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1. Understanding dark side of artificial intelligence (AI) integrated business
analytics: assessing firm’s operational inefficiency and competitiveness
Presented By: Tayyab Ali Baig
Date: April 15, 2024
Advances in Consumer Behavior
Department of Management Sciences
Author: Rana et al., (2022)

2. Outline
• Introduction
• Study Objectives
• Study Gap
• Theoretical Background
• Conceptual Model and
Hypothesis Development
• Methods
• Data Analysis & Results
• Discussion
• Implications
• Limitation and Future
Research
• Conclusion

3. • The introduction section provides an overview of the study on the dark side of
artificial intelligence integrated business analytics.
• It highlights the importance of understanding the potential negative implications of
AI integration in business operations.
• The study aims to assess how AI integration can lead to operational inefficiencies and
competitive disadvantages for firms.
• The introduction sets the stage for exploring the impact of AI on business analytics
and the need for transparency and accountability in AI systems.
• It emphasizes the significance of designing next-generation AI integrated BA systems
that are beneficial, explicable, and transparent to avoid unintended outcomes.
Introduction

4. Study Objectives
• The study aims to identify how factors such as AI-BA opacity, suboptimal business
decisions, and perceived risk contribute to a firm's operational inefficiency and
competitive disadvantage.
• It seeks to understand the components and effects of AI-BA opacity on a firm's risk
environment and negative performance.
• The research model is based on the resource-based view, dynamic capability view,
and contingency theory to capture the relationship between AI-BA opacity,
suboptimal decisions, perceived risk, operational inefficiency, and competitive
disadvantage.

5. • The study addresses a gap in research by focusing on the unintended consequences of
AI integrated business analytics (AI-BA) on a firm's competitive advantage.
• There is a lack of research on how factors like AI-BA opacity, suboptimal business
decisions, and perceived risk influence operational inefficiency and competitiveness.
• The study aims to fill the gap by investigating how AI-BA opacity leads to
suboptimal decisions, higher perceived risk, operational inefficiency, and ultimately,
competitive disadvantage for firms.
• By examining the dark side of AI integration, the study provides insights into the
potential risks and challenges associated with leveraging AI in business analytics.
Study Gap

6. • The theoretical background draws on the resource-based view and dynamic
capability view to understand how AI-BA opacity can impact a firm's competitive
advantage.
• It integrates contingency theory to explore the moderating effect of contingency
plans on suboptimal business decisions, perceived risk, operational inefficiency, and
firm performance.
• The study emphasizes the importance of data governance capability, data quality, and
training capabilities in mitigating AI-BA opacity and its negative effects on firm
performance.
• By combining these theoretical perspectives, the research model aims to identify the
components and effects of AI-BA opacity on a firm's risk environment and
operational inefficiency.
Theoretical Background

7. • The conceptual model explores the impact of adopting an inappropriate AI integrated
business analytics (AI-BA) solution on a firm's performance.
• It consists of three main clusters of factors: flawed technology strategy, risk
environment due to inappropriate AI integration, and negative firm performance and
competitive disadvantage.
• Under flawed technology strategy, the model focuses on AI-BA opacity, which
includes components like lack of governance, poor data quality, and inefficient
training.
• The model highlights the importance of addressing AI-BA opacity to prevent
suboptimal business decisions, perceived risk, operational inefficiency, and
competitive disadvantage within a firm.
Conceptual Model

8. Conceptual Model and Hypothesis Development

9. H1: AI-BA opacity will lead to a suboptimal decision.
H2: AI-BA opacity will result in increased perceived risk.
H3: Suboptimal business decision has a significant impact on perceived risk
H4: Suboptimal business decision will lead to the firm’s operational inefficiency.
H5: Perceived risk will result in the increase of a firm’s operational inefficiency.
H6: Higher firm’s operational inefficiency will result in superior negative sales growth
H7: Higher firm’s operational inefficiency will lead to higher employee’ dissatisfaction
H8: Higher negative sales growth of a firm will lead to a firm’s competitive disadvantage.
H9: Higher employee’s dissatisfaction will result in a firm’s competitive disadvantage.
H10a: The contingency plan has a considerable moderating effect on the SOD → OPI (H4)
linkage.
H10b: The contingency plan has a considerable moderating effect on the PRI → OPI (H5)
linkage.
Hypothesis

10. • The research methodology involved developing a set of 33 measurement instruments
based on existing literature and theoretical background.
• Six domain experts were consulted to ensure the validity and readability of the
measurement items.
• A pretest was conducted to validate the measurement instruments, and minor
corrections were made based on the feedback received.
• Data collection was facilitated through an online questionnaire shared with key
officials in business organizations in India.
• The data collection strategy aimed to gather responses efficiently and cost-
effectively, utilizing industrial links and online survey tools like Google Docs.
Research Methods

11. Research Instrument
• The research instrument comprised 33 measurement items designed to assess
constructs related to AI integrated business analytics (AI-BA) opacity.
• The items were developed based on existing literature, theoretical background, and
input from domain experts.
• Six experts in the study domain were consulted to ensure the validity and readability
of the measurement items.
• A pretest was conducted to validate the 33 measurement instruments, and
adjustments were made based on the feedback received.
• The items were quantified using a 5-point Likert scale and provided to usable
respondents for data collection.
Research Methods

12. Data Collection Strategy
• The data collection strategy involved targeting potential respondents through
industrial links with key officials at business organizations in India.
• Key associations such as the Federation of Indian Chambers of Commerce &
Industry (FICCI) and Confederation of Indian Industry (CII) National Association of
Software and Service Companies (NASSCOM) were utilized for data collection.
• An online questionnaire was created using Google Docs and shared with key officials
in business organizations through the assistance of industrial links.
• The online questionnaire format allowed for swift data collection in a specific format
with minimal cost.
• The data collection strategy aimed to gather responses efficiently and effectively
from a diverse pool of respondents with expertise in various sectors.
Research Methods

13. • The study applied partial least squares (PLS) structural equation modeling (SEM) to
estimate the research model, known for providing robust results for complex
hierarchical models.
• Higher-order constructs such as inappropriate AI integrated business analytics (AI-
BA) and perceived risk were estimated using the PLS-SEM approach.
• The study utilized the guidelines of Becker et al. (2012) for a repeated indicator
approach to estimate the higher-order constructs.
• The analysis involved using Smart PLS 3.2.3 software with non-parametric
bootstrapping of 5,000 replications to estimate path coefficients and test hypotheses.
• The data analysis and results section focused on analyzing the relationships between
constructs related to AI-BA opacity and operational inefficiency in firms based on the
collected responses.
Data Analysis and Results

14. Data Analysis
• The measurement properties of all first-order constructs were presented in Table 2.
• Convergent validity of all items was assessed by estimating the loading factor (LF)
for each item, with all loadings exceeding 0.70.
• Composite reliability (CR) and average variance extracted (AVE) of all constructs
were calculated, with all estimates surpassing 0.80 and 0.50, respectively.
• Cronbach's alpha (α) was measured for each construct to assess consistency, ensuring
reliability of the measurement model.
• The results indicated strong measurement properties for the first-order constructs,
supporting the validity and reliability of the measurement model used in the study.
Data Analysis and Results

15. Discriminant Validity Test
• Discriminant validity of all first-order constructs was evaluated using the Fornell and
Larcker criterion.
• The square roots of all average variance extracted (AVE) values were compared to
the corresponding bifactor correlation coefficients.
• Results confirmed discriminant validity when the square roots of AVE values were
greater than the bifactor correlation coefficients.
• The assessment demonstrated that the constructs were distinct from each other,
supporting the validity of the measurement model.
• The discriminant validity test provided assurance that the constructs measured in the
study were unique and not overlapping.
Data Analysis and Results

16. Structural Model
• Partial Least Squares Structural Equation Modeling (PLS-SEM) was utilized to
examine the relationships between latent variables in the structural model.
• PLS-SEM provided insights into how the latent variables were interrelated and
assessed the overall model fit.
• The analysis focused on determining if the model accurately represented the
relationships between constructs.
• Path coefficients between the main constructs and their subdimensions were
estimated and found to be significant at p < 0.001.
• The structural model analysis helped validate the relationships proposed in the
research model and provided a clear understanding of the interconnections between
variables.
Data Analysis and Results

17. Moderation Analysis
• A multi-group analysis (MGA) was conducted to estimate the effects of the
moderator, contingency plan (COP), on specific linkages covered by hypotheses H4
and H5.
• Bias-correlated accelerated bootstrapping with 5,000 resamples was used to
determine p-value differences for the effects of the moderator on the linkages.
• The analysis focused on assessing the significance of the moderator's impact on the
relationships between variables.
• The moderator effects on the linkages SOD→OPI and PRI→OPI were examined
through hypotheses H10a and H10b, respectively.
• Significant p-value differences for strong COP and weak COP categories indicated
the moderator's significant influence on the relationships, as shown in Table 7.
Data Analysis and Results

18. Robustness Analysis
• The PLSpredict analysis was employed to evaluate the predictive robustness of the
Partial Least Squares Structural Equation Modeling (PLS-SEM) outcomes.
• The analysis involved dividing the samples into segments to assess the predictive
power of the model on the outcome construct, firm's competitive disadvantage
(FCD).
• Assessments included root-mean-square error (RMSE), mean absolute error (MAE),
mean absolute percentage error (MAPE), and Q2 for various analysis types such as
PLS-SEM, linear regression model (LM), and PLS-LM.
• MAE values were reported for different factors, indicating the predictive accuracy of
the model.
• The results demonstrated the model's predictive power and provided insights into the
accuracy of predictions related to negative sales growth (NSG) and employee
dissatisfaction (EDS) on the firm's competitive disadvantages (FCD).
Data Analysis and Results

19. • Lack of governance in AI integrated business analytics systems can lead to poor data
quality and inadequate employee training, potentially resulting in the adoption of
inappropriate solutions.
• Risks associated with inadequate governance can influence firms to make
inappropriate business decisions, negatively impacting operational efficiency and
competitive advantage.
• Good governance is essential for improving data quality and providing effective
employee training to ensure appropriate AI integrated business analytics solutions.
• The study considered dimensions such as PDQ, LOG, and INT for AI integrated
business analytics operationalization, highlighting the importance of technology and
security risk as reflective subdimensions of perceived risk.
• Findings supported the conceptual relationships proposed in the study, emphasizing
the impact of inappropriate business decisions on operational inefficiency and
competitive disadvantages within firms.
Discussion

20. Theoretical Implications
• Theoretical contributions of the study include the development of a conceptual model that elucidates the adverse effects of adopting
inappropriate AI integrated business analytics solutions.
• Drawing on big data analytics literature and theories like RBV, DCV, and CT, the study identified three clusters of factors: flawed
technology strategy, risk environment due to inappropriate AI integration, and negative firm performance.
• Components of a flawed technology strategy, such as AI-BA opacity, encompass lack of governance, poor data quality, and inefficient
training, highlighting the importance of these factors in understanding operational inefficiency.
• The study emphasizes the significance of governance in AI systems, data quality, and employee training to mitigate risks and ensure the
successful implementation of AI integrated business analytics solutions.
• The conceptual model provides insights into the relationships between AI-BA opacity, suboptimal decisions, perceived risks, and their
impact on firm performance and competitive advantage.
Practical Implications
• The study's findings offer several primary implications for managers involved in designing and deploying AI integrated business analytics systems to
enhance operational efficiency and gain competitive advantages.
• Proper deployment of AI integrated BA systems is crucial to replace manual or heuristic-based solutions, emphasizing the importance of robust governance,
data quality, and employee training.
• Addressing AI-BA opacity through governance, data quality improvements, and effective training can help prevent the adoption of inappropriate solutions
that may negatively impact operational efficiency and competitive advantage.
• The study underscores the need for managers to focus on governance, data quality, and training to ensure the effectiveness ofAI integrated BA solutions and
avoid risks associated with inadequate governance.
• By implementing appropriate governance practices and ensuring data quality and training, firms can enhance decision-making processes, mitigate risks, and
improve operational efficiency, leading to sustained competitive advantages.
Implications

21. • The study's limitations include a focus solely on the service industry, neglecting other
sectors like manufacturing, which may limit the generalizability of the findings.
• The sample size of 355 usable responses may restrict the robustness of the results,
suggesting the need for larger sample sizes in future research.
• The study primarily addressed governance and management-related issues,
overlooking technical aspects of AI systems, which could be a valuable area for
exploration in future studies.
• The research focused on operational inefficiency and competitive disadvantage
resulting from opaque AI-BA solutions, neglecting other potential impacts on finance
or reputation within firms.
• Responses were gathered from managers in the service sector in India, where some
firms had not adopted AI integrated BA solutions, potentially skewing the
perspectives presented in the study.
Limitations

22. • Future research directions could involve expanding data collection to include the
manufacturing sector and comparing findings with the service sector to explore
potential differences in the impact of AI integrated BA solutions.
• Researchers may consider increasing the sample size beyond 355 usable responses to
better understand the moderating effects of certain variables on the relationships
studied.
• Future studies could delve into technical issues related to AI systems, exploring the
black box of emerging AI tools to enhance understanding and address potential
challenges.
• Researchers could investigate additional consequences of adopting opaque AI-BA
solutions beyond operational inefficiency and competitive disadvantage, such as
impacts on finance or reputation within firms.
• Exploring the perspectives of firms that have not yet adopted AI integrated BA
solutions and those in the early stages of implementation could provide valuable
insights for future research in this area.
Future Directions

23. • The study emphasizes the importance of proper governance, data quality, and employee training in the
successful deployment of AI integrated business analytics solutions to enhance operational efficiency
and gain competitive advantages.
• Addressing AI-BA opacity through robust governance practices and ensuring data quality and training
can help prevent the adoption of inappropriate solutions that may lead to negative impacts on firm
performance.
• The findings highlight the significance of understanding the components and effects of AI-BA opacity
to improve decision-making processes and mitigate risks associated with inadequate governance.
• By focusing on governance, data quality, and training, firms can enhance decision-making capabilities,
reduce operational inefficiencies, and maintain competitive advantages in dynamic IT environments.
• The study contributes to the discourse on "explainable AI" and underscores the importance of
transparency and accountability in AI systems to avoid unintended outcomes and ensure the effective
utilization of intelligent machines in decision-making processes.
Conclusion

24. Thank You !!!