• No results found

The Influence of AI Capabilities on Employee Performance through Employee Engagement


Academic year: 2023

Share "The Influence of AI Capabilities on Employee Performance through Employee Engagement"


Hele tekst


Employee Performance through Employee Engagement


AI, is it a Culture Threat or a Culture Opportunity?


The Influence of AI Capabilities on Employee Performance through

Employee Engagement


AI, is it a Culture Threat or a Culture Opportunity?

Britt Hermans

Student number: 13491466 30th of June 2022 final version

Executive Programme in Management Studies - Digital Business Track University of Amsterdam / Amsterdam Business School

EBEC approval: EC 20220401030406 Supervisor: dr. H. Güngör


Statement of originality

This document is written by Britt Hermans, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

Date: Signature:


The submission of this Master's Thesis marks the end of my educational journey at the University of Amsterdam. This journey has resulted in exponential academic and personal growth, particularly during the last six months. As I would not be here without the assistance and support of some people, I would like to take this opportunity to thank them. First, I want to express my gratitude to dr. H. Güngör, my university coach, for his advice and criticism during the development of this thesis. Our sessions gave me insightful information that significantly raised the overall quality of my thesis. Along with this, he was adaptable in arranging time for consultations and was easily accessible when required. This was highly appreciated. Additionally, I want to express my gratitude towards PwC Netherlands for giving me the opportunity to write my thesis. I would like to thank my company coach, Yousra Boutkabout, in particular, for her ongoing support and input during this process. She made time in her hectic schedule for our feedback sessions and was always accessible to answer questions. Additionally, I want to thank my colleagues for piloting my survey. Furthermore, I want to thank everyone who participated in my survey for their time and effort in helping me out. These findings were critical in completing this research. Last but certainly not least, I would like to thank my family and friends for their endless encouragement and support. Thank you for dealing with my complaining, breakdowns, and insecurities. Without you, I genuinely wouldn't be where I am now.

Thank you, Britt Hermans


Artificial Intelligence has attracted the attention of organizations and academics due to its impact on the organization and competitive advantage. The aim of this quantitative study was to investigate the influence of AI capabilities (tangible, human, and intangible resources) on employee performance through employee engagement. A survey method was used to collect data from 212 respondents who are working with AI. Statistical analysis of the questionnaire outcome provides insights into the relation between AI capabilities, employee performance, and employee engagement. This research revealed that, on average, employee engagement positively mediates the influence of AI capabilities on employee performance. As a result, employees who work in a company with advanced AI capabilities are more likely to perform well as they are more engaged with their work. Firstly, it was determined that higher levels of AI capabilities lead to higher employee engagement. In addition, employee engagement is positively associated with employee performance. It was also found that higher levels of AI capabilities lead to higher employee performance. Lastly, the AI relationship (indirect or direct) was included as a possible moderator for the indirect effect of AI capabilities on employee performance through employee engagement, but no significant relationship was found. The results of this study will provide professionals with a better understanding of the impact of AI. Managers should not only pay attention to the profitability of AI but also to the (positive) experience of employees working in a company as a result of AI. Using AI to increase employee engagement and overall levels of positivity will enable companies to increase productivity and decrease turnover as an effect. Awareness of this cultural opportunity should be encouraged.

Keywords: Artificial Intelligence, Employee Engagement, Employee Performance


Statement of originality... 3

Acknowledgements ... 4

Abstract ... 5

Table of contents ... 6

List of figures ... 7

List of tables ... 7

1. Introduction ... 9

2. Literature review ... 12

2.1 An introduction to AI ... 12

2.2 AI capabilities ... 14

2.2.1 Tangible resources ... 15

2.2.2 Human resources ... 16

2.2.3 Intangible resources ... 17

2.3 Employee engagement ... 19

2.4 Employee performance ... 22

2.5 Conceptual model ... 26

3. Data and methodology ... 27

3.1 Research design ... 27

3.2 Sample context ... 27

3.3 Instrumentation and questionnaire design ... 28

3.4 Data collection and preliminary checking ... 29

3.5 Data analyses and hypothesis testing ... 30

4. Results ... 33

4.1 Demographic profiles of the respondents... 33

4.2 Normality test ... 35

4.3 Validity and reliability test ... 36

4.4 Descriptive results ... 37

4.5 Correlation test ... 40

4.6 Hypothesis testing ... 42

4.6.1 Mediation ... 42

4.6.2 Moderated mediation ... 45


5.1 Reflection on hypothesis ... 48

5.1.1 AI capabilities positively associated with employee engagement... 48

5.1.2 Employee engagement positively associated with employee performance ... 50

5.1.3 AI capabilities positively associated with employee performance ... 50

5.1.4 The mediating effect of employee engagement ... 51

5.1.5 The moderating effect of AI relation ... 52

5.2 Theoretical implications ... 53

5.3 Practical implications ... 53

6. Conclusion ... 55

6.1 Overall conclusion... 55

6.2 Limitations ... 56

6.3 Recommendations for future research... 57

References ... 58

Appendices ... 62

Appendix I – Questionnaires and sources ... 62

List of figures Figure 1 AI capability and categorization of resources (Mikalef & Gupta, 2021) ... 15

Figure 2 Conceptual model for the influence of AI capabilities on employee performance through employee engagement ... 26

Figure 3 Conceptual model with results of regression coefficients paths. N=212. (Note standardize regression coefficients are reported) ... 42

List of tables Table 1 Demographic Characteristics of the sample (N=212) ... 34

Table 2 Normality testing ... 35

Table 3 KMO and Bartlett's Test ... 36

Table 4 Reliability testing ... 37

Table 5 Overall average scores ... 37


Table 8 Means, Standard deviations, Correlations ... 41 Table 9 Regression analysis of AI capabilities, employee engagement, and employee

performance (H1, H2 and H3) ... 43 Table 10 Total, direct and indirect mediation effect of employee performance of AI capabilities on employee performance (H4) ... 43 Table 11 The moderated mediation effect of the AI relation (H5) ... 46 Table 12 Summarized results ... 47



1. Introduction

In today’s increasingly competitive environment, businesses must be innovative in order to maintain a competitive advantage. Globalization and internationalization of markets, innovation, product or service quality, and customers’ requirements have led companies to integrate IT into their managerial approach (Rachinger et al., 2018). Artificial Intelligence (AI) is still one of the most outstanding IT applications today, a technology that has progressed at an unrivaled rate over the last few decades (Wiljer & Hakim, 2019). Recent years have seen a reemergence of interest in AI among both managers and academics. Furthermore, driven by technological advances and public interest, AI is considered by some as an unprecedented revolutionary technology with the potential to transform humanity (Brock & Wangenheim, 2019). McCarthy invented the term

"Artificial Intelligence" in 1955, describing it as "the science and engineering of making intelligent machines" (McCarthy, 2007). Since then, AI has gone through moments of great achievement and times of misguided hope (Borges et al., 2021). Regarding AI in business, it’s not a single technology but a set of technologies. In this respect, AI affects a wide range of industries and many business areas (Chui, 2017). Furthermore, AI raises new challenges for employees, who are now both managed and held accountable by AI. Leading firms emphasize that organizations need a unique mix of physical, human, and organizational resources to create an AI capability that can add value by distinguishing itself from those of their competitors. The key types of resources are tangible, human skills, and intangible resources to effectively leverage investments that affect firm performance (Mikalef & Gupta, 2021).

However, getting value from AI investments is more complex than expected because of the paradox that employees whose tasks are taken over by AI applications have a negative or positive attitude toward AI, depending on the specific situation (Borges et al., 2021). Every CEO



is concerned about how culture will make or break the deployment of AI in the company, but few realize that, conversely, AI can also transform organizational culture (Candelon et al., 2021).

According to Nilson (2005), AI may help firms improve their efficiency. The benefits of AI, on the other hand, according to Ransbotham et al. (2021), go far beyond enhanced efficiency and decision-making. AI may also help organizations become more effective as well as enhance teams and corporate cultures. Employee engagement is part of this culture and is according Hughes et al.

(2019) essential to the health and performance of an organization. Moreover, according to Katia Walsh (chief global strategy and AI officer at Levi Strauss & Co.), AI encourages employee engagement and innovation (Kiron et al., 2022).

The literature has already addressed the interface between employees and AI technologies, indicating issues about AI use in organizational contexts due to the potential deep change in workforce and subsequent job loss, as well as a lack of confidence in AI decisions, recommendations, and responses. It has been researched that AI adoption can be influenced by human resources competence, emotions, behaviors, and motivations. However, the strategic use of AI technologies for employee engagement has not been well exploited yet (Borges et al., 2021).

That corporate culture affects AI implementations is clear, but what is not yet in focus is that AI implementations also affect corporate culture (Ransbotham et al., 2021). It is critical to understand how AI capabilities (tangible, human, and intangible resources) and employee engagement interact with one another for successful performance. Therefore, the research question is addressed as follows:

How do AI capabilities influence employee performance through employee engagement?



This study applies an evaluation approach which seeks to explain the interaction and influence of three specific AI capabilities (tangibly, human, and intangibly resources) on employee performance through the mediating effect of employee engagement. There is a paucity of literature on employee engagement with AI-driven technological change. Moreover, no other research has been published regarding the combination of AI capabilities, employee engagement, and employee performance. Additionally, the relevance of this research goes beyond science; it will provide managerial guidance on the relationships among AI capabilities, employee engagement, and employee performance. This is critical to increasing AI’s value in an organization and encouraging leaders to learn how they can inspire workers to learn about and adopt new technologies.

This first chapter establishes the study topic's relevance and awareness, and it acts as a basis for the subsequent chapters. The chapters that follow are organized as follows. The second chapter is devoted to the literature review, in which the thesis' key constructs will be reviewed and prior literature on the study subject will be critically analyzed. This is done to identify the research gap and, as a result, to develop the conceptual framework for the thesis. The research strategy and data collection methods employed in this study will be discussed in the third chapter. In the fourth chapter, the study findings will be presented. The fifth chapter will situate these findings in the perspective of previous research and discuss the thesis' theoretical and practical contributions.

Finally, this research will be completed with a conclusion chapter in which the major research question will be addressed, as well as the study's limitations and recommendations for future research.



2. Literature review

This literature review provides a detailed (theoretical) background for this study on how AI capabilities influence employee performance through employee engagement. This chapter starts with an introduction to AI and then presents the three core elements of this study and their hypotheses: AI capabilities, employee engagement, and employee performance. Finally, a conceptual model is shown, incorporating all the elements that form the basis of the methodology.

2.1 An introduction to AI

Recent years have seen a reemergence of interest in AI among both managers and academics.

Driven by technological advances and public interest, AI is considered by some as an unprecedented revolutionary technology with the potential to transform humanity (Brock &

Wangenheim, 2019). The term "Artificial Intelligence" was introduced by John McCarthy in 1955.

He described AI as a creation that combines science and engineering to create technical products such as machines to interact with human intelligence (McCarthy, 2007). As in a more recent interpretation, AI is the replication of human analytical and/or decision-making processes (Finlay, 2021). AI includes techniques such as machine learning, natural language processing, knowledge representation, and computational intelligence (Accenture, 2019). In addition, AI in business is usually defined as computer systems that can perceive the digital or physical world, process what they perceive, and, in most cases, take the kind of action that would normally require human intelligence. For business, there are great benefits to machines that can see, hear, or sense, draw conclusions, and then make smart choices and act on them. Most business applications fall into three categories: The first category is making it easier for people to access important information.

The second category is providing insights to support faster and more informed decisions. The last



category automates increasingly complex activities. A combination of the categories is also possible (PwC, 2021). Automation is one of the major advantages of AI technology, and it has had a major impact on the communications, transportation, consumer goods, and service industries. In a variety of industries, automation allows for higher production rates and efficiency, as well as more efficient raw material consumption, improved product quality, and increased safety. AI has also long been used to assist organizations in making better decisions. AI technology may help companies manage data supply, analyze trends, ensure data consistency, and make accurate forecasts. Furthermore, AI assists in the creation of predictive models and algorithms for data processing and forecasting the outcomes of various trends and scenarios. AI is supporting businesses from a variety of industries in discovering the best solutions to their problems. As a result, higher efficiency in handling difficult problems translates to increased organizational productivity. AI technology not only supports businesses in making critical decisions, but also prepares them for any scenario, assuring their long-term sustainability (Ramachandran et al., 2022). To summarize the above, the AI definition is as follows:

“AI is a system’s ability to continuously learn from and solve new problems in an ever-changing environment, based on its continuing collection of data, to achieve specific goals” (Cao, 2021).

Gartner's 2019 CIO Survey shows that the number of companies implementing AI has grown 270% over the past four years. In addition, based on the most recent McKinsey Global Survey on AI (2021), AI use is increasing, and the benefits remain significant. According to Burges (2018), the following megatrends are driving the rise of AI: big data, cheap storage, faster processors, internet connectivity and connected devices, developments in machine learning and



cloud AI with many tools available from tech giants such as Google and Amazon. As a result, these trends will continue to accelerate, and AI will continue to influence businesses, workers, consumers, suppliers, local communities, and society as a whole (Güngör, 2020).

2.2 AI capabilities

Like every new technology, such as AI, organizations must develop a unique set of resources to effectively use their investments to generate business value.

“The AI capability is the ability of a firm to select, orchestrate, and leverage its AI-specific resources.” (Mikalef & Gupta, 2021).

According to the information systems (IS) literature, businesses obtain competitive performance advantages by developing distinctive and difficult-to-copy capabilities that come from the combination and deployment of numerous complementing firm-level resources (Mikalef

& Gupta, 2021). In addition, according to Grant (1996), a firm's capabilities imply complex, coordinated patterns of skills and knowledge that become entrenched as routines over time and differentiate themselves from other organizational processes as they are outperformed by those of their rivals. Insofar as such capabilities are valuable, they can be sources of advantage that are particularly difficult for competitors to compete away from because they are difficult to detect and embed in the firm (Morgan et al., 2009). These capabilities are also linked to dynamic capacity, which allows firms to actively transform their resource base. In their study Dynamic Capabilities and Strategic Management, Teece et al. (1997) described the definition of dynamic capabilities as

"the firm's capacity to integrate, create, and reconfigure internal and external skills to handle



rapidly changing contexts". AI is therefore, in its broadest sense, about the skills, data, processes, structures, and strategies of an organization. AI readiness therefore encompasses more than just AI technology (ALSheibani et al., 2018). In this study, the following types of AI capabilities are considered: AI tangible resources, AI human skills, and AI intangible resources.

Acknowledgement is needed that maybe in the fast-evolving world of AI, these or other capabilities may change over time. This list of capabilities is therefore not collectively exhaustive.

Figure 1 and the following paragraphs explain these three proposed capabilities in more detail.

Figure 1 AI capability and categorization of resources (Mikalef & Gupta, 2021)

2.2.1 Tangible resources

Tangible resources are sources that can be sold or bought on the market, such as data, technology, and basic resources (e.g., time and financial resources) (Mikalef & Gupta, 2021). Managers perceive data to be one of the major facilitators in exploiting the potential of AI. In essence, having access to high-quality data is crucial since it is used to train AI algorithms (Ransbotham et al., 2018). In addition, because AI systems require large training data sets and applications effectively

"learn" from available information in the same way that people do, enormous volumes of high-



quality data are required (Alonso, 2015). These innovative kinds of data require whole new technologies for storing, processing, transferring, and securing data at all phases of data collection, insight production, and AI application training (Bayless et al. 2020). Aside from flexible data storage, AI technologies force firms to invest in systems that can rapidly analyze data and execute complicated algorithms. Given that AI technologies need infrastructure expenditures at numerous levels, many businesses, particularly those with limited resources, face significant challenges (Dwivedi et al., 2021). Aside from investments in data and technology infrastructure to enable AI, firms must be able to offer the time and financial resources required for such initiatives to succeed.

Because most businesses are only now beginning to experiment with AI, the vast majority of these efforts will need some time to develop before being deployed and generating value (Ransbotham et al., 2018).

2.2.2 Human resources

Human resources entail the knowledge, skills, experience, leadership qualities, vision, communication and collaboration competencies, and problem-solving abilities of its employees. It can be summarized as technical skills and business skills (Mikalef & Gupta, 2021). Technical AI skills are those required to deal with the development and realization of AI algorithms, managing the infrastructure to support such projects, and introducing and ensuring AI applications comply with goals (Spector et al., 2019). Obtaining the business value of artificial intelligence investments requires genuine knowledge and commitment on the part of leaders to drive a large-scale transformation. Managers must also be aware of AI's possible application areas and how to manage the transition to AI-enabled operations (Fountaine et al., 2019). Another crucial factor is managers' ability to initiate and organize AI implementations (Kolbjørnsrud et al., 2016). To eliminate



friction and possible forces of inertia, managers must build effective working connections between technical personnel and line function staff. This will help to speed the adoption of AI and increase business value (Kiron, 2017).

2.2.3 Intangible resources

Intangible resources are difficult to replicate by other firms and are of heightened importance in uncertain and volatile markets. Inter-departmental coordination, organizational change capacity, and risk proclivity are examples of these intangible resources (Mikalef & Gupta, 2021). Barney (1991) also states that organizational capabilities are intangible and socially complex resources that are difficult to imitate and thus are a source of sustainable competitive advantage for companies. According to current research on AI and business value, firms must establish a culture of cooperation, shared goals, and shared resources in order to maximize the value of AI technology (Ransbotham et al., 2018). According to Fountaine et al. (2019), AI has the greatest impact when it is created by cross-functional teams with a diverse set of talents. Organizations will be able to guarantee that AI projects serve broad corporate goals rather than discrete business challenges by doing so. Furthermore, organizational change capacity refers to the possible difficulties that might develop if an old procedure is not replaced with a new one. Developing a capability that reduces the frictions and inertia associated with change is regarded as a major source of digital transformation capabilities and overall company value in both management literature and IS research (Besson & Rowe, 2012). An organization that is unable to overcome this resistance is unlikely to enjoy the benefits of AI investments. Even though an organization has large quantities of data, highly trained technical staff, and state-of-the-art AI infrastructure, it will not be able to derive performance advantages unless it can leverage these resources and modify its present



business model to embrace AI developments (Mikalef & Gupta, 2021). Finally, Fountaine et al.

(2019) propose that firms must abandon their risk-averse strategic orientation and become agile, exploratory, and adaptive in order to benefit from AI. The fundamental premise is that organizations that are ready to break away from established procedures and set new, more ambitious goals are more likely to develop strong AI capabilities than companies that take a more cautious approach.

Jöhnk et al. (2021) found similar capabilities, stating that there are five categories of AI readiness factors: strategic alignment, resources, knowledge, culture, and data. Each action fulfills a prerequisite for successful AI adoption. According to Alsheiabni et al. (2019), AI can also be seen in four dimensions: AI functions, data structure, people, and organizational. The four AI dimensions are measured to determine how mature the organization is. At the last level of AI maturity, there is full adoption and standardization of AI infrastructure (AI functions). In addition, there is proactive data analysis because the data is available in real time (data structure). Further, employees are engaged with centralized leadership (people) and, finally, roles, responsibilities, and accountability are clearly defined in each AI project. This results in an AI culture (organization). As a result, while discussing AI capabilities, it is evident that employees play a significant role.



2.3 Employee engagement

Employee engagement has become one of the most important management concepts.

Organizations are focusing on increasing employee engagement in order to improve individual and organizational performance (Bailey et al., 2017). The concept of employee engagement is the degree to which employees are satisfied with their jobs, feel valued, and experience cooperation and trust. It states that engaged employees will stay with the company longer and continuously find smarter, more effective ways to add value to the organization. The end value is a well- performing company where people enjoy themselves and where productivity is increased and maintained (Catteeuw et al., 2007). According to Shrotryia & Dhanda (2020), employee engagement is a multifaceted construct with three dimensions: alignment, affectivity, and action orientation.

First, "the alignment dimension of employee engagement captures an employee's understanding of his/her work role to be in sync with the overall organization." An engaged employee aligns with the vision and mission of the organization, adopts the values of the organization, understands the broader goals of the work and knows what is expected of him/her.

A committed employee sees the work as meaningful, feels empowered, takes responsibility, and focuses on his/her goals for achieving the overall organizational goals. Furthermore, "the affectivity dimension of employee engagement reflects an employee's positive affectivity towards work and the organization." It reflects an employee's emotional experience, characterized by feelings of pride, involvement, belonging, and positivity. An employee with positive affectivity is motivated to put his/her heart and soul into the work. Such an engaged employee enjoys being at work and experiences self-enrichment. Finally, "the action-orientation dimension captures the behavioral manifestation of employee engagement." It reflects an employee's willingness to take



action for the effectiveness of the organization. An engaged employee is productive and creates value for the organization he/she works for. Action-orientation is seen in terms of an employee's proactivity, discretionary effort, participation, and contribution at work and for the organization.

In addition, employee engagement has also been extensively studied in the context of the information society because it is considered a key influencing factor for IT. This is also the case for the adoption of AI because this also requires organizations to engage their employees to overcome internal resistance (Brock & Wangenheim, 2019). According to Ransbotham et al.

(2021), AI can help organizations become more effective as well as disrupt teams and corporate cultures. Employee engagement is part of this culture and, according to Hughes et al. (2019), is essential to the health and performance of an organization. Brenyah & Obuobisa-Darko (2017) also state that employee engagement is a subject of organizational culture, and this organizational culture touches every aspect of an organization's life and affects everything an organization does.

Therefore, AI is not just about profitability; it is also about how the experience of working in a company might change as a result of AI. Once organizations start using AI, the AI finds its effectiveness in terms of improved quality of decisions and improved efficiency and starts transmitting learning to the organization. However, when implementing AI, one of the biggest obstacles indicated by senior management, by CEOs, and by other executives is that the implementation is going well, but the culture of the organization needs to change. There is a certain cultural aversion to the application of AI, but the implementation of AI itself actually helps to improve that. The company can be required to adopt some solutions without the requirement for a separate cultural shift. More and more people have faith in AI as a result of the variety of AI projects being undertaken and the effects these initiatives have on teams' productivity. Simply doing it eliminates the cultural issues. Making it a habit to carry out more AI implementations and



seeing the outcomes helps to reinforce the culture required to carry out more of them (Kiron et al., 2022).

However, Borges et al. (2021) state that literature shows that the strategic use of AI technologies for employee engagement has not yet been well exploited. They indicate that it could be investigated whether companies can have a competitive advantage through the proper use of AI technologies to increase employee engagement. The question is whether companies are mistakenly bracing themselves for a revolt when introducing AI. An example from the $10 billion spirits and wine company Pernod Ricard is that concerns were unfounded: people embraced AI because the technology enhanced rather than replaced their experience and knowledge. When companies use technology successfully, employees across the organization are able to perform more efficiently and collaborate more effectively. The introduction of AI not only improved their business performance but also changed their corporate culture (Candelon et al., 2021). The MIT SMR-BCG report of 2021 found that new levels of effectiveness brought about by AI implementation changed the team culture of companies. The cultural changes concerned what teams learned, how they learned, how they worked together, and, in some cases, what they liked about their work (Ransbotham et al., 2021). There are already examples in practice, such as the Chief Global Strategy and AI Officer at Levi Strauss & Co., who states that AI drives employee engagement and innovation. Hence, the following hypothesis is presented:

H1 AI capabilities (tangible resources, human resources, and intangible resources) are positively associated with employee engagement



2.4 Employee performance

Employee performance, in essence, refers to employee behavior that contributes to the organization's desired goals in terms of job quality, quantity, and timeliness. For an organization's goods and services, job quality is critical. Job quality is defined as satisfying established criteria and standards in the acquisition, manufacture, quality inspection, and delivery of products and services. Besides, the units of output created by employees' behavior, such as product amounts, waste quantities, and sales numbers, are referred to as job quantities. Monitoring job quantity is critical for work-related behavior because job quantity (i.e., units of output) represents an employee's dedication to fulfilling his or her tasks. Lastly, the amount of time required to execute work-related activities in relation to the difficulty of the tasks is referred to as task time. Employees accomplish their working time goals if needed tasks are completed precisely and in a fair period of time, and goods or services are delivered on time. To summarize, employee performance is a crucial factor for determining the output, outcomes, and success of an organization (Na-Nan et al., 2018).

The study of the relationship between information technology and performance is still relevant, even though it has changed over time. With the amazing advances that AI has brought, several researchers are now combining numerous theories and models for assessing the impact of AI on organizational performance and determining the financial value of transformation initiatives enabled by AI (Wijayati et al., 2022). According to Wiljer and Hakim (2019), AI enhances employee performance. Furthermore, the benefits of AI are its ability to improve performance at both the organizational (financial, marketing, and administrative) and process levels (Wamba- Taguimdje et al., 2020). This enables AI professionals to focus on higher-value tasks. Also, according to Malik et al. (2021), AI has a positive impact on work-related flexibility and autonomy,



creativity and innovation, and the performance of the employee. Therefore, AI will be used to gain new insights, make more reliable decisions, and improve business results. Companies can benefit from AI by directing, organizing, and automating processes. It will increase employee productivity and efficiency (Jain et al., 2017). AI has been hailed as a powerful tool for increasing productivity and improving the way companies attract, train, and retain staff. AI has a major impact on business productivity and performance. It has been proven that businesses perform better because AI algorithms efficiently use huge amounts of data, leading to better results. Managers can also benefit from AI because it provides them with critical data that allows them to make better business decisions. It will also help with retaining employees and acquiring new customers. Machine automation produces higher quality items faster and more efficiently while also providing critical data that allows managers to make better business decisions (Ramachandran et al., 2022).

Companies that perform better when implementing AI across the organization will have a significant advantage in a world where people and machines work together rather than people or machines working alone (Fountaine et al., 2019). Hence, the following hypothesis is presented:

H3 AI capabilities are positively associated with employee performance

It is not only claimed that AI capabilities can improve employee performance. It is also stated that companies with higher levels of employee engagement outperform their rivals (Cook, 2008). The literature has established a link between employee engagement and productivity and profitability, among others (Kumar & Pansari, 2014). Passionate employees produce better results for any company because they express active engagement and immersion in their work (Reijseger et al., 2017). Passionate employees perform better than non-engaged employees due to 10% higher



customer appreciation, 22% higher profitability, and 21% higher productivity (Ahmed et al., 2020). Therefore, employee engagement is an important topic in modern organizations because it is related to organizational performance. Several researchers confirmed in their research the positive effect that employee engagement has on the job performance of employees. This may be because engaged employees are likely to stay with their organization, and thus better performance will continue to be achieved within the organization (Brenyah & Obuobisa-Darko, 2017). Hence, the following hypothesis is presented:

H2 Employee engagement is positively associated with employee performance

The theory raises the question of whether good employee performance is achieved solely through the development of AI capabilities or through the impact that employee engagement can have on those capabilities. Other research, such as Bailey et al. (2017), who looked at perceived organizational support and job satisfaction, and Memon et al. (2014), who looked at person- organization fit and turnover intention, has also looked at this mediating influence of employee engagement. After all, engaged employees generate better results for any company because they show active engagement and absorption in their work (Reijseger et al., 2017). Hence, the following hypothesis is presented:

H4 Employee engagement has a mediating effect on the relationship between AI capabilities and employee performance



Because AI in a broad sense can refer to a variety of roles in organizations, including direct relationships such as data scientists, software engineers, system analysts, and IT directors, as well as indirect relationships such as operations managers, business managers, and project managers, which can result in differences in the level of employee engagement and employee performance, the following hypothesis is presented:

H5 The indirect effect of AI capabilities on employee performance through employee engagement depends on the AI relationship



2.5 Conceptual model

Based on the literature review, the research model captures the effect of three specific AI capabilities on employee performance through employee engagement. This model incorporates elements from the theories of Mikalef & Gupta (2021), Shroryia & Dhanda (2020), and Na-Nan et al. (2018), which enables the measurement of those variables. In addition, the moderator AI relationship is included in the model. The following conceptual model in Figure 2 will therefore serve as the foundation of this research:

Employee Engagement


Employee Performance

Dependent Variable

AI Capabilities

Independent Variable

H1+ H2+



Tangible Resources

Human Resources

Intangible Resources

Figure 2 Conceptual model for the influence of AI capabilities on employee performance through employee engagement

AI Relationship





3. Data and methodology

The research design and data collection methods used in this study will be discussed in this chapter.

The primary goal is to thoroughly explain the steps taken by the researcher to answer the study's main research question. The research design, sample context, questionnaire, data collection, data analysis, and hypothesis testing will be discussed.

3.1 Research design

The hypotheses are examined in this study using a deductive technique and a quantitative study in the form of a survey. Quantitative data collection methods ensure the generalizability of sample results to the entire population. In addition, the study has a correlational design that observes what naturally happens in the real world without intervention and will try to establish signs that AI capability impacts employee performance through employee engagement. The data source is primary data since a survey was used. Furthermore, a cross-sectional study was used as the research period was only a few months. The first advantage of a cross-sectional survey is that the data can be used to test hypotheses using regression analysis. Secondly, because the time for the survey is limited, a cross-sectional survey is the most practical option.

3.2 Sample context

A population is defined by Saunders, Lewis, and Thornhill (2016) as the elements from which a sample is taken. According to Fink (2003), the study population is defined as the people who are qualified to participate in a study. The target population was a 'subset of the population,' which was the research's main emphasis (Saunders, Lewis, and Thornhill, 2016). For this study, the target group consists of employees that are currently working with AI. The purpose of sampling was to



pick an adequate number of items from the population that accurately represented the population (Sekaran & Bougie, 2016). Because a sample frame was not readily accessible, convenience sampling was utilized in this study (Saunders, Lewis & Thornhill, 2016). The sample size refers to the number of respondents or units from whom credible results were obtained (Fink, 2003). In most research, a sample size of greater than 30 but less than 500 is considered adequate (Roscoe, 1975). The sample size was set at a total of 200 people to ensure the quality of the results.

3.3 Instrumentation and questionnaire design

The questionnaire was divided into two parts. In the first part of the questionnaire, the questions measured the demographic profile of the respondents. The questionnaire's second section includes questions about AI capability, employee engagement, and employee performance. The measurements were adopted from previous studies. All the constructs of the model were measured in a 7-point Likert scale format (strongly disagree – strongly agree), except the control variables.

The AI capability (independent variable) measurement has been adopted from the study of Mikalef

& Gupta (2021). Jöhnk et al. (2021) also investigated some of the dimensions. This results in a total of 19 questions: tangible resources (N = 7), human resources (N = 7) and intangible resources (N = 5). The 12 questions on employee engagement (mediator) were adopted from a study by Shroryia & Dhanda (2020). The 7 questions on employee performance were adopted from a study by Na-Nan et al. (2018). Besides the instruments, control variables are included in the survey to correct for differences among firms and employees. The control questions include industry, geographic region, size-class organization, employees’ relationship with AI, and employee age.

Participants of the questionnaire need to work with AI, so the respondent is being asked if AI is being used in their firm and how often they use AI in their daily work. The final survey was



constructed and distributed by the use of online research software Qualtrics and can be found in appendix I.

3.4 Data collection and preliminary checking

A pilot study with a sample of 10 participants was undertaken before launching the survey and continuing with its distribution. The primary goal of pilot testing the survey was to ensure that the questions were clear and understandable, as well as to learn more about how respondents felt about the overall page length experience. Participants were invited to provide their thoughts on three primary subjects after finishing the survey preview: understanding, survey length, and device (desktop and mobile version) compatibility. Data is collected by using various sources. The respondent used a direct link to the survey. First, the researcher's network is used by sharing the survey via the online platform LinkedIn. This platform is also used to get responses from AI groups. Furthermore, the network of PwC, the employer of the researcher, is used. The response rate of this approach was 98 started questionnaires. To increase the response rate, the platform Prolific (ProA, http://www.prolific.ac) is used. ProA has been used to recruit participants for hundreds of published studies. ProA was founded in 2014 as a software incubator firm by a group of graduate students from Oxford and Sheffield Universities. It is primarily aimed towards researchers and businesses and is backed by Isis Innovation, which is part of the University of Oxford. On its website, ProA gives a variety of demographic information about its participant pool, which researchers may use to screen participants. Prolific is promoted by Peer et al. (2017), and the data quality was assessed in this research. Prolific is also recommended by Palan & Schitter (2018) as an online participant recruitment method. The functioning of Prolific was assessed in this paper. The researchers praised Prolific's openness and accessibility, particularly the



prescreening function, which helps users find target groups. In total, 281 respondents started the questionnaire. The respondents were asked if they consented to participate in the study. Three respondents didn’t consent and were therefore excluded from further analysis. A company can only develop AI capabilities if AI is being used within the organization. Thus, the respondent is asked if AI is being used in their firm, ‘No’, ‘Don’t know’, or ‘Yes’. Survey data is only used when the answer ‘Yes’ is selected. This resulted in 18 survey responses being deleted. The relations between the variables are also only useful if the respondent is using AI in their daily work. Thus, the respondent is asked if they use AI in their daily work, ‘Frequently’, ‘Sometimes’

or ‘None use’. Survey data is not used if the answer was ‘None use’. This resulted in 13 survey responses being deleted. Only fully completed questionnaires were used, which resulted in 221 complete questionnaires with no missing data. These 221 are used, for instance, to compute the variables that represent an average score across multiple items (scale). Out of these 221 responses, nine outliers were removed due to unengaged responses (Z score above |3| for AI capability tangible resources (1 item) AI capability intangible resources (2 items), employee engagement (2 items) and, for AI relation (4 items)). 212 questionnaires met the target and were used for further analysis.

3.5 Data analyses and hypothesis testing

The first step was to get acquainted with the data in the SPSS system. Therefore, frequency distributions were created. Descriptive statistics were generated to describe the characteristics of the respondents. The data was then examined to determine if it met the requirements for further analysis. The test for normality is performed using skewness and kurtosis. A visual examination is also used to check all variables for normality (histogram). Since the numbers were within the



permissible range of -1 to +1, no transformation was necessary. Furthermore, the reliability was checked. When the measurement is constant and stable, the study can be considered reliable. If the research technique is repeated, the results should be the same or similar (Bush, 2012). Several methods can be used to assess the reliability of research. Cronbach's alpha, a technique for evaluating how well multi-item scales are internally consistent, is considered adequate for verifying survey data. This is because all variables are measured on multi-item scales. A Cronbach's alpha value of 0.7 indicates adequate reliability (Field, 2013).

The validity of the data was also examined. Validity refers to a technique's ability to accurately measure what it claims to measure. Concept validity relates to how well an instrument examines the construct in question. It is an important category of validity in survey research. A factor analysis can be performed to see if the questions are linked to one another or to a certain construct. External and internal validity are also crucial considerations. Internal validity refers to the question of whether the study findings are connected to and caused by the factors being studied (Winter, 2000). A well-established method of ensuring that the study is internally valid is to use a defined research framework that establishes the causal linkages between variables and removes the potential effect of false correlations (Gibbert, Ruigrok & Wicki, 2008). Its validity has been established in previous research on AI capabilities, employee engagement, and employee performance. External validity refers to the degree of generalizability of the research results across different settings, measures, and times (King & He, 2005). By selecting a representative sample of the population, the study will seek to ensure generalizability. Additionally, asking the same questions repeatedly will improve generalizability. Furthermore, the survey was distributed to representatives from a variety of firms across a wide range of industries, and all measures were completed within a four-week time frame.



Standardized coefficients of the hypotheses H1, H2, H3, and H4 (the mediating effect of employee engagement) are analyzed using PROCESS model 4 (simple mediation), measuring direct, indirect, and total effects. The AI relationship variable is tested as a moderator in the mediation model (H5). The moderation meditation effect is analyzed using PROCESS model 8 (moderated mediation with moderation of the a-path and the c'-path/direct effect).



4. Results

The results of this study will be discussed in depth within this chapter. Demographic profiles, a normality test, a validity and reliability test, descriptive findings, and a correlation test are all part of the analysis. Furthermore, inferential statistical techniques will be used to draw conclusions regarding the hypothesized relationships.

4.1 Demographic profiles of the respondents

As previously stated, there were a total of 212 valid responses. Table 1 provides an overview of the demographics of the sample used in this research. As shown in Table 1, most respondents are from Europe (72%), followed by Africa (23%). Moreover, respondents were asked to indicate their industry by selecting one of the five pre-defined sections. If the pre-defined sections were not applicable to the respondents, they could fill in an open section “other”. The majority of the respondents used this option. Smaller groups of respondents have an organizational size of micro, small, or medium, but the majority are working within companies with more than 250 employees (57%). As can be observed, the higher age groups were underrepresented in the sample because 73% were between 20 and 35 years old. Moreover, none of the 212 respondents used the option that they frequently use AI in their daily work; only the option "sometimes" was used. Finally, most of the respondents have a direct relationship (e.g., data scientist, software engineer, system analyst, IT director) with AI (67%) compared to a total percentage of 33% having an indirect relationship (e.g., operation manager, business manager, project manager) with AI.



Table 1 Demographic Characteristics of the sample (N=212)

Demographic characteristics Frequency (n) Percentage (%) Geographic

Europe 153 72

North America 6 3

Asia 4 2

Africa 49 23


Bank & Financial sector 29 14

Retailer & Consumer 15 7

Telecom & Media 58 27

Transport & Logistics 15 7

Others 95 45

Organizational size

Micro (1-9 employees) 4 2

Small (10-49 employees) 43 20

Medium (50-249 employees) 44 21

Large (250+ employees) 121 57

Employee age

Younger than 20 1 0

20-35 154 73

36-50 43 20

51-65 14 7



AI in daily work

Sometimes 212 100

AI relationship

Direct relationa 143 67

Indirect relationb 69 33

a e.g., data scientist, software engineer, system analyst, IT director

b e.g., operation manager, business manager, project manager

4.2 Normality test

The normality of the data distribution was tested using the skewness and kurtosis metrics. The skewness value reflects the symmetry of the data distribution. The kurtosis number indicates how peaked the data distribution is. For skewness and kurtosis, cut-off values of -1 and +1 are acceptable. Since all variables fulfilled the criteria, no power transformation was required, and this indicates that the data is typically distributed. Table 2 shows the results of the normality analysis.

Table 2 Normality testing

Variables Skewness Kurtosis

AI capability -0.16 -0.41

Tangible resources -0.40 0.15

Human resources -0.46 -0.40

Intangible resources -0.30 -0.32

Employee engagement -0.74 0.50

Employee performance -0.57 0.18



4.3 Validity and reliability test

A factor analysis is used to further validate the model. With KMO = 0.904, the Kaiser–Meyer–

Olkin measure confirmed the analysis' sampling adequacy. Furthermore, Bartell's sphericity test (Chi-square = 4616.13, p < .001) demonstrates that correlations between items were certainly high enough to warrant additional investigation; see Table 3 for results.

Table 3 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0,904 Bartlett's Test of Sphericity Approx. Chi-Square 4616,131

df 703

Sig. 0,000

Reliability analysis is used to ensure that the questionnaire consistently reflects the construct it is intended to measure. Cronbach's alpha was used to determine the measurement. For all multi-item scales in this study, the instrument is used to measure internal consistency, AI capability (19 items), employee engagement (12 items), and employee performance (7 items).

Furthermore, the latent variables, tangible resources (7 items), human resources (7 items), and intangible resources (5 items). See Table 4. Higher Cronbach's alpha values indicate a greater level of internal consistency and, as a result, construct validity. The Cronbach alpha value of 0.70 and above is preferred (Field, 2013). Construct reliability was found sufficient for all multi-item scales because all Cronbach’s alphas in this study exceeded this threshold value and most of them well exceeded it. The corrected item-total correlations indicate that all the items have a good correlation with the total score of the scales (all above 0.30). Also, the deletion of any of the items would not substantially affect reliability. As a result, all questions relevant to the constructs that were



primarily included in the survey were integrated to generate the final variables used for further analysis.

Table 4 Reliability testing

Variables Number of items Cronbach’s Alpha

AI capability 19 0.92

Tangible resources 7 0.81

Human resources 7 0.91

Intangible resources 5 0.76

Employee engagement 12 0.90

Employee performance 7 0.86

4.4 Descriptive results

Table 5 shows the overall averages and standard deviations for all the variables included in the survey. Because all questions were responded on a 7-point Likert scale (1=strongly disagree, 7=strongly agree), means might range from 1 to 7. Taking into mind the neutral response 4.0 (neither agree nor disagree), a mean over 4.0 is perceived as positive, and a mean below 4.0 is perceived as negative.

Table 5 Overall average scores

Variables Mean Standard Deviation

AI capability 5.16 0.85

Tangible resources 5.36 0.87

Human resources 4.99 1.13

Intangible resources 5.12 0.97



Employee engagement 5.82 0.72

Employee performance 5.73 0.75


Table 5 shows that, on average, respondents showed a high level of AI capability in their organization (M=5.16). Of this AI capability, tangible resources on average scored the highest (M=5.36), followed by intangible resources (M=5.12) and finally human resources (M=4.99).

Furthermore, respondents believe they are engaged in their jobs based on the high degree of employee engagement (M=5.82). Finally, respondents also believe they have a good personal performance (M=5.73).

Furthermore, control variables were included in the survey to explain possible variations and patterns in the respondents' answers. A total of two control variables were included in the analysis that were related to the demographic characteristics of the respondents, namely the age of the respondent and the size of the company. The effect of these control variables on employee performance was assessed by means of One-Way Anova.

Table 6 One Way Anova employee age

Anova descriptives N Mean Standard deviation

Younger than 20 1 5.71 -

20-35 154 5.73 0.78

36-50 43 5.71 0.67

51-65 14 5.82 0.65

Older than 65 - - -

Total 212 5.74 0.75



One-Way Anova Sum of squares df Mean square F Sig.

Between groups 0.13 3 0.04 0.07 0.97

Within groups 118.47 208 0.57

Total 118,59 211

Table 7 One Way Anova firm size

Anova descriptives N Mean Standard deviation Micro (1-9 employees) 4 5.71 0.78

Small (10-49


43 5.72 0.87

Medium (50-249 employees)

44 5.72 0.68

Large (250+


121 5.74 0.74

Total 212 5.73 0.75

One-Way Anova Sum of squares Df Mean square F Sig.

Between groups 0.03 3 0.01 0.02 1.00

Within groups 118.56 208 0.57

Total 118,59 211

The test verified that there is no significant link between respondents' age and employee engagement (F = 0.07 and p = 0.97) or firm size and employee engagement (F = 0.02 and p = 1.00), as shown in Tables 6 and 7. These control variables could not be utilized to explain



variability in respondents' responses since they had no significant relationship with the dependent variable.

4.5 Correlation test

The matrix containing the Pearson correlation coefficients for all constructs addressed in the research is shown in Table 8. The Pearson Correlation Coefficient, r, determines the strength and direction of the relationship between the variables in this research. A coefficient of +1 shows that the two variables are perfectly positively correlated, meaning that if one variable increases, the other increases proportionately as well. This does not imply that one variable causes the other to change, only that their changes coincide. A coefficient of -1, on the other hand, implies a perfect negative relationship: if one variable increases, the other decreases proportionately. A 0 coefficient shows that there is no linear relationship at all (Field, 2013). Except for the AI relationship, the coefficients in Table 8 show a positive and very significant correlation between all variables.



Table 8 Means, Standard deviations, Correlations

7 1.00 N = 212 *Correlation is significant at the .05 level (2-tailed) ** Correlation is significant at the .01 level (2-tailed)

6 1.00 .15*

5 1.00 .65** .19**

4 1.00 .48** .54** .13

3 1.00 .56** .45** .47** .10

2 1.00 .59** .58** .46** .50** .14*

1 1.00 .85** .89** .80** .54** .59** .14*

SD 0.85 0.87 1.13 0.97 0.72 0.75 Categorical data

Mean 5.16 5.36 4.99 5.12 5.82 5.73

Main variable/ latent variables AI capabilities AI tangible resources AI human resources AI intangible resources Employee engagement Employee performance AI relation

1 2 3 4 5 6 7



4.6 Hypothesis testing

The previously formulated hypothesis will now be tested in order to determine whether or not the proposed relationships do in fact exist between the constructs. The main objective of this research is to establish whether higher AI capabilities lead to more employee engagement and, as a result, better employee performance. The research continues by looking at the impact of the AI relationship on employee engagement and employee performance.

4.6.1 Mediation

Mediation refers to a situation when the relationship between a predictor variable (AI capability) and an outcome variable (employee performance) can be explained by their relationship to a third variable (employee engagement, the mediator). For this hypothesis to be true, (1) AI capabilities must predict employee performance in the first place (path c’); (2) AI capability must predict employee engagement (path a); (3) employee engagement must predict employee performance (path b) and (4) the relationship between AI capability and employee performance should be higher

Employee Engagement


Employee Performance

Dependent Variable

AI Capabilities

Independent Variable

H1+ H2+




β = 0.296**

β = 0.227**


Figure 3 Conceptual model with results of regression coefficients paths. N=212. (Note standardize regression coefficients are reported)


**p<0.001 (two-tailed)



when employee engagement is included in the model than when it isn’t (total effect c) (Field, 2013). The letters denoting the paths, in Figure 3, represent the unstandardized b-values.

Table 9 Regression analysis of AI capabilities, employee engagement, and employee performance (H1, H2 and H3)


Employee engagement (M) Employee performance (Y)

Antecedent Coeff. SE p Coeff. SE p

AI Capability (X) a 0.456 0.050 <.001 c’ 0.296 .052 <.001 Employee

engagement (M)

--- --- --- b 0.497 .060 <.001

Constant i1 3.250 0.321 <.001 i2 1.576 .341 <.001

R2 = .294 R2 = .506

F(3,208) = 28.841, p <.001 F(4,207) = 53.024, p <.001

Table 10 Total, direct and indirect mediation effect of employee performance of AI capabilities on employee performance (H4)


Direct effect c’ 0.296 .052 <.001 0.195 0.398

Total effect c 0.523 0.050 <.001 0.424 0.622

Boot SE Boot LLCI Boot ULCI

Indirect effect a*b 0.227 0.042 0.148 0.315



As can be derived from Table 9, the R² is 0.51, and this quantifies the proportion of the total variance of employee performance (Y) explained by the overall model. Therefore, this solution explains 51% of the variance of employee performance, which is statistically significant (p < 0.01). When looking at the a-path of the regression analyses, it can be concluded that the effect of AI capabilities on employee engagement is a = 0.456, this means that two employees whose AI capabilities differ by one unit are estimated to differ by 0.456 units on employee engagement. The sign of a is positive, meaning that those relatively higher in AI capabilities are estimated to be higher in employee engagement. This effect is statistically different from zero, t=

9,097, p = .000, with a 95% confidence interval from 0.357 to 0.555. The second regression analysis conducted, the b-path, has an effect of b = 0.497 and indicates that two employees who experience the same level of AI capability but differ by one unit in their level of employee engagement are estimated to differ by b = 0.497 units in employee performance. The sign of b is positive, meaning that those relatively higher in employee engagement are estimated to be higher in employee performance. This effect is statistically different from zero, t= 8.245, p = .000, with a 95% confidence interval from .378 to .615. The results in Table 10 demonstrate the a*b-path, the indirect effect. An indirect effect of 0.227 means that two workers who differ by one unit in AI capability are estimated to differ by 0.227 units in employee performance because of their employee engagement. This indirect effect is statistically different from zero, as revealed by a 95%

BC bootstrap confidence interval that is entirely above zero (0.148 to 0.315). Moreover, Table 10 shows the c’-path, the direct effect of AI capability, c’ = 0.296. This is the estimated difference in employee performance between two workers experiencing the same level of employee engagement but who differ by one unit in AI capability, meaning that a worker with a higher AI capability but who is equally engaged is estimated to be 0.296 units higher in employee performance. This direct



effect is statistically different from zero, t= 5.750, p = .000, with a 95% confidence interval from 0.195 to 0.398. Lastly, the c-path, the total effect of AI capability on employee performance, is shown in Table 10. The result is c = 0.523, meaning two workers who differ by one unit in AI capability are estimated to differ by 0.523 units in their employee performance. The positive sign means the person with higher AI capability reports higher performance. This effect is statistically different from zero, t = 10.436, p = .000, interval between 0.424 and 0.622 with 95% confidence.

4.6.2 Moderated mediation

Whether the indirect effect of AI capabilities (X) on employee performance (Y) through employee engagement (M) depends on the AI relationship (W) was investigated using moderated mediation analysis using PROCESS model 8. However, the p-value of the interaction term turns out to be 0.333 and is therefore not significant. The analysis stops here because the moderation effect does not take place. The results of the moderated mediation analysis may be found in Table 11.



Under high workload situation, the breadth of functional experience maybe cannot be transferred to creativity because the lack of cognitive control resources and damaged cognitive

While we know from previous research that contingent reward leadership in general is positively related to employee job performance (Podsakoff, Todor &amp; Skov, 1982; Bass,

As shown in this figure,a high level of expertise of the employee shows a positive relation between performance orientation and leaders‟ attitude to employee voice, whereas a

ROE is the return on equity, calculated as Earnings per Share for the most recent fiscal year divided by the previous year’s book value per share.. Net profit is the net

If managers are obliged to shareholders and employees, and both groups have substantial power in the executive pay bargaining process, the performance-based compensation elements

Word embedding is important for the similarity measure of soft cosine similarity because student answers can address the same topic in different ways (by using different words,

While the performance of an intuitive and a non-intuitive interface that controls a 2-D cursor using arm muscles has been studied before, no known study has compared control

The results for the fiscal policy outcomes ( Fig. 6 c) suggest even more procyclicality – e.g., when government efficiency is low and fiscal rules are weak, a positive output

We motivate that the service time and channel access delay for the DCF MAC cannot directly be used to obtain the end-to-end delay of the received information at the receiver, because

The estimates by Leithwood and Jantzi (2009) express this consensus well when they claim that optimal school sizes at elementary and secondary school levels are

In the pilot, we evaluate the four services mentioned: social interaction, social activities, medication intake and compliance, and health monitoring.. Before the pilot,

De eerste hypothese wordt hiermee dus verworpen, de attitude ten opzichte van een goed doel is niet positiever wanneer er een sportevenement georganiseerd wordt door het goede

The tool framework is used to answer the questions of the deployment question set and the textual representation of the architectural model is produced by the tool given in

With all of the implementations it is possible to send a message to a specific set of people, however if the message needs to be marked private and sent to users on remote hosts

similis te creëren, zijn in het jaar voorafgaand aan het schadeonderzoek dus in de jaren 2006, 2007 en 2008 verschillende behandelingen en teelten uitgevoerd, namelijk:

This study focuses on the particular context of underperforming schools and the role of the principal in staff‟s motivation to participate in development activities

We found that (1) EDI routes emerge through an idea generation phase, an idea development phase, and an implementation phase (2) through which phases work- floor employees

Guedri and Hollandts (2008) test the moderating impact of employee shareholder board representation on the relation between employee ownership and firm performance, but they find

The research question can therefore be answered as follows: the outcomes of the case study indicate that changes in the performance measurement system have a negative

Current research, however, indicates that a more collaborative teaching culture picking up characteristics of research cultures, such as collaboration, collegiality, continuous

 Method: theory and report on a case study of the “Money maker” water pump.... Normative aspect

This usually occurs if the originating organization lacks the resources to support and implement the innovation themselves (Mulgan et al., 2007, p.18). Governments are often

Longmans, Parallel Series Parallel with &#34;Longmans' Leesboek voor Verenigd Zuid - Afrika&#34;. Longmans' Union South African