Impact of Artificial Intelligence on Informed Business Decisions Using Social Media Apps in India: A TOE Framework Analysis

 

Dr. Joy Samuel Dhanraj. G ¹, Valentina Puffi Graf. G ²

 

¹ Assistant Professor, Department of Business Administration Loyola College, Chennai – 34, India  

² Assistant Professor, Department of Artificial Intelligence and Data Science, Meenakshi Sundararajan Engineering College, Chennai, India

 

1. INTRODUCTION

The digital transformation landscape in India has witnessed unprecedented growth, with social media becoming an integral part of the business ecosystem. As of 2024, India boasts over 755 million social media users, representing a fertile ground for businesses to gather consumer insights, track market trends, and engage with customers. However, the sheer volume, velocity, and variety of data generated through these platforms’ present significant challenges for meaningful analysis. Artificial Intelligence (AI) has emerged as a potential solution to navigate this data complexity, offering capabilities to process unstructured data, identify patterns, and generate actionable business intelligence.

The Technology-Organization-Environment (TOE) framework, first proposed by Tornatzky &Fleischer (1990), provides a comprehensive lens to understand the adoption of technological innovations within organizational contexts. This framework is particularly relevant for examining AI integration with social media analytics in India, where technological infrastructure, organizational capabilities, and environmental factors vary significantly across different business segments. Despite India's position as an emerging technology hub with a rapidly evolving digital ecosystem, research on AI adoption for business intelligence through social media remains limited, especially studies addressing the unique characteristics of the Indian market.

 

2. Significance of the Study

This research addresses several critical gaps in current understanding. First, while studies on AI adoption exist within Western contexts, there is limited empirical evidence regarding implementation patterns in emerging economies like India, where technological infrastructure, digital literacy, and regulatory frameworks differ significantly. Second, social media usage patterns in India present unique characteristics shaped by cultural, linguistic, and regional diversities that require specialized AI approaches different from global standards. Third, the interaction between technological capabilities, organizational readiness, and environmental pressures requires systematic investigation to develop effective adoption strategies.

The findings from this study will benefit multiple stakeholders. For business leaders, it provides a roadmap for AI integration with social media analytics to enhance decision-making processes. For technology developers, it highlights India-specific requirements for AI solutions that can effectively process multilingual content and cultural nuances. For policymakers, it offers insights into creating supportive regulatory environments that balance innovation with data protection. Additionally, academic researchers gain a theoretical framework for investigating technological adoption in emerging market contexts.

 

3. Research Questions

This study addresses the following research questions:

1)     What technologies affect AI adoption for social media analytics in Indian businesses? 

2)     How do organizational factors influence AI use in social media intelligence? 

3)     What external factors impact AI deployment for business intelligence in India? 

4)     How does AI-powered social media analytics improve business decision-making?

 

3.1. Objectives of the Study

The primary objectives of this research are:

1)     Identify key technologies factors AI adoption for social media analytics in businesses. 

2)     Assess organizational factors affecting AI implementation and use. 

3)     Examine external influences on AI adoption in India. 

4)     Measure AI’s impact on business decision-making. 

5)     Provide strategic recommendations for businesses, tech providers, and policymakers.

 

4. Literature Review

The Technology Organization-Environment (TOE) framework by Tornatzky & Fleischer (1990) provides a broad perspective on technology adoption in organizations. It highlights three key factors technological, organizational, and environmental that shape adoption decisions Baker, 2012. Unlike models like TAM or UTAUT, which focus on individuals, the TOE framework examines adoption at the organizational level, making it ideal for studying AI integration in social media analytics Oliveira & Martins (2011).

 

5. Technological Context

The technological context includes both internal and external technologies relevant to a business, such as existing systems and new market innovations Gutierrez et al. (2015). Key factors influencing AI adoption include relative advantage, complexity, compatibility, and observability Rogers (2003), Alshamaila et al. (2013). For AI in social media analytics, relative advantage refers to AI’s benefits over traditional data analysis.  Chatterjee et al. (2021) found that Indian firms adopted AI when they saw clear benefits in handling large-scale social media data. However, complexity can be a barrier Rana et al. (2019) noted that SMEs in India struggled with AI adoption due to technological difficulties.  Compatibility is also crucial, Kumar et al. (2020) highlighted that seamless integration with existing systems played a key role in AI implementation.  Additionally, data security and privacy concerns impact AI adoption in India.  Dwivedi et al. (2021) and Sengupta & Sahay (2018) found that businesses were cautious about AI due to evolving data protection regulations.

 

6. Organizational Context

The organizational context includes a company’s size, structure, resources, and management processes, which influence technology adoption Tornatzky & Fleischer (1990).  Organizational readiness, including financial and technological resources, plays a key role in AI adoption Gangwar et al. (2015). In India, Balasubramanian et al. (2022) found that financial constraints hindered smaller businesses, while larger firms invested more in AI for social media analytics.   Top management support is another crucial factor.  Chatterjee & Bhattacherjee (2020) showed that leadership commitment significantly impacted AI adoption in Indian businesses. Additionally, digital literacy and technical expertise among decision-makers influenced implementation success Mittal & Sharma (2022).   Organizational culture, including innovation and risk tolerance, affects AI adoption.  Venkatesh et al. (2020) found that firms encouraging experimentation were more likely to adopt AI for social media analytics. Furthermore, alignment with business strategy played a role, as organizations were more likely to adopt AI when it supported their strategic goals Verma & Bhattacharyya (2017).

 

7. Environmental Context

The environmental context includes industry characteristics, market structure, regulations, and infrastructure, all of which impact technology adoption Baker (2012).  Competitive pressure is a key driver Singh & Chopra (2020) found that rising competition boosted AI adoption in India.  Service-oriented industries showed higher adoption rates Patil & Baligar (2019).  Regulations also play a role.  Kshetri (2020) noted that India's evolving data policies influenced AI decisions, while Nambisan et al. (2019) found that regulatory uncertainty caused hesitation.

 External support from vendors and consultants’ aids adoption.  Kapur et al. (2021) found that localized AI solutions helped Indian firms integrate AI effectively. Additionally, network effect influenced adoption—businesses were more likely to adopt AI when peers had already implemented similar solutions Gupta & Bose, (2019).

AI and Social Media Analytics for Business Decision-Making

AI enhances social media analytics in several key ways: sentiment analysis, trend identification, competitive intelligence, and crisis management Kumar & Sharma (2019). Sentiment analysis helps businesses understand customer emotions using NLP and machine learning Liu (2020), Ghani et al. (2019). Trend identification detects shifts in consumer behavior, crucial in India's fast-changing market (Drus & Khalid (2019), Srivastava & Arya (2022). Competitive intelligence provides insights into competitor strategies and market positioning Grover et al., (2020). AI improves decision-making by reducing information overload, uncovering hidden patterns, and enabling real-time responses Cao et al. (2019), Sivarajah et al., (2017), Behl et al. (2019).

In India, AI adoption for social media analytics faces unique challenges. Linguistic diversity complicates AI processing since most models are designed for English Jha & Mamidi (2017), Kunchukuttan et al. (2020). Cultural nuances influence social media behavior, requiring AI to interpret traditional and modern references accurately (Arora & Malik (2021), Shah et al. (2020). The digital divide also affects AI-driven insights, as rural areas have lower digital literacy and connectivity, skewing analytics Agarwal & Garg (2022), Mehta & Joshi, (2021). Additionally, India’s evolving regulations on data protection and AI governance impact adoption, requiring compliance with emerging policies (Kshetri (2020), Agarwal et al. (2021).

Conceptual Model and Hypothesis Development

Based on the literature review and the TOE framework, a conceptual model is proposed to guide this research Figure 1 The model illustrates the relationships between technological, organizational, and environmental factors and their collective influence on AI adoption for social media analytics, which subsequently impacts business decision-making performance.

H1: Perceived usefulness positively influences the adoption of AI for social media analytics.

H2: Perceived Ease of Use influences the adoption of AI for social media analytics.

H3: Decision Quality positively influences the adoption of AI for social media analytics.

H4: Decision Speed influence the adoption of AI for social media analytics.

H5: Data Utilization influence the adoption of AI for social media analytics.

H6: Social media Influence positively influences the adoption of AI for social media analytics.

H7: Adoption of AI Influence positively influences the adoption of AI for social media analytics.

This conceptual framework and the associated hypotheses provide a comprehensive foundation for examining AI adoption for social media analytics in the Indian business context, incorporating technological, organizational, and environmental dimensions consistent with the TOE framework.

Figure 1

Figure 1 Technology Organization-Environment (TOE), Tornatzky and Fleischer (1990)

 

8. RESEARCH METHODOLOGY

This study employs a quantitative research approach with cross-sectional primary data from industry professionals, with descriptive and inferential statistical methods establishing relationships between technological, organizational, and environmental factors influencing AI adoption. The study targets business professionals, marketing analysts, and decision-makers using a stratified random sampling method, with a sample size of 400 respondents determined via Cochran’s formula. Data is gathered through online surveys and interviews, supplemented by secondary sources. Variables are categorized under the TOE framework, where technological factors include AI capabilities like sentiment analysis and predictive analytics, organizational factors cover firm size, leadership support, and digital maturity, and environmental factors involve regulatory policies, market competition, and customer expectations.

Informed business decisions serve as the dependent variable, measured through market trend analysis, customer behavior prediction, sentiment analysis, and competitive intelligence. AI helps businesses identify trends, anticipate customer needs, and monitor competitors, improving decision-making and competitiveness. Data analysis involves descriptive statistics, reliability tests - Cronbach’s Alpha, Correlation and multiple regression analysis to evaluate AI’s influence on business intelligence.

 

 

 

8.1. Data Analysis

Table 1

 

The descriptive statistics reveal that AI adoption levels vary significantly across businesses, with some fully utilizing AI while others lag behind. Decision-making quality and speed show noticeable differences, indicating that while AI enhances efficiency for some, others struggle with implementation. Social media influence and data utilization have a measurable impact on business performance, with companies leveraging AI-driven insights experiencing better engagement. However, businesses that adopt AI without effectively integrating data-driven strategies may not see substantial benefits. 

ROI analysis suggests that AI adoption alone does not guarantee profitability; instead, the way AI is utilized in decision-making plays a crucial role. A high variance in ROI indicates that while some businesses achieve significant gains through AI, others fail to capitalize on its full potential. Effective AI integration in decision-making, customer engagement, and data analytics is key to maximizing returns. Ultimately, businesses should focus on strategic AI implementation rather than just adoption to drive meaningful financial outcomes.

 

8.2. Regression Analysis

Table 2

Table 2 Hierarchical Regression
Particulars	Beta	Std Er	t value	Sig
const	-56.6055	12.595	-4.494	0.000*
AI Adoption Level	0.2428	0.444	0.547	0.584
Perceived Usefulness	-0.4188	0.483	-0.867	0.386
Perceived Ease of Use	0.2054	0.418	0.491	0.623
Decision Quality	2.8852	1.432	2.015	0.045*
Decision Speed	0.9478	1.547	0.613	0.54
Data Utilization	5.2063	1.507	3.454	0.001*
Social Media Influence	2.3223	1.517	1.531	0.127
Overall Satisfaction	3.115	1.654	1.883	0.060*
Note: N = 400, *p<.05, Const – ROI Business Decisions
Source Primary Data, Spss Output)

 

The regression analysis provides valuable insights into the factors influencing the dependent variable. The constant (intercept) is -56.6055, with a statistically significant p-value of 0.0000. This suggests that when all independent variables are set to zero, the predicted dependent variable value is significantly different from zero. However, the focus remains on the influence of the independent variables.

AI adoption level has a positive coefficient (β = 0.2428), but its effect is not statistically significant (p = 0.584), indicating that AI adoption does not strongly predict the dependent variable. Similarly, perceived usefulness has a negative coefficient (β = -0.4188), but with a p-value of 0.386, it does not show a significant impact. Perceived ease of use follows the same pattern, with a positive coefficient (β = 0.2054) but an insignificant p-value (0.623), suggesting it does not play a critical role in shaping the dependent variable.

Among the studied factors, decision quality (β = 2.8852, p = 0.045) and data utilization (β = 5.2063, p = 0.001) emerge as significant predictors. The positive and statistically significant coefficients indicate that better decision quality and effective data utilization have a meaningful impact on the dependent variable. Decision speed, on the other hand, has a positive but insignificant effect (β = 0.9478, p = 0.54), suggesting that making faster decisions does not strongly influence the outcome.

Social media influence (β = 2.3223, p = 0.127) and overall satisfaction (β = 3.115, p = 0.06) both exhibit positive coefficients. However, their p-values indicate that their effects are not statistically significant, with overall satisfaction being marginally significant at 0.06. This suggests that while overall satisfaction may have some influence, it does not meet the strict significance threshold.

 

9. Conclusion

The findings underscore that AI adoption alone does not ensure business success; rather, its strategic integration into decision-making and data utilization is crucial. While some companies effectively leverage AI for improved efficiency and engagement, others struggle due to poor implementation. The regression analysis further emphasizes that decision quality and data utilization are the most significant factors influencing business performance, while AI adoption, perceived usefulness, and decision speed show little statistical significance. Businesses seeking to maximize their returns should prioritize refining data-driven decision-making processes and optimizing AI implementation strategies rather than merely adopting AI technologies.

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

 

 

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