularly conducive to expressing and implementing their ideas (Nguyen etal., 2021). They can mold the technology to better fit their innovative visions, hence, effectively appropriating the technology to serve their unique purposes or solve distinct problems (Schmitz etal., 2016). Hence, we propose the following hypothesis:
H3.
Individual Creativity has a positive impact on Generative AI Appropriation
The variables used in the model are defined in the context of this study as outlined in Figure1.
3. Research methodology
3.1 Research context
In the past decade, AI has undergone a remarkable transformation, evolving from rule-based systems to machine learning and deep learning models (Shao etal., 2022). This evolution has not only empowered organizations to optimize operational tasks but has also laid the foundation for a new era of AI. This era is known as generative artificial intelligence (van der Zant etal., 2013). Generative AI is a notable advancement. It can simulate human-like creativity and ideation by leveraging machine learning on massive databases experiences (Bandi etal., 2023). This transformation of AI has not gone unnoticed by investors and innovators. In this regard, funding for generative AI has been substantial and is experiencing rapid growth, with investments reaching a total of $12bn in just the first five months of 2023 (Chui etal., 2023). Venture capital and other private external investments in generative AI have experienced an impressive annual compound growth rate of 74% between 2017 and 2022. Furthermore, estimates suggest that the impact of generative AI on productivity has the potential to contribute trillions of dollars in value to the global economy (Chui etal., 2023). Generative AI finds diverse applications across industries, with a particularly significant role within innovation management (Füller etal., 2022). Innovation management, definitely essential for driving innovation performance, has reached noteworthy progress, especially in the Italian context where this research is conducted. Italy’s Innovation Index, as calculated by European Innovation Scoreboard (EIS) in 2023, has experienced since 2016 a remarkable 19% increase, rising from 100 to 119. This positive trend is not confined to Italy alone; Europe as a whole has displayed a growing commitment to innovation. The EU’s innovation performance, as reported in the EIS 2023 – Executive Summary report, has surged by 8.5% points within the same timeframe, overtaking the progress of five global competitors (Australia, India, Japan, Mexico and South Africa). Europe and Italy’s substantial investments in innovation underscore the potential for generative AI to further enhance innovation generation, with innovation managers serving as the catalysts driving this transformation. This research focuses on the intersection of generative AI and innovation management. The primary goal is to delve into the complex dynamics involved in how innovation managers utilize generative AI tools, like ChatGPT. The study aims to understand how these tools are appropriated to efficiently guide and implement innovation processes, thereby contributing to the ongoing innovation evolution. To test our hypotheses empirically a survey has been conducted from March 2023 to July 2023 by using, as primary data collection tool, a custom-developed questionnaire. The questionnaire was divided into two sections, each designed with a specific purpose. The initial section was dedicated to gather general and demographic information of the innovation managers participating in the study. This information includes email addresses, gender, age, educational level, management level and job experience. Additionally, within this section, a specific question determines whether the interviewed innovation managers were aware of ChatGPT. The rationale behind this was to proceed with the questionnaire only for those who were aware of it, ensuring relevance and meaningful responses. The second section of the questionnaire was structured to explore the constructs outlined in the proposed research conceptual model, as illustrated in Figure1. Atotal of 20 questions has been defined (see Appendix) and respondents were provided with Likert scales, ranging from “strongly agree” to “strongly disagree” on a scale of 1–5, allowing them to express their viewpoints concerning the constructs in question.
3.2 Data collection
Prior to the official launch of the survey, the authors conducted a rigorous testing phase to evaluate the questionnaire’s effectiveness and clarity. Copies of the questionnaire were distributed to professors’ expert in the field of innovation management at the University of Messina, University of Salento and University of Calabria in Italy. Their valuable feedback was considered and incorporated to enhance the questionnaire’s effectiveness. Following this expert review, the questionnaire was further tested with a selected small group of innovation managers. This testing phase was carried out in person, with one of the authors available to assist respondents as they completed the survey. This approach allowed for direct observation of how participants interpreted the questions and concepts. The insights gained from these pilot interviews were useful for refining the formulation of the questionnaire. Subsequently, the finalized questionnaire was distributed to a sample of 870 Italian innovation managers, covering a diverse geographical range across the country. The survey was administered online through Google Forms and was presented in English. Completing the questionnaire required approximately 10min of respondents' time. The response rate for the survey was approximately 28%, resulting in a total of 244 completed responses. Toensure data quality, the authors meticulously reviewed the responses, removing any repetitive or incoherent entries. This rigorous cleaning process yielded 222 valid questionnaires, forming the base for the research analysis.
3.3 Data analysis
We tested the hypotheses empirically using partial least squares structural equation modeling (PLS-SEM). PLS-SEM was chosen as a statistical method (Wold, 1975, 1982; Lohmöller, 2013; Bentler and Huang, 2014; Dijkstra, 2014; Dijkstra and Henseler, 2015). This choice was driven by its proven suitability for situations involving small sample sizes (Willaby etal., 2015) and its effectiveness in exploratory research studies (Hair etal., 2019). Moreover, the sample size for the PLS-SEM application in this study consists of 222 valid questionnaires. This adheres to the recommended guideline of being at least ten times larger than the maximum number of arrowheads pointing at the latent variable (CGA) in the model (Hair etal., 2021). SmartPLS4 software, provided by SmartPLS GmbH, has been used to perform the PLS-SEM analysis efficiently and effectively. Further details and information about SmartPLS4 can be accessed at https://www.smartpls.com.
4. Results
4.1 Descriptive statistics
The demographical data of the interviewed participants are presented in Table1. The sample consists of 222 innovation managers, with a predominant gender composition of 97.7% male and 2.3% female. Regarding age groups, the majority of respondents fall into the >50years category, accounting for 55.9%, followed by 46–50years (16.7%) and 41–45years (13%). Interms of educational level, a substantial portion of the sample holds a Master’s Degree (45.5%), while 23% has a Higher Degree. When it comes to management roles, the majority of participants were top-level managers (66.7%), with middle-level managers comprising 20.7% and front-line managers making up 12.6% of the sample. Finally, in assessing work experience, a significant 69.8% of respondents reported having more than 20years of experience, while the remaining participants were distributed across various experience levels.
4.2 Measurement model results
The evaluation of the measurement model, including the assessments of indicator reliability, internal consistency and reliability, as well as convergent validity, is reported in Table2. Theassessment of indicator reliability, conducted by analyzing the indicator loadings, demonstrates that all values are higher than the recommended threshold of 0.708 (Hairetal., 2021). No items needed to be dropped due to low indicator loadings. All constructs under examination shows that Composite Reliability (CR) and Cronbach’s alpha values falls within the range of 0.7–0.9, indicating that the internal consistency and reliability criteria are met (Hair etal., 2021). Finally, to assess convergent validity, the authors use the Average Variance Extracted (AVE) metric. All constructs exhibit AVE values surpassing the threshold limit of 0.5 (Hair etal., 2022), confirming the model’s validity.
Moving forward to the assessment of the model discriminant validity, the authors employee three different metrics: Cross-loadings (Hair etal., 2017), Fornell-Larcker (Fornell and Larcker, 1981) and heterotrait-monotrait ratio (HTMT) (Henseler etal., 2015). Tables 3–5 present the results for each metric, confirming the positive outcomes regarding the model’s discriminant validity. The cross-loadings values for each item related to its construct are higher than the values for that item related to other constructs (Hair etal., 2017). Furthermore, in alignment with the Fornell–Larcker metric, the AVE for each construct is greater than the highest correlation that particular construct has with any other construct in the model (Fornell and Larcker, 1981; Hair etal., 2021). Additionally, all HTMT values fall below the established threshold of 0.9 (Henseler etal., 2015; Hair etal., 2021).
4.3 Structural model results
After assessing the measurement model, the structural model has been assessed. As initial step, the authors examine the presence of potential collinearity issues by calculating the Variance Inflation Factor (VIF). Table6 shows VIF values, confirming none surpasses the threshold limit of 5. This remarks the absence of collinearity issues in the structural model (Hair etal., 2011). Subsequently, the authors turn their attention to the structural model path analysis, the results of which are summarized in Table7. The findings reveal positive impacts on ChatGPT appropriation by Innovation managers for all the independent constructs, thus confirming H1 (AL), H2 (IO) and H3 (IC). These impacts show a relatively balanced distribution, with corresponding values of 0.135 (AL), 0.153 (IC) and 0.167 (IO). To assess the statistical significance of the path model, the authors utilize bootstrapping standard errors for calculating the t-values of path coefficients. These computed values, exceeding the threshold of 1.96, clearly indicate the statistical significance of the indicator weights, assuming a significance level of 5% (Hair etal., 2021). Last but not the least, the authors chose to employ confidence intervals as an alternative method for evaluating the significance of the path coefficients. As illustrated in Table8, the confidence interval values offer further confirmation, as none of them include the value zero (Hair etal., 2021).
5. Discussion
The aim of our study was to analyze the impact of contextual and individual variables on the level of appropriation of Generative Artificial Intelligence tools and in particular of ChatGPT, by Innovation Managers. Innovation managers play a key role in innovation processes and Generative Artificial Intelligence tools, if used profitably, promise to help improve their performance.
The appropriation of a technology leads a subject to adapt it to his own task and organizational context, exploiting some functions, customizing others or even inventing new ones (Nguyen etal., 2021). Among the context variables that can influence the appropriation of technology, some can be traced back to the role of the leader and among these an agile leadership approach (Akkaya etal., 2022; Guzmán etal., 2020). On an individual level past research has highlighted the relevance, for technology appropriation, of the individual’s orientation toward innovation (Siguaw etal., 2006; Aldahdouh etal., 2019) as well as of individual creativity (Slåtten etal., 2020). Our study therefore investigated the impact of these three variables – Agile Leadership, Innovation Orientation and Individual Creativity – on Innovation Managers' appropriation of ChatGPT.
The hypotheses were tested on a sample of Italian innovation managers. The results obtained confirm all our hypotheses. The three independent variables considered all have a statistically significant impact on the appropriation of ChatGPT by innovation managers. The strength of the causal links is balanced, that is, AL, IO and IC exert a similar influence on the variable “appropriation.”
The results of our study are part of the line of studies that investigates the relationship between technology and organization (e.g. DeSanctis and Poole, 1994; Orlikowski and Iacono, 2001; Jones and Karsten, 2008). In the wake of studies inspired by the socio-technical approach (Suchman, 1987), our work confirms that the use of technology is not independent of the context in which it is used, nor of the characteristics of the individual who uses it. On the contrary, the characteristics of the organizational context and of the individual induce the personalization of the use of technologies. Going further along this path, our study adopts and confirms the assumptions of the AST and in particular of the theory of appropriation (DeSanctis and Poole, 1994), confirming that the interaction between organization, individual and technology leads to the personalization of the use of technological tools and even to the development of functions not originally foreseen.
More specifically, one contextual variable was considered in this study: Agile Leadership. AL has often been considered as a factor capable of influencing organizational performance (Guzmán etal., 2020) and the processes of adopting new technologies (Fachrunnisa etal., 2020). Our study extends in this last line of research, investigating the impact of AL on the use of technologies at an individual level. Using the framework of appropriation theory, in fact, our study indicates how agile leadership serves as a catalyst, not only for technology adoption but, crucially, for driving personalization in the utilization of technologies by innovation managers. This exploration adds depth to the existing literature and enhances our understanding of the role leadership plays in shaping technological appropriation.
Our results also confirm that appropriation is influenced by the characteristics of the individual using the technology. In line with the findings of prior research on appropriation of ICT, we found that individuals with higher IO, not only tend to adopt new ChatGPT more easily, but also show higher levels of technology appropriation (Ciftci etal., 2021; Paganin and Simbula, 2021). A similar reasoning can be made for IC, which, in particular by supporting the identification of new functionalities promotes personalization in the use of ICT and Generative Artificial Intelligence.
In our study Generative AI is regarded as a case of malleable IT solution (Schmitz etal., 2016; Shao and Li, 2022). Indeed, Generative AI tools, such as GPT-based models, are flexible and adaptable, allowing for various applications. Creative, innovation-oriented individuals, supported by agile leaders, are well-suited to harness the capabilities of Generative AI. As in the case of other malleable IT solutions (Shao etal., 2017), they can explore innovative uses of this technology to maximize its potential for business purposes.
Building upon these insights, our study goes beyond a mere examination of factors influencing Generative AI appropriation. We provide an analysis that extends from organizational contexts to individual levels, offering insights into how innovation managers personalize technology use. The richness of our findings contributes not only to the broader discourse on the relationship between technology and organization but also presents practical implications for enhancing innovation-oriented mindsets and leveraging individual creativity in the adoption and customization of Generative AI. As such, our research stands as a valuable resource for practitioners, scholars and organizations seeking to optimize the integration of Generative AI tools in innovation processes, and it contributes substantively to the evolving landscape of technology and innovation research.
6. Implications
6.1 Theoretical implications
Our research contributes significantly to the theoretical understanding of the mechanisms supporting innovation managers in adopting and effectively utilizing generative AI to enhance both innovation processes and organizational decision-making.
First and foremost, our study adds to the growing body of literature on generative AI in organizational decision-making processes by emphasizing the pivotal role of user adaptability in effectively utilizing emerging technologies.
The research model tested in this paper underscores the importance of fostering users' abilities and capabilities necessary for the successful adoption of generative AI tools. Therefore, it is crucial, before implementing generative AI technology to improve decision-making processes, to emphasize the development of users' capabilities, such as innovation orientation and individual creativity, including agile leadership, as fundamental attributes for shaping the appropriation process.
Moreover, through empirical evidence, our study addresses the underexplored area of understanding the mechanisms that impact the adoption and efficient use of generative AI tools, which are crucial for enhancing the decision-making process (Dwivedi etal., 2021). This contribution aids in developing a more comprehensive framework for understanding IT adoption in general (Verdegem and De Marez, 2011).
Additionally, we extend the conceptualization of the interplay between technology and the social environment in which it operates. Our study introduces and empirically supports the interaction between technological tools like ChatGPT and human factors, including agile leadership, innovation orientation and individual creativity, as pivotal in shaping the appropriation process. This element enhances our understanding of human attitudes toward using generative AI for efficient decision-making processes. Moreover, the inclusion of variables such as agile leadership, innovation orientation and individual creativity enriches our comprehension of how human factors influence the appropriation of advanced AI tools.
6.2 Practical implications
Our study also offers valuable practical managerial insights.
Firstly, for organizations seeking to maximize the benefits of tools like ChatGPT in decision-making processes, it is essential to create conducive conditions and cultivate an environment that encourages agile leadership, nurtures innovation orientation and supports individual creativity.
This implies that organizations should establish effective mechanisms and ensure adequate training and support (Cao etal., 2021) for innovation managers to proficiently work with generative AI tools. Training programs can be designed to refine the necessary attributes among innovation managers.
In summary, our article clarifies the factors that push innovation managers to personalize the use of technology. Our study contributes to the literature on innovation diffusion and technology management. Compared to the existing literature, ours is one of the few studies that adopts a quantitative approach to the analysis of the antecedents of the “technology appropriation” variable. The latter is seen as the degree to which the user adapts a tool to their context of use. This ability to adapt technology to the specific context is seen as a positive phenomenon, as it potentially allows the technology itself to be better exploited. A further element of originality is the application context of the study. Not only do we consider a key figure in innovation processes, that of innovation managers, but we also study a malleable technology potentially with an enormous impact on people’s work, namely generative artificial intelligence.
7. Conclusions
Our study offers valuable insights into the mechanisms governing the adoption and customization of generative AI tools within the realm of innovation management. A deeper understanding of these factors can serve as a guiding framework for organizations seeking to effectively integrate such technologies, thereby enhancing innovation outcomes and decision-making processes. The positive correlation identified between Innovation Orientation (IO) and the utilization of ChatGPT underscores the significance of cultivating innovation-oriented mindsets among innovation managers. Conversely, Individual Creativity (IC) emerged as a pivotal factor influencing the adoption and customization of Generative AI. In conclusion, our study emphasizes that organizations looking to harness the potential of generative AI tools for decision-making and innovation should prioritize the development of innovation-centric cultures and the nurturing of individual creativity among their innovation managers. Additionally, they should be prepared to offer training and support to enhance adaptability to new technology.
Limitations and Future Research: While our research provides valuable insights, it is not without limitations. The study focused on Italian innovation managers, which may limit its generalizability to other cultural or organizational contexts. Future research could explore similar dynamics in different cultural settings or across diverse industries. Moreover, the dynamic nature of technology means that as AI tools evolve, the factors influencing their appropriation might also shift. Continuous research in this area will be essential to stay abreast of these changes. In conclusion, this research sheds light on the intricate relationship between human factors and technology appropriation, emphasizing the need for a holistic approach when considering the implementation and use of advanced AI tools in organizational settings.
Furthermore, our research paves the way for future investigations that can investigate deeper into the intricate relationship between leadership styles, innovation orientation, individual creativity and technology adoption. Subsequent studies can also explore additional contextual factors that may exert influence on the utilization of generative AI tools in the domain of innovation management.
Figures
Figure1
The research model
Innovation managers demographical data
| Demographic character | Frequency(n) | Percentage (%) | |
|---|---|---|---|
| Gender | Male | 217 | 97.7 |
| Female | 5 | 2.3 | |
| Age | <21years | 0 | 0.0 |
| 21–25years | 2 | 0.9 | |
| 26–30years | 1 | 0.4 | |
| 31–35years | 9 | 4.1 | |
| 36–40years | 20 | 9.0 | |
| 41–45years | 29 | 13.0 | |
| 46–50years | 37 | 16.7 | |
| >50years | 124 | 55.9 | |
| Educational level | High school | 25 | 11.2 |
| Bachelor’s degree | 38 | 17.1 | |
| Master’s degree | 101 | 45.5 | |
| Vocational degree | 7 | 3.2 | |
| Higher degree (PhD, etc.) | 51 | 23.0 | |
| Management level | Frontline managers | 28 | 12.6 |
| Middle-level managers | 46 | 20.7 | |
| Top-level managers | 148 | 66.7 | |
| Work experience | <1year | 0 | 0.0 |
| 1–5years | 5 | 2.3 | |
| 6–10years | 10 | 4.5 | |
| 11–15years | 24 | 10.8 | |
| 16–20years | 28 | 12.6 | |
| >20years | 155 | 69.8 | |
Source(s): Authors' own creation
Reliability, consistency and validity measures
| Construct | Items # | Indicators loadings | CR | Cronbach’s alpha | AVE |
|---|---|---|---|---|---|
| AL | 4 | 0.722–0.821 | 0.884 | 0.850 | 0.684 |
| IO | 6 | 0.710–0.792 | 0.851 | 0.846 | 0.562 |
| IC | 5 | 0.736–0.788 | 0.879 | 0.852 | 0.621 |
| CGA | 5 | 0.718–0.767 | 0.829 | 0.820 | 0.578 |
Source(s): Authors' own creation
Cross-loadings
| AL | IO | IC | CGA | |
|---|---|---|---|---|
| AL-1 | 0.794 | 0.280 | 0.347 | 0.143 |
| AL-2 | 0.854 | 0.298 | 0.275 | 0.252 |
| AL-3 | 0.846 | 0.223 | 0.243 | 0.217 |
| AL-4 | 0.814 | 0.332 | 0.304 | 0.162 |
| IO-1 | 0.256 | 0.714 | 0.402 | 0.233 |
| IO-2 | 0.296 | 0.724 | 0.387 | 0.273 |
| IO-3 | 0.211 | 0.792 | 0.564 | 0.253 |
| IO-4 | 0.302 | 0.765 | 0.595 | 0.192 |
| IO-5 | 0.239 | 0.731 | 0.481 | 0.156 |
| IO-6 | 0.218 | 0.771 | 0.480 | 0.251 |
| IC-1 | 0.281 | 0.449 | 0.746 | 0.324 |
| IC-2 | 0.283 | 0.563 | 0.826 | 0.243 |
| IC-3 | 0.297 | 0.548 | 0.818 | 0.234 |
| IC-4 | 0.203 | 0.388 | 0.744 | 0.147 |
| IC-5 | 0.249 | 0.557 | 0.802 | 0.175 |
| CGA-1 | 0.247 | 0.281 | 0.260 | 0.775 |
| CGA-2 | 0.215 | 0.225 | 0.200 | 0.739 |
| CGA-3 | 0.131 | 0.260 | 0.303 | 0.773 |
| CGA-4 | 0.183 | 0.236 | 0.197 | 0.774 |
| CGA-5 | 0.130 | 0.145 | 0.176 | 0.741 |
Source(s): Authors' own creation
Fornell-Larcker
| AL | IO | IC | CGA | |
|---|---|---|---|---|
| AL | 0.827 | |||
| IO | 0.338 | 0.750 | ||
| IC | 0.343 | 0.640 | 0.788 | |
| CGA | 0.244 | 0.311 | 0.307 | 0.761 |
Source(s): Authors' own creation
Heterotrait-Monotrait ratio (HTMT)
| AL | IO | IC | CGA | |
|---|---|---|---|---|
| AL | ||||
| IO | 0.403 | |||
| IC | 0.399 | 0.751 | ||
| CGA | 0.272 | 0.348 | 0.328 |
Source(s): Authors' own creation
Variance inflation factors (VIFs)
| VIF | |
|---|---|
| AL → CGA | 1.164 |
| IO → CGA | 1.739 |
| IC → CGA | 1.746 |
Source(s): Authors' own creation
Path coefficients – mean, STDEV, t-values, p-values
| Original sample (O) | Sample mean (M) | Standard deviation (STDEV) | T-statistics (|O/STDEV|) | p-values | Result | |
|---|---|---|---|---|---|---|
| AL →CGA(H1) | 0.135 | 0.142 | 0.065 | 2.085 | 0.037 | Supported |
| IO → CGA (H2) | 0.167 | 0.178 | 0.075 | 2.217 | 0.027 | Supported |
| IC → CGA (H3) | 0.153 | 0.162 | 0.076 | 2.025 | 0.043 | Supported |
Source(s): Authors' own creation
Path coefficients – confidence intervals
| Original sample (O) | Sample mean (M) | 2.5% | 97.5% | |
|---|---|---|---|---|
| AL → CGA | 0.135 | 0.142 | 0.016 | 0.267 |
| IO → CGA | 0.167 | 0.178 | 0.027 | 0.322 |
| IC → CGA | 0.153 | 0.162 | 0.015 | 0.314 |
Source(s): Authors' own creation
Research model constructs and items
| Construct | Item | Nr |
|---|---|---|
| Agile leadership (AL) | My leader has an agile mindset | AL-1 |
| My leader depicts a sense of urgency where change is concerned | AL-2 | |
| My leader readily accepts new ideas | AL-3 | |
| My leader focuses on faster outcomes through technological assistance | AL-4 | |
| Innovation orientation (IO) | I consider myself to be creative in my thinking | IO-1 |
| I seek out new ways to do things | IO-2 | |
| I am creative in my methods of operation | IO-3 | |
| I have the requisite technical design skills for developing the latest in my field | IO-4 | |
| I keep researching industry trends to stay ahead of the curve | IO-5 | |
| I am the first one who brings new ideas towards product and services | IO-6 | |
| Individual creativity (IC) | I suggest new ways to achieve goals or objectives | IC-1 |
| I suggest new ways to increase quality | IC-2 | |
| I am a good source of creative ideas | IC-3 | |
| I often have a fresh approach to problems | IC-4 | |
| I suggest new ways of performing work | IC-5 | |
| ChatGPT appropriation (CGA) | I feel confident that ChatGPT aligns with our organization’s goals | CGA-1 |
| I’m willing to use ChatGPT functions | CGA-2 | |
| I can validate my thought process through ChatGPT | CGA-3 | |
| I find ChatGPT a good tool for my work | CGA-4 | |
| I can’t wait to invent new uses of ChatGPT | CGA-5 |
Appendix
TableA1
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Corresponding author
Valentina Ndou is the corresponding author and can be contacted at: valentina.ndou@unisalento.it
About the authors
Antonio Cimino is an assistant professor at the Department of Innovation at the University of Salento, Italy. He received his master’s in Management Engineering Summa Cum Laude and his PhD in Mechanical Engineering at University of Calabria. His research interests are mainly devoted to supply chain design and management, products innovation, resilience management, Industry 4.0 and smart manufacturing, production processes design and optimization. He participated to several international conferences as speaker. He is member of the International Program Committee of the International Conference on Industry 4.0 and Smart Manufacturing (ISM). He works as a reviewer for several international journals.
Alberto Michele Felicetti is Tenure-track assistant professor at the Department of Mechanical, Energy and Management Engineering of the University of Calabria – Italy and visiting senior researcher at Tampere University of Technology – Finland. His main research activities deal with collaborative networks, digitalization and innovation management. He is member of the Editorial Advisory Board di European Journal of Innovation Management. He acted as Guest Editor for several Special Issues in peer-reviewed journals (Measuring Business Excellence, Sustainability, British Food Journal) on the themes of innovation, digitalization and sustainability. He published over 40 papers in peer-reviewed journals and conference proceedings.
Vincenzo Corvello, PhD, is associate professor at the Department of Engineering, University of Messina. He holds a PhD in Business and Economic Engineering from the University Federico II of Naples. He teaches innovation management, strategy and organization and project management. His research interests are in the fields innovation management and organizational theory. He published papers in international journals and chapters in international books. He is co-founder and has been CEO of Beautiful Mind S.r.l. His research has been supported by national and international research grants. He is Editor-in-Chief of the European Journal of Innovation Management.
Valentina Ndou is Certified Associate Professor at the Department of Engineering for Innovation – University of Salento (Lecce, Italy). She teaches project management, technology entrepreneurship, business innovation. She has authored more than 50 papers published on leading international journals such as Technology Forecasting and Social Change, Management Science, Current Issues in Tourism. She is co-author and co-editor of different books. Her research interests are on entrepreneurship, digital transformation tourism innovation and project management. Her research has been supported by European and national research grants.
Francesco Longo is currently Associate Professor at the Mechanical Department of University of Calabria and Director of the Modeling and Simulation Center – Laboratory of Enterprise Solutions (MSC-LES). His research interests include virtual/augmented/mixed reality, AI and digital twin applications in Industry 4.0, smart operators within manufacturing systems and industry, modeling and simulation for production systems and supply chains design and management. He has served as Principal Investigator and Scientific Responsible for many research projects and as General/Program chair in a number of International Conferences (e.g. I3M, ISM, etc.). He has published more than 200 articles on international journals and conferences.
