factors affecting artificial intelligence capability and evaluating the impact of artificial intelligence capability on businesss performance

UNIVERSITY OF ECONOMICS HO CHI MINH CITY UEH UNIVERSITY Factors affecting Artificial Intelligence capability and evaluating the impact of Artificial Intelligence capability on busine

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

UEH

UNIVERSITY

Factors affecting Artificial Intelligence

capability and evaluating the impact of

Artificial Intelligence capability on business’s performance

SV THUC HIEN: HUYNH THUY MINH HUY MSSV: 31211020477

GV: LÊ NHẬT HẠNH LHP: 22C1MANS50213104

01/01/2023 HCM

TABLE OF CONTENTS Chapter 01: Introduction

1.1 Research background and statement of the problem 1.2 Research objectives

1.3 Research question 1.4, Subject and scope of research 1.5 Research Method

1.6 Overview of research related to the topic 1.7 Research contribution

Chapter 02: Literature review and hypothesis development 2.1 Artificial intelligence and conceptualizing an AI capability 2.2 Tangible resources

2.3 Data 2.4 Technology 2.5 Basic Resources 2.6 Human skills 2.7 Technical skills 2.8 Business skills 2.9 Intangible 2.10 Inter-departmental coordination 2.11 Organizational change capacity 2.12 Risk proclivity

2.13 Performance Chapter 3: Research Methods 3.1 Introduction

3.2 Study design

12 12 12 13 13 13 14 14 15 15 15 16

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Chapter 01: In YOU COmpUter

In recent years, the continuous development o value and breakthroughs to national and global economies And one of the great advances in the field of computer science and technology in recent years is Al-Artificial Intelligence, which remains the most spectacular IT application, a technology that has gone through an unequaled development over the last decades (Blanchet, 2016; Lee et al., 2018; Wiljer and Hakim, 2019).It is defined as a set of “theories and techniques used to create machines capable of simulating intelligence Al is a general term that involves the use of a computer to model intelligent behavior with minimal human intervention” (Benko and Lanyi, 2009; Haenlein and Kaplan, 2019; McCorduck et al., 1977)

Although AI is no longer odd in everyone's psyche, integrating AI into business and production processes, as well as providing value in any profession, remains a challenging task Because AI requires a large amount of resources and human resources, there are far too many aspects that emerge in tandem with its operation Furthermore, there are currently few papers that explain the influence of AI on performance and how it works in an organizational setting

The goal of our study is to examine what factors affect Al capability as well as how AI capability impacts on business's performance

1.2 Research objectives Research focuses on the following issues : e Factors affecting the AI capability e Evaluate the impact of those factors on the AI capability e Verify the impact of AI capability on business’s performance e Evaluate the impact of Al capability on business’s performance e Propose a plan to improve the impact of Al capability on business’s performance 1.3 Research question

- What are the main factors affecting the AI capability? - How do these factors impact AI capability? - How is the impact of Al capability on business’s performance? 1.4, Subject and scope of research

A Subject The research subjects of this topic are factors affecting Artificial Intelligence capability and evaluating the impact of Artificial Intelligence capability on business's performance

Excel 2016 is used to encode the raw data from the survey table, Google Forms is utilized for descriptive statistical analysis, and PLS from SMART is used for the remaining tests

1.6 Overview of research related to the topic Prior relevant studies

(1) Influence of artificial intelligence (AI) on firm performance: the business value of Al-based transformation projects

The main purpose of the study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of Al-based transformation projects

Machine translation, chatbots, and self-learning algorithms are just a few examples of the many technologies under the umbrella of artificial intelligence (AI) that may help people comprehend their surroundings and respond appropriately In order to disrupt their ecosystem or adapt to it, organizations have been using AI technology advancements while also creating and maximizing their strategic and competitive advantages The findings of this study have underlined these advantages of AI in firms, and more especially, its capacity to enhance process and organizational

performance (financial, marketing, and administrative) The same findings demonstrated that enterprises can only achieve performance through AI capabilities when they modify their business processes in order to take use of their features and technology Limitations/implications of the research Clearly, AI has an impact on how firms operate today

As a result, practitioners and academics should view AI as useful auxiliary technology or even as a test bed for novel business models Organizations may therefore increase the commercial value of their changed initiatives by building on these characteristics In terms of interest, this work fosters a scientific curiosity that tries to fill a vacuum in the literature by presenting a model for measuring the impact of AI on the performance of companies To better account for the intangible advantages of AI in enterprises, the Authors selected a research paradigm for their study that is targeted toward a more inclusive and complete approach Regarding the managerial interest, their study aims to give managers elements that can be changed or added in order to fully benefit from AI, and as a result, improve the performance of organizations, the profitability of their investments in AI transformation projects, and some competitive influence of AI on firms' advantages

Capabilities of Al Process LeVeÌ ——————¬ ©rganizational Level

Financial Performance Automational Effect * Budgeting, Cash flow, Cost * Efficiency P4 cuttings, Market value,

AI Management Pl * Realibility Growth, Profitability,

Capabilities * Routinazation Labour savings

Marketing Performance Customer Satisfaction, Price

AI Personnel P2 Informational Effect a jms’ reduction, New products

Expertise —_ * Responsiveness Bin ‘services, customer/prospect

Decision Quality scoring and targeting

Resource Management * Personalization

Listening to and analyzing

Al Infrastucture | P3 Transformational Effect social “noises” Flexibility , ervices Enhancement E6 ‘Administrative

Competitive Capability Control , Co-ordination

* Build Trust * Planning

* Personalization: zoning and editorial content, product recommendation engine, dynamic pricing

Figure 1 Research model, Adapted from Anand and Fosso Wamba (2013) (2) The impact of big data analytics and artificial intelligence on green supply

chain process integration and hospital environmental performance In recent years, BDA and AI research as well as applications in healthcare have advanced Numerous prospects in the healthcare industry have been made possible by artificial intelligence technology The combination of BDA and AI provides intriguing potential for medical research in addition to advantages for medical treatment built a

conceptual model that enables the explanation of organizational behavior in businesses using information processing techniques made possible by BDA-AI technology

They drew connections between hospitals' usage of BDA-AI technology and its EP In order to improve environmental performance, they explained how BDA-AI assists hospitals in processing the data necessary to make internal and external green supply chain choices The model provides a direct connection between BDA-AI and cooperation in the green supply chain (GSCC) BDA-AI technology is used to make choices for EPI and to transmit real-time knowledge among internal operations The authors created their hypotheses on the effects of a moderating variable, specifically the GDLO, and predicted that the GDLO would have a moderating influence on the link between BDA-AI, EPI, and GSCC

The study offers insightful information about how BDA-AI technologies support EPI and GSCC and affect EP A significant result that has not been addressed in the existing literature is that the study provides fresh information on how digital learning moderates the interaction between BDA-AI technologies and the GSC process The results demonstrate the connections between BDA-AI, EPI, GSCC, GDLO, and EP and back up our theoretical framework The study offers a significant theoretical advancement and crucial information for managers and decision-makers who could support hospitals’ shift from linear to circular economies

The results of their study demonstrate a substantial positive link between BDA and the GSCC, which is in line with earlier studies that support the use of BDA for efficient internal process integration in an unpredictable environment (Lee and Klassen, 2008; Narayanan et al., 2011) (H1) The association between BDA-AI technologies and environmental internal integration in a hospital context hasn't been explicitly shown via empirical research, nevertheless Their findings matched those of a previous engineering and technology management study (Lamba et al., 2019), which showed the influence of BDA on the supplier selection procedure In the medical industry, which lacks the capacity and maturity to apply environmental principles, supply chain coordination is a crucial step Their discovery highlights the important direct and indirect impact of internal integration on hospitals' EP (H3; H6) More precisely, they believe that the big data already exists in hospitals as a resource that has to be developed in terms of internal processing and analysis in order to lessen organizational complexity in hospitals and promote GSC efforts Internal process activities (quality, buying, logistics, manufacturing, and administration) can profit from cutting-edge medical activities by using BDA and AI to create a reliable environmental model Finally, the results demonstrate that GSCC has a favorable impact on hospitals’ environmental performance (H7)

+ Green supply chain process + | ' '

(H6

GDLO 5

Fig 1 Conceptual model

Fig 2: Conceptual model of S Benzidia et al (3) Artificial intelligence capability: Conceptualization, measurement

calibration, and empirical study on its impact on organizational creativity and firm performance

Over the past few years, artificial intelligence (AI) has become a top technology goal for businesses, partly due to the availability of huge data and the development of advanced tools and infrastructure An enterprise needs a special combination of physical, human, and organizational resources to establish an AI capacity, according to early reports from prominent corporations in terms of AI adoption One such resource that is required but insufficient to build an AI capacity is AI technologies Therefore, in order to maximize their investments in AI, organizations need to make complementary resource investments In order to get performance improvements from Al, it is crucial to comprehend what complementary resources need to be produced and put them into practice

Implementation and reorganization delays, according to the authors, are one of the key reasons AI hasn't yet produced the desired results Although AI technologies have enormous promise Additionally, the data needed to power these methods by itself won't be enough to develop distinct AI capabilities In other words, it's time to look at how businesses develop their AI capabilities Because of the unique and difficult-to-copy capabilities that emerge when combining and deploying a number of complementary firm-level resources, we know from the IS literature that firms can improve their competitive performance This highlights the fact that we are currently facing a modern productivity paradox

In essence, this suggests that while AI approaches are readily available on the market and susceptible to replication, they are unlikely to provide significant competitive

advantages on their own They tested the AI capabilities scale's association with organizational innovation and performance to determine its nomological validity

Artificial Intelligence

Capability

Tangible Human Intangible

ca * inter-departmental

+ Oete * Technical Skilis Coordination

lechnology + BusinessSkills * Organizational

* Basic Resources Change Capacity

Organizational

Creativity H3 AI Capability

Organizational Performance

Fig 4: Conceptual research model (P Mikalef and M Gupta) (4) Artificial intelligence capability: Conceptualization, measurement

calibration, and empirical study on its impact on organizational creativity and firm performance

A slow but fundamental paradigm shift is required to transition from conventional business-to-business (B2B) relationship management to an artificial intelligence-based customer relationship management (AI-CRM) Intelligent systems are used by AI-CRM to automate B2B relationship activities so that decisions may be made without human involvement Relationship management is regarded as an organizational strategy in the B2B sector Introduction Artificial intelligence (AI) is on track to revolutionize marketing management by presenting fresh opportunities and difficulties for the industry In order to understand how AI-CRM could affect the performance of the firm with variable firm size, firm age, and industry type, this study blends institutional theory and the resource-based view (RBV) in B2B relationship management | The usefulness of Al-based solutions is widely acknowledged and explored, particularly within the business-to-business (B2B) and industrial marketing

segments Employees are impacted by cultural, cognitive, regulative, and normative aspects of organizations By definition, organizations can never reach a perfect, stable state and are always undergoing both gradual and drastic transformation processes In a complicated corporate environment, B2B marketers want intelligent tools to automate the process of organizing, standardizing, aligning, and personalizing data It is likely that academics and practitioners have been inspired to increase their understanding of customer relationship management by the effects of managing long-term customer relationships on corporate profitability (CRM)

j a ễ R2=0.76

Employee Experience for B2B

(EEB)

5 R2=0,69 ®

> Competitive

Organization ứ y

It is suggested that while it has a direct impact on SCP in the short term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP The developed framework was evaluated using structural equation modeling Firms should develop analytical capabilities to enhance SCRes by effectively utilizing the resident from knowledge, thereby strengthening organizations

existing information capabilities The studies have found a positive linkage between business, information, engineering, and analytics to develop digitalization and supply chain risks

Artificial Hị; Supply chain Hii Supply Chain lacs Geographic Remarc { eg xử —T | area(GA) Intelligence i oe? Collaboration F— Resilience l$ mg

(AD ' (SCC) (SCRes) bà \ - Ea - /

\ J Business Ha { 4 sector(BS)

V /

Firm size Supply Chain

dynamism (SCD)

(FS)

Supply Chain % performance }

Chapter 02: LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT Chapter | provides an overview of the research This chapter 2 aims to introduce the theoretical basis for the research On this basis, the research model is built along with hypotheses about the relationship between the concepts in the model This chapter consists of three main parts:

(1) Theoretical basis (2) Research hypothesis (3) Research model

2.1 Artificial intelligence and conceptualizing an AI capability AI has been a topic of interest for several decades, which is not a new and unfamiliar concept anymore Al - Artificial Intelligence with two distinct core concepts "the ability to interact and learn, , adopt, and resort to information from experiences, as well as to deal with uncertainty" according to Legg and Hutter And artificial is an

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idea, something created by human hands, or perhaps a copy of something out of nature We can rely on two factors: “intelligence” and "artificial" to determine that Al is a stimulation of human learning mechanisms, information processing as well as problem solving Above all, as (Patrick Mikalef and Manjul Gupta,2021) in their recent Al paper, provide a new definition based on previous research: "AI is the ability of a system to identify, interpret, make inferences, and learn from data to achieve predetermined organizational and societal goals.” This definition is limited in scope toward the study of management and information systems—related phenomena By developing this definition, it is thus easier to identify what does and what does not constitute an AI within the organizational setting

Al capabilities could be defined as the firm’s ability to create a bundle of organizational, personnel and AI resources for business value creation and capture According to (S-L Wamba-Taguimdyje et al, 2020) research paper, AI capability is divided into 3 parts: Al management capability, AI personnel expertise and AI infrastructure flexibility Three of them also have a significant positive effect on Al capabilities

Published research on business value and use of AI that its impact on organizations is still limited Incompetent technology 1s one of the biggest barriers to fully utilizing AI capabilities Above all, the fact that Al infrastructure and data transport is an untapped resource which is of great concern.In addition, the problem of lack of human resources and leaders to support AI is a very difficult problem Previous studies have also emphasized that Al-enabled resources should be cultivated and developed, and inter-departmental cooperation and coordination possible When working with AI, it requires a kind of skills brand new for both technical staff and management

From past academic research to the present, there is a diversity of resources that organizations need to leverage to derive business value from AI investments However, there is still very little theoretically grounded research on how organizations can create AI capabilities This is an important gap for both research and practice, as it can point to core areas that organizations should focus on when implementing AI initiatives and evaluate potential business value and generate huge value and profits

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