i have attached a latex code of overleaf with you its my reasech paper file i have create it in the format of 2 coloumns on a page.now i want to convert the paper in single coloumn format for me do it and alos make sure the tables and the graphs alos get format accordengli.make sure the tables will not reamin same like now the tables are long in length because i have follow the 2 coloumn format but now i need it into the single coloumn format of word so use the width of the page and make the tables and the graphs. dont miss use the space dont leave empty space also.give me the full latex code of single coloumn with proper format and full content of text,table,graphs.latex code - \documentclass[ journal=largetwo, manuscript=article-type, year=2025, volume=1,]{cup-journal}\usepackage{amsmath}\usepackage[nopatch]{microtype}\usepackage{booktabs}\usepackage{graphicx}\usepackage{xcolor}\usepackage{tikz}\usepackage{pgfplots}\pgfplotsset{compat=1.18}\usepackage{float}\usepackage{setspace}\usepackage{parskip}\usepackage{siunitx}\usepackage{multirow}\usepackage{array}\usepackage{colortbl}\usepackage{fancyhdr}\usepackage{caption}\usepackage{subcaption}\usepackage{natbib}\usepackage{hyperref}\usepackage{enumitem}\usepackage{tabularx}\usepackage{threeparttable}\usepackage{longtable}\usepackage{lscape}% === Suppress CUP metadata header ===\usepackage{etoolbox}\makeatletter\patchcmd{\@maketitle} {\@typesetjournalinfo} {}{}{}\patchcmd{\@maketitle} {\@typesetarticletype} {}{}{}\makeatother% Hyperref setup for clean PDF output\hypersetup{ colorlinks=false, hidelinks=true, pdfborder={0 0 0},}% Improved color definitions for charts\definecolor{sbicolor}{RGB}{0, 91, 170}\definecolor{hdfccolor}{RGB}{237, 28, 36}\definecolor{icicicolor}{RGB}{236, 0, 140}\definecolor{liccolor}{RGB}{0, 175, 80}\definecolor{gicolor}{RGB}{255, 127, 0}% Spacing and layout settings for enhanced readability\setlength{\parskip}{10pt}\setlength{\parindent}{0pt}\linespread{1.15}% Header and footer configuration\pagestyle{fancy}\fancyhf{}\fancyhead[R]{\thepage}\renewcommand{\headrulewidth}{0pt}% Caption formatting for figures and tables\captionsetup{font=small, labelfont=bf, justification=justified, singlelinecheck=false}\title{Social Media Engagement: Evidence from the Insurance Sector - A Multi-Case Study Approach}\author{Vinisha Bhagwani}\affiliation{Department of Management Studies, Sardar Patel Institute of Technology, Mumbai}\author{Daksh Jain}\affiliation{Department of Management Studies, Sardar Patel Institute of Technology, Mumbai}\author{Ronit Sonwane}\affiliation{Department of Management Studies, Sardar Patel Institute of Technology, Mumbai}\author{Madhav Shah}\affiliation{Department of Management Studies, Sardar Patel Institute of Technology, Mumbai}\author{Esha Chavan}\affiliation{Department of Management Studies, Sardar Patel Institute of Technology, Mumbai}\keywords{emotional engagement, social media marketing, sentiment analysis, digital consumer behavior, insurance sector, mixed-methods research, multi-case study, trustworthiness}\begin{document}\begin{abstract}In an increasingly digitalized landscape, the engagement of consumers with intangible financial products, such as insurance, presents unique challenges and opportunities. This multi-case study investigates the pivotal role of emotional engagement in reshaping marketing strategies within the Insurance sector through a comprehensive mixed-methods approach. We analyzed over 1,500 social media posts and conducted 15 semi-structured interviews with industry professionals across different organizational levels to ensure methodological triangulation and enhance trustworthiness. Our research design emphasizes credibility through prolonged engagement, transferability through thick description, dependability through systematic documentation, and confirmability through reflexive practices. The findings reveal that emotionally resonant content significantly fosters higher engagement, with discernible variations across demographic segments and platforms. By integrating insights from sentiment analysis, behavioral economics, and digital marketing, this research uncovers substantive shifts in the communication of financial products in the digital sphere. The implications extend beyond the insurance sector, offering valuable perspectives for diverse industries striving to establish meaningful connections with consumers in the contemporary digital environment.\end{abstract}\section{Introduction}\label{sec:introduction}The insurance industry is currently navigating a period of profound transformation, characterized by a strategic shift from conventional, face-to-face service paradigms towards dynamic, digitally-mediated customer engagement models. Social media platforms, notably Instagram and Twitter, have emerged as central conduits in this transition, empowering insurers to articulate complex value propositions through evocative emotional storytelling, facilitate real-time interactions, and leverage platform-specific content formats. Within this evolving ecosystem, marketing transcends its traditional peripheral function to become a strategic instrument for cultivating trust and achieving market differentiation.This digital transition, however, is not a monolithic phenomenon. Insurance firms exhibit distinct trajectories of digital adoption and strategic experimentation, profoundly influenced by their internal capabilities, legacy system constraints, and the dynamically evolving expectations of consumers. A comprehensive understanding of these diverse pathways necessitates an analytical approach that extends beyond the mere quantification of engagement metrics. It demands a processual perspective, examining how firms progressively align their marketing initiatives, technological infrastructure, and overarching strategic intent over extended periods.This study explores how leading insurance companies have strategically utilized social media platforms to emotionally engage their customer base, construct compelling brand narratives, and navigate their respective digital transformation journeys. Diverging from traditional marketing paradigms that predominantly emphasize transactional promotions, these firms increasingly deploy sophisticated content strategies designed to evoke core human values such as family security, personal resilience, and prospective optimism. Such campaigns signify not merely a tonal adjustment in communication but reflect a more profound recalibration in how digital platforms are perceived, integrated, and operationalized within the organizational framework.To investigate these phenomena, we employ a robust multi-case research design with a two-tiered approach: a qualitative content analysis of the selected firms' activities on Twitter and Instagram, complemented by semi-structured interviews with senior marketing professionals across multiple organizational levels. This dual-methodological approach enables a nuanced linkage between externally manifested communication strategies and the internal organizational logics, adaptive processes, and institutional learning that underpin them.Our principal aim is to delineate the digital transformation trajectories of these firms, conceptualizing them not as isolated initiatives but as evolving, path-dependent sequences shaped by continuous experimentation, iterative customer feedback, and the affordances inherent in digital platforms. By identifying the salient emotional and strategic patterns embedded within their social media engagement practices, this research offers critical insights into how traditional industries are reimagining customer interaction paradigms in the digital age.Ultimately, this paper proposes a behavior-centered model of social media transformation specific to insurance marketing. It contributes to the academic discourse on digital adaptation and provides actionable insights for firms seeking to embed emotional intelligence and platform fluency within their broader organizational transformation endeavors.\section{Theoretical Background}\label{sec:theoretical_background}Digital transformation within traditional industries is seldom a linear or uniform process. Instead, it typically unfolds through a complex sequence of interpretations, strategic trials, and adaptive responses that reflect how organizations engage with and internalize emerging technologies over time. To capture this inherent complexity, this study adopts a trajectory-based analytical lens, substantively informed by affordance theory \citep{Gibson1979, Zammuto2007}.Affordance theory posits that actors perceive and act upon the potential uses and possibilities that technologies offer within specific socio-technical contexts. In the context of this research, social media platforms such as Twitter and Instagram present a range of affordances—including interactive features, visual communication capabilities, and real-time engagement mechanisms—which insurance firms interpret and leverage in distinct ways. These interpretations and subsequent actions are contingent upon factors such as their existing digital maturity, overarching organizational objectives, and specific customer engagement strategies.A trajectory perspective allows for the conceptualization of social media adoption not as a singular, discrete event but as an ongoing, iterative process. Insurance firms may commence with a foundational online presence, gradually progress towards employing emotionally resonant storytelling, and ultimately endeavor to embed social media engagement deeply within their broader strategic identity. This progression illustrates how technological affordances are initially perceived, subsequently tested through practical application, and finally institutionalized within the organizational structure and culture.By examining our multi-case study through this theoretical framework, we can effectively compare how different firms respond to similar technological possibilities in varied manners. These responses are shaped by internal resource configurations, leadership vision, and evolving interpretations of the strategic value that social media can confer upon customer engagement. This theoretical framing enables a departure from static models of digital marketing, facilitating instead an understanding of how firms co-evolve with technology through continuous interaction, organizational learning, and strategic adaptation.In applying this approach to the insurance sector, our analysis focuses on how emotional marketing narratives, delivered through platform-native content formats, emerge as key affordances that reshape the modalities through which insurers connect with their customers. Consequently, affordance theory serves to ground our analysis of strategy, organizational structure, and narrative construction in a coherent and dynamic model of digital transformation.\section{Literature Review}\label{sec:literature_review}The discourse on digital transformation in the insurance sector has evolved significantly, extending beyond operational automation and backend system upgrades to encompass customer-facing strategies, particularly in marketing and brand communication. The extant literature increasingly conceptualizes digital platforms not merely as promotional tools but as active sites of transformation, profoundly shaping how firms interpret consumer behavior, define value propositions, and evolve their market identity.\subsection{From Transactional Logic to Emotional Narratives in Insurance Marketing}Early research on insurance marketing predominantly emphasized rational decision-making, focusing on pricing, product features, and risk mitigation as primary levers for customer acquisition \citep{Gardan2015}. Over time, this perspective was augmented by studies recognizing the significant emotional and psychological dimensions inherent in financial decision-making processes \citep{Kahneman2011}. Consequently, emotional storytelling, evocative visual cues, and sophisticated narrative framing have become increasingly central to how insurers differentiate themselves in a content-saturated and highly competitive marketplace. Platforms such as Instagram and Twitter have been instrumental in facilitating this shift, offering creative formats that effectively blend immediacy with relatability, thereby enabling more nuanced emotional connections with consumers \citep{Singh2024}.\subsection{Social Media as a Catalyst for Strategic Transformation}More recent scholarship positions social media not only as a communication channel but as a potent catalyst for broader strategic transformation within organizations \citep{Kumar2023}. Firms interpret the affordances of social media platforms such as hashtags, interactive reels, and threaded discussions as opportunities to experiment with novel forms of engagement, reconfigure customer relationships, and test the efficacy of various emotional appeals. These interpretations and subsequent strategic deployments vary considerably, contingent upon an organization's internal culture, level of digital maturity, and willingness to adopt agile marketing paradigms \citep{Sharma2022}. This observed divergence necessitates a longitudinal, comparative research approach to understand how digital strategies are formulated, implemented, and refined over time.\subsection{Trajectory and Affordance-Based Perspectives on Digital Transformation}To elucidate these divergent strategic pathways, trajectory-based models offer a valuable analytical lens. Rather than presupposing a singular, uniform path to digital maturity, these models emphasize how firms navigate through distinct phases of interpretation, experimentation, and eventual institutionalization of digital tools and practices \citep{Zammuto2007}. Affordance theory complements this perspective by highlighting that technologies do not unilaterally determine outcomes; instead, they offer a spectrum of possibilities that organizational actors selectively enact based on their specific goals, accumulated experience, and contextual constraints \citep{Gibson1979}.Collectively, these theoretical perspectives suggest that the adoption of emotional storytelling in insurance marketing is not a mere emulation of industry best practices. Rather, it is the outcome of an evolving internal organizational process a dynamic co-creation of meaning involving the firm, the digital platform, and the consumer. A comprehensive understanding of this process requires an analytical approach that examines both externally directed communications (e.g., campaigns, posts, engagement metrics) and internal reflective processes (e.g., strategic choices, experimentation protocols, managerial learning).\subsection{Multi-Case Study Research in Digital Marketing}Multi-case study methodology has emerged as a particularly valuable approach for investigating complex organizational phenomena in digital marketing contexts. This methodology enables researchers to explore variation across cases while maintaining analytical depth within each case, facilitating both pattern recognition and contextual understanding.In digital marketing research, multi-case studies have proven effective for examining how different organizations interpret and implement similar technologies, revealing the role of organizational factors in shaping digital transformation outcomes \citep{Vial2019}. The methodology's strength lies in its ability to capture the processual nature of digital transformation while enabling cross-case comparison and theory building.\subsection{Rationale for the Present Study}Despite a burgeoning academic interest in digital consumer behavior, existing literature frequently treats social media campaigns as static phenomena, often detached from the underlying firm-level dynamics and strategic decision-making processes. There remains a discernible paucity of integrated studies that systematically examine how multiple insurance firms interpret and implement emotional engagement strategies across diverse social media platforms. Moreover, few studies have combined rigorous content analysis with in-depth qualitative insights from industry practitioners to explore how emotional tactics are internally justified, iteratively refined, or potentially resisted within organizational contexts.This paper aims to address these identified lacunae through a multi-case study of five prominent insurance providers. By meticulously mapping their digital transformation trajectories as manifested through social media engagement, and by analyzing both their public-facing content and internal strategic narratives captured via interviews, we seek to understand how emotional marketing is conceptualized, deployed, and institutionalized within the broader journey of digital adaptation in the insurance sector.\section{Methodology}\label{sec:methodology}This study employs a multi-case research design to explore how major insurance firms conceptualize, implement, and institutionalize emotional engagement strategies across various social media platforms. Consistent with a trajectory-based view of digital transformation, our objective extends beyond identifying prevailing best practices to understanding the intricate processes through which firms interpret emotional affordances, experiment with diverse content formats, and learn from consumer responses over time.\subsection{Research Design and Philosophical Approach}This research adopts a convergent parallel mixed-methods design \citep{Creswell2018}, where quantitative and qualitative data are collected and analyzed concurrently, with integration occurring during the interpretation phase. The study is grounded in a pragmatic philosophical approach, emphasizing practical problem-solving and the use of multiple methods to address complex research questions \citep{Morgan2014}.The multi-case study design follows established guidelines for case study research \citep{Yin2018}, incorporating multiple sources of evidence, systematic data collection procedures, and rigorous analytical techniques. This approach enables both within-case analysis and cross-case pattern identification while maintaining contextual richness.\subsection{Trustworthiness and Research Quality}Following Lincoln and Guba's \citep{Lincoln1985} framework for establishing trustworthiness in qualitative research, this study implements multiple strategies to enhance research quality:\textbf{Credibility} is established through:\begin{itemize}\item Methodological triangulation using multiple data sources\item Prolonged engagement with data collection over 18 months\item Member checking with interview participants\item Peer debriefing sessions with research colleagues\end{itemize}\textbf{Transferability} is enhanced through:\begin{itemize}\item Thick description of research contexts and procedures\item Detailed case profiles and organizational characteristics\item Explicit discussion of scope and limitations\item Clear articulation of theoretical frameworks\end{itemize}\textbf{Dependability} is ensured through:\begin{itemize}\item Systematic documentation of all research procedures\item Detailed audit trails for data collection and analysis\item Inter-rater reliability testing for coding procedures\item Consistent application of analytical frameworks\end{itemize}\textbf{Confirmability} is achieved through:\begin{itemize}\item Reflexive journaling throughout the research process\item Systematic bias checking procedures\item External auditing of analytical procedures\item Transparent reporting of limitations and potential biases\end{itemize}\subsection{Case Selection, Sampling Justification, and Scope}\label{ssec:case_selection}We selected five leading insurance providers operating within the market: Life Insurance Corporation (LIC), SBI Life Insurance, HDFC Life Insurance, ICICI Prudential Life Insurance, and General Insurance Corporation (GIC Re). The selection was guided by a purposive sampling strategy \citep{Patton2015}. These firms were chosen based on several criteria: their significant market presence and brand recognition, established and active digital engagement across multiple social media platforms (primarily Instagram and Twitter), and their representation of diverse strategic approaches to customer communication, encompassing both public and private sector entities, as well as life and general insurance segments.This purposive approach facilitates a rich comparative analysis of varied digital transformation trajectories and emotional engagement strategies within the specific context of the insurance sector. While the theoretical framework possesses broad applicability, anchoring the study within a single national setting ensures cultural coherence and enhances interpretive depth. Each selected case offers a distinct approach to emotional marketing—ranging from humor and aspiration to reassurance and risk awareness—allowing for a nuanced comparison and contrast of strategic trajectories rather than treating digital behavior as a homogeneous phenomenon.\subsection{Multi-Data Sources Integration}Following Yin's \citep{Yin2018} recommendations for enhancing construct validity, this study systematically integrates multiple data sources to provide comprehensive insights into the phenomenon under investigation.\begin{table}[H]\centering\scriptsize\setlength{\tabcolsep}{2pt}\renewcommand{\arraystretch}{0.9}\caption{Multi-Data Sources Integration Matrix}\label{tab:data_sources}\begin{tabular}{@{}p{1.7cm}p{2.1cm}p{1.6cm}p{1.2cm}p{1.8cm}@{}}\toprule\textbf{Data Source} & \textbf{Primary Purpose} & \textbf{Collection Period} & \textbf{Sample Size} & \textbf{Analysis Method} \\\midruleSocial Media Posts & Behavioral patterns & Jan 2022--Dec 2024 & 1,500 & Content analysis \\Interview Transcripts & Strategic insights & Mar 2024--Aug 2024 & 15 & Thematic analysis \\Engagement Metrics & Performance measurement & Jan 2022--Dec 2024 & 1,500 & Statistical analysis \\Company Documents & Strategic context & 2022--2024 & 25 & Document analysis \\Industry Reports & Market context & 2022--2024 & 12 & Secondary analysis \\\bottomrule\end{tabular}\vspace{0.2em}\floatfoot{\textit{Note:} All data sources were systematically triangulated to enhance validity and reliability of findings.}\end{table}\vspace{-0.8em}\subsection{Data Collection}\label{ssec:data_collection}Our dataset reflects our mixed-methods approach and incorporates multiple data sources:\textbf{Digital Content Analysis:} We systematically collected 1,500 social media posts (including associated metadata such as likes, shares, comments, hashtags, and timestamps) from the official Instagram and Twitter accounts of the five selected firms. The data spans from January 2022 to December 2024, retrieved using authenticated Application Programming Interfaces (APIs) to ensure data integrity and comprehensiveness. The content collected included textual captions, visual elements (images and videos), and interactive components like calls-to-action.\textbf{Semi-Structured Interviews:} To complement the digital trace data, we conducted semi-structured interviews with industry professionals (n=15) involved in marketing, digital strategy, and customer engagement functions within the selected or comparable firms. These interviews were designed to uncover the internal logics and strategic rationales behind observed emotional strategies and to contextualize content patterns within broader organizational goals and digital transformation efforts.\textbf{Organizational Documents:} We collected and analyzed internal documents including marketing strategy reports, social media guidelines, performance dashboards, and training materials (n=25 documents) to understand organizational contexts and strategic frameworks.\textbf{Industry Reports and Secondary Data:} We incorporated relevant industry reports, market analyses, and regulatory documents (n=12 reports) to provide broader contextual understanding of the insurance sector's digital transformation.\subsection{Interview Protocol and Participant Profile}\label{ssec:interview_protocol}The semi-structured interviews formed a crucial component of our qualitative data collection, designed to elicit rich insights into the strategic thinking and operational practices underpinning social media engagement in the insurance sector.\textbf{Interview Structure and Ethics:} Each interview, lasting approximately 60-90 minutes, was guided by an interview protocol that covered themes such as: the firm's overall social media strategy, the role of emotional engagement, content development processes, target audience considerations, methods for measuring success, and perceived challenges and opportunities in digital marketing. While the protocol provided a consistent framework, its semi-structured nature allowed for flexibility to explore emergent themes and probe deeper into specific responses.All interviews were conducted remotely via video conferencing, audio-recorded with explicit consent from participants, and subsequently transcribed verbatim. Ethical considerations were paramount throughout the process. Participants were provided with a detailed information sheet about the study's objectives and how their data would be used. Informed consent was obtained prior to each interview, emphasizing their right to withdraw at any time and assuring them of anonymity and confidentiality. All data were stored securely and anonymized during analysis and reporting to protect participant identities.\textbf{Participant Selection and Profile:} Participants were selected using purposive sampling to ensure representation across different organizational levels, functional areas, and types of insurance companies. The selection criteria included:\begin{itemize}\item Senior-level involvement in digital marketing or social media strategy\item Minimum 3 years of experience in insurance sector digital marketing\item Direct involvement in content creation or strategy development\item Willingness to participate and provide informed consent\end{itemize}\begin{table}[htbp]\centering\caption{Profile of Interview Participants}\label{tab:participant_profile}\begin{tabularx}{\textwidth}{@{}lXXc@{}}\toprule\textbf{Participant ID} & \textbf{Role} & \textbf{Organization Type Focus} & \textbf{Years of Experience} \\\midruleP1 & Senior Marketing Manager & Large Private Life Insurer & 12 \\P2 & Head of Digital Strategy & Public Sector Life Insurer & 15 \\P3 & Social Media Lead & Mid-Sized Private General Insurer & 8 \\P4 & Chief Marketing Officer & Large Private Composite Insurer & 18 \\P5 & Customer Engagement Specialist & Reinsurance Firm & 10 \\P6 & Content Strategy Lead & Large Private Life Insurer & 7 \\P7 & Brand Manager & Public Sector Life Insurer & 11 \\P8 & Digital Marketing Specialist & Mid-Size Private Insurer & 6 \\P9 & Community Manager & Large Private Life Insurer & 5 \\P10 & Customer Service Manager & Public Sector Life Insurer & 14 \\P11 & Content Creator & Large Private Composite & 4 \\P12 & Analytics Manager & Large Private Life Insurer & 9 \\P13 & Regional Marketing Head & Public Sector Life Insurer & 16 \\P14 & Social Media Coordinator & Mid-Size Private Insurer & 3 \\P15 & Customer Experience Lead & Reinsurance Firm & 8 \\\bottomrule\end{tabularx}\floatfoot{\textit{Note:} Organization types are generalized to maintain anonymity. All participants provided informed consent.}\end{table}The 15 participants represented three organizational levels:\begin{itemize}\item Senior Management (n=5): Chief Marketing Officers, Heads of Digital Strategy\item Middle Management (n=5): Marketing Managers, Social Media Leads, Brand Managers\item Operational Level (n=5): Content Creators, Community Managers, Customer Service Representatives\end{itemize}\subsection{Analytical Approach}\label{ssec:analytical_approach}The analysis of the collected data proceeded in three distinct yet interconnected phases:\textbf{Phase 1: Qualitative Content Audit and Thematic Analysis:} We conducted an initial content audit of the social media posts to identify recurring emotional theme such as nostalgia, fear, optimism, trust, security, and aspiration across both visual and textual modalities. This involved a systematic coding process using both inductive and deductive approaches. Rather than relying solely on pre-trained sentiment analysis models, we triangulated our coding with insights derived from the practitioner interviews to ensure conceptual validity and contextual relevance. This interpretive grounding allowed us to link observed content patterns to managerial intentions and strategic considerations, rather than mere algorithmic classifications.Inter-rater reliability was established through independent coding by two researchers, achieving Cohen's kappa of 0.84, indicating substantial agreement \citep{Landis1977}.\textbf{Phase 2: Quantitative Engagement Analysis:} We assessed engagement outcomes across platforms by normalizing key metrics such as likes, comments, shares, and views relative to follower counts and temporal trends. This normalization enabled a more equitable comparison of emotional effectiveness across different firms and platforms, mitigating biases arising from platform-specific affordances or disparate audience sizes. Statistical analyses were performed using R software (version 4.3.0) to identify correlations between emotional content characteristics and engagement levels.\textbf{Phase 3: Trajectory Mapping and Cross-Case Synthesis:} Finally, we synthesized the findings from both the content analysis and the interviews to construct trajectory maps for each firm. These maps traced how their emotional engagement strategies evolved over the study period. Particular attention was devoted to identifying moments of strategic experimentation, content revision, and the routinization of successful practices—key indicators of institutionalization as described in digital transformation literature.Cross-case analysis followed Miles and Huberman's \citep{Miles2014} approach, involving the creation of cross-case displays, pattern matching, and the development of explanatory frameworks.\subsection{Rationale for Multi-Case, Mixed-Methods Design}\label{ssec:rationale_mixed_methods}A multi-case, mixed-methods research design was deliberately chosen for this study to foreground the variation in digital transformation trajectories across different insurance firms while simultaneously enabling in-depth exploration within each individual case. This study employs a convergent parallel mixed-methods design \citep{Creswell2018}, where quantitative and qualitative data are collected and analyzed concurrently, and the findings are integrated during the interpretation phase.This approach is particularly apposite for investigating complex socio-technical phenomena such as digital transformation in traditional industries \citep{Venkatesh2013}. The quantitative analysis of extensive social media data (1,500 posts) offers insights into the broad patterns, prevalence, and statistical significance of various emotional engagement strategies and their impact on consumer interaction metrics. This provides a macro-level view of "what" strategies are being employed and "how" effective they appear to be.Concurrently, the qualitative data derived from semi-structured interviews with industry professionals provide rich, contextual understanding of the "why" behind these strategies. These interviews illuminate the internal decision-making processes, strategic rationales, experiential learning, organizational culture, and perceived challenges that shape the firms' approaches to emotional engagement on social media.By systematically triangulating findings from these multiple data sources, the research aims to achieve a more comprehensive, nuanced, and robust understanding of how insurance companies conceptualize, implement, and institutionalize emotional engagement on social media than either method could yield in isolation. This integration enhances the validity and depth of our conclusions regarding digital adaptation in the insurance sector.\vspace{-0.8em} \section{Data \& Variables}\label{sec:data_variables}\subsection{Key Behavioral and Image-Based Variables}The operationalization of emotional engagement and its constituent elements required a carefully defined set of variables. Table~\ref{tab:variables} provides an overview of these key metrics, which encompass both textual sentiment and visual emotional cues, alongside standard engagement indicators.\begin{table}[htbp]\centering\caption{Overview of Key Behavioral and Image-Based Variables}\label{tab:variables}\small\renewcommand{\arraystretch}{1.1}\begin{tabularx}{\textwidth}{@{}lXcX@{}}\toprule\textbf{Variable} & \textbf{Definition} & \textbf{Range} & \textbf{Validation Method} \\\midruleSentiment Polarity & Net affective tone of textual content, ranging from negative to positive. & $[-1.0, +1.0]$ & TextBlob \& VADER ensemble pipeline; inter-rater reliability ($\alpha > 0.85$) \\Emotional Intensity & Combined magnitude of discernible emotion in both text and associated imagery. & $[0.0, 1.0]$ & Ensemble NLP+CV models; expert calibration against interview insights \\Visual Emotion Score & CNN-derived valence score for primary image frames. & $[0, 10]$ & VGG19 feature extraction; human coding inter-rater reliability ($\kappa > 0.75$) \\Color Saturation Index & Mean HSV saturation level of pixels corresponding to dominant brand colors. & $[0,100]$ & OpenCV color segmentation; inter-rater reliability ($\kappa > 0.80$) \\Facial Expression Count & Number of detected human faces exhibiting positive emotional expressions. & $\mathbb{N}$ & OpenCV face detection API cross-checked with emotion recognition API \\Saliency Focus Area & Proportion of image area within top 10\% of saliency scores. & $[0.0, 1.0]$ & Saliency map analysis; threshold validated on pilot dataset \\Engagement Rate & (Likes + Comments + Shares) / Impressions $\times$ 100\%. & \% & Platform API data; randomized subsample verification \\\bottomrule\end{tabularx}\end{table}In Table~\ref{tab:variables}, established metrics such as \emph{SentimentPolarity} and \emph{EngagementRate} are presented alongside novel, image‐centric constructs designed to capture nuanced visual and behavioral dynamics. For instance, \emph{ColorSaturationIndex} and \emph{SaliencyFocusArea} quantify how color intensity and focal regions within an image may drive viewer attention. Similarly, \emph{FacialExpressionCount} and \emph{VisualEmotionScore} measure the presence and perceived strength of emotional cues conveyed through imagery. Each variable is supported by robust validation procedures—including statistical reliability tests (e.g., Cronbach's alpha, Cohen's kappa), API confidence intervals, or expert calibration against qualitative data—ensuring that our analytical framework yields precise and interpretable insights into emotional engagement and digital consumer behavior within the social media marketing context of the insurance sector.\begin{table}[H]\centering\caption{High-Level Summary of Social Media Performance and Estimated Revenue Impact (Annualized)}\label{tab:overview}\scriptsize\setlength{\tabcolsep}{2pt}\renewcommand{\arraystretch}{0.95}\begin{tabular}{lcccccclc@{}p{1.2cm}@{}p{0.9cm}}\toprule\textbf{Company} & \textbf{IG} & \textbf{X} & \textbf{Leads} & \textbf{Conv.} & \textbf{Rev. SM} & \textbf{Total Rev.} & \textbf{Emp.} & \textbf{SM Rev.(\%)} & \textbf{CTA Style} & \textbf{Cred.} \\\midruleLIC & 220 & 300 & 6500 & 1.10 & 22.3 & 1070000 & 98661 & 0.02 & Nostalgic, Trust Appeal & Mod. \\SBI Life & 130 & 240 & 8100 & 2.40 & 42.8 & 1320180 & 23888 & 0.03 & Testimonials, Empathy & High \\HDFC Life & 250 & 210 & 11800 & 2.80 & 67.3 & 1019600 & 32486 & 0.08 & Aspirational, Benefits & High \\ICICI Pru & 160 & 180 & 10200 & 2.60 & 61.1 & 899320 & 18842 & 0.07 & Myth-Bust, Education & V.High \\GIC Re & 110 & 150 & 2300 & 1.40 & 10.1 & 371818 & 449 & 0.03 & Safety, Risk Awareness & Mod. \\\bottomrule\end{tabular}\vspace{0.1em}\begin{minipage}{\linewidth}\scriptsize\floatfoot{\textit{Note:} Revenue from Social Media (Rev. SM) and its percentage of Total Revenue are estimates based on industry reports and interview data. Total Revenue is indicative. Credibility Score is a composite measure based on perceived trustworthiness and authority from survey data and content analysis.}\end{minipage}\end{table}\section{Case Studies}\label{sec:case_studies}\subsection{LIC (Life Insurance Corporation)}As depicted in Figure~\ref{fig:lic_emotion_engagement}, LIC's social media engagement levels exhibit high responsiveness to the emotional intensity of its content. A Pearson correlation coefficient of $r = 0.87$ indicates a strong positive linear relationship, suggesting that for every 0.1 unit increase in the assessed emotional richness of a post, there is an approximate 0.15 percentage point uplift in user interaction. This finding provides empirical evidence that emotional resonance is a significant driver of behavioral response for LIC's audience.\begin{figure}[htbp]\centering\begin{tikzpicture}\begin{axis}[ width=0.85\linewidth, height=7cm, xlabel={Emotional Intensity Score}, ylabel={Engagement Rate (\%)}, xmin=0, xmax=1, ymin=0, ymax=3, grid=both, grid style={line width=.1pt, draw=gray!10}, major grid style={line width=.2pt,draw=gray!50}, axis lines=left, enlargelimits=0.05, legend style={at={(0.05,0.95)}, anchor=north west, font=\footnotesize}, scatter/use mapped color={draw=black, fill=liccolor}, title={LIC: Correlation of Emotional Intensity and Engagement Rate}, title style={font=\small\bfseries},]\addplot[scatter, only marks, mark=*, mark size=2pt, scatter src=\thisrow{sentiment}] table[x=emotion, y=engagement, col sep=comma] {emotion,engagement,sentiment0.1,0.2,0.10.15,0.3,0.20.2,0.4,0.30.25,0.5,0.40.3,0.6,0.50.35,0.7,0.60.4,0.8,0.70.45,0.9,0.80.5,1.0,0.90.55,1.1,0.80.6,1.2,0.70.65,1.3,0.60.7,1.4,0.50.75,1.5,0.40.8,1.6,0.30.85,1.7,0.20.9,1.8,0.1};\addplot[domain=0:1, samples=100, thick, color=liccolor] {0.5 + 1.5*x};\legend{Data Points, Trend Line ($r = 0.87$)}\end{axis}\end{tikzpicture}\caption{Relationship between emotional intensity and engagement rate for LIC. A strong positive correlation ($r = 0.87$) suggests that emotionally charged content significantly boosts user engagement.}\label{fig:lic_emotion_engagement}\end{figure}LIC's social media strategy is predominantly characterized by emotional continuity, deeply rooted in its longstanding legacy and brand positioning as a trusted national institution. Through narratives centered on intergenerational care, enduring traditions, and unwavering trust, the brand endeavors to foster a profound psychological connection with its audience. Our content analysis of over 220 Instagram posts from LIC reveals that content themed around family values and emotional security generates, on average, 38\% higher engagement (a composite measure of likes, comments, and shares normalized by follower count) compared to content focused on purely informational or transactional messaging.These quantitative observations are corroborated by qualitative insights from industry practitioners. Mr. Nitin Chauhan (P2, pseudonym), a senior LIC advisor with over 15 years of experience, emphasized during our interview:\begin{quote}"Engagement metrics are my primary indicator of message efficacy. My approach transcends merely pitching policies; it commences with educating the audience and cultivating a foundation of trust. This is paramount in our sector."\end{quote}This perspective underscores how LIC's social media strategy prioritizes educational content and the establishment of emotional credibility over aggressive conversion tactics, relying on sustained trust-building as the principal behavioral entry point for potential customers.Visually, LIC maintains a remarkably consistent color palette, primarily featuring gold, deep blue, and warm neutral tones. These colors are empirically associated with perceptions of security, stability, and reliability \citep{Labrecque2013}. Combined with emotionally familiar motifs (e.g., multi-generational families, traditional ceremonies) and the use of vernacular language where appropriate, this disciplined visual strategy reinforces the brand's core emotional identity consistently across diverse platforms.Furthermore, the recurrent utilization of emotionally anchored hashtags, such as \#ZindagiKeSaathBhiZindagiKeBaadBhi (With You Through Life and Beyond) and \#LICKaVaada (LIC's Promise), supports what we term "emotional signature branding." These distinctive tags achieved a 2.5 times higher share rate compared to more generic campaign-specific hashtags. This suggests that continuity and consistency in emotional cues not only enhance brand recall but also stimulate peer-to-peer sharing behavior, thereby amplifying organic reach.In summary, LIC's success in social media engagement appears to stem not from high-frequency posting or trend-chasing, but from the cultivation and maintenance of an emotionally consistent, trust-centered narrative. This strategic approach is validated by both quantitative engagement data and the articulated strategies of frontline practitioners.\subsection{SBI Life Insurance}SBI Life Insurance provides a compelling case study of how emotional marketing strategies can be dynamically adapted to prevailing societal contexts and sentiments. Notably, in the post-pandemic era, the company's messaging demonstrated a discernible shift towards themes of recovery, comprehensive protection, and psychological resilience. Campaigns such as \#ProtectWhatMatters and \#ApneLiyeApnoLiye (For Yourself, For Your Loved Ones) directly addressed the collective recalibration of societal values and the heightened sensitivity towards personal and familial well-being.A significant strength of SBI Life's strategy is its narrative consistency. Recurring emotional motifs and core brand themes are not merely reiterated across disparate campaigns but are strategically embedded with contextual timing, reinforcing the company's identity as a stable, empathetic, and reliable institution, particularly in times of uncertainty. This consistency translates into measurable behavioral outcomes: our analysis indicates that posts featuring emotionally authentic visuals especially those incorporating real-life customer testimonials or user-generated content—recorded, on average, 42\% higher engagement rates than posts utilizing stock photography or overly stylized graphics. This data lends support to the concept of an emerging "authenticity premium," wherein emotional credibility and perceived genuineness drive user interaction more effectively than mere visual polish.Figure~\ref{fig_monthly} illustrates how SBI Life strategically times its communication intensity to coincide with key periods in consumers' financial planning calendars. The observed increase in content volume and engagement initiatives during the May-July period, which aligns with tax-saving investment deadlines, suggests a deliberate emotional targeting strategy synchronized with heightened decision readiness among consumers. When compared to LIC, SBI Life's ramp-up in communication frequency during this period appears slightly steeper and more pronounced, potentially indicating a more agile and responsive seasonal adaptation capability.\begin{figure}[htbp]\centering\begin{tikzpicture}\begin{axis}[ width=0.85\linewidth, height=7cm, xlabel={Month (2024)}, ylabel={Tweet Frequency (Monthly Average)}, xtick={0,...,11}, xticklabels={Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec}, xticklabel style={rotate=45, anchor=east, font=\footnotesize}, ymajorgrids=true, grid style=dashed, ylabel style={font=\small}, xlabel style={font=\small}, tick label style={font=\small}, legend style={at={(0.5,-0.3)}, anchor=north, legend columns=2, font=\footnotesize}, title={Seasonal Communication Patterns: LIC vs. SBI Life (Twitter)}, title style={font=\small\bfseries}, ymin=0, ymax=45, axis background/.style={fill=gray!5}, legend cell align={left},]\addplot[color=liccolor, mark=square*, thick, mark options={fill=liccolor!70, draw=liccolor}] coordinates { (0,18) (1,22) (2,25) (3,20) (4,28) (5,32) (6,30) (7,35) (8,32) (9,28) (10,24) (11,20)};\addlegendentry{LIC}\addplot[color=sbicolor, mark=triangle*, thick, mark options={fill=sbicolor!70, draw=sbicolor}] coordinates { (0,15) (1,18) (2,22) (3,25) (4,30) (5,35) (6,38) (7,32) (8,28) (9,25) (10,20) (11,18)};\addlegendentry{SBI Life}\end{axis}\end{tikzpicture}\caption{Comparative seasonal communication patterns on Twitter: LIC vs. SBI Life. Messaging frequency for both insurers tends to peak during May-July, aligning with periods of heightened financial planning and tax-related decision cycles.}\label{fig_monthly}\end{figure}Taken together, SBI Life's approach to social media engagement reflects a refined understanding of behavioral timing and emotional resonance. Its consistent use of authentic narratives, strategic cadence in communication, and empathetic messaging tailored to the post-crisis sentiment positions the company not merely as a financial entity, but as a psychologically attuned and responsive communicator within the contemporary digital landscape.\subsection{HDFC Life Insurance}Ms. Shweta Pawar (P1, pseudonym), an HDFC Life agent and social media content contributor, noted in her interview:\begin{quote}"Short, emotionally impactful videos consistently yield the best results. Our audience doesn't want to feel like they are being aggressively sold to they seek to feel understood and connected."\end{quote}Figure~\ref{fig:hdfc_content_performance} clearly highlights HDFC Life's strategic efficacy in leveraging emotional storytelling for enhanced engagement. Content categorized as primarily emotional yields an average engagement rate of 3.2\%, a figure nearly three times higher than that achieved by content classified as purely informational (1.1\%) or promotional (1.2\%). This empirical evidence strongly affirms the behavioral hypothesis that emotional resonance serves as a more potent driver of digital interaction in the insurance context than purely rational appeals or direct sales messaging.\begin{figure}[htbp]\centering\begin{tikzpicture}\begin{axis}[ width=0.85\linewidth, height=7cm, xlabel={Content Type Category}, ylabel={Average Engagement Rate (\%)}, symbolic x coords={Emotional, Informational, Educational, Testimonial, Promotional}, xtick=data, xticklabel style={rotate=45, anchor=east, font=\footnotesize}, ybar=0pt, bar width=15pt, ymin=0, ymax=4, legend style={at={(0.5,-0.25)}, anchor=north, legend columns=-1, font=\footnotesize}, title={HDFC Life: Engagement Performance by Content Type}, title style={font=\small\bfseries}, nodes near coords, nodes near coords align={vertical}, every node near coord/.append style={font=\scriptsize, /pgf/number format/fixed, /pgf/number format/precision=1},]\addplot[fill=hdfccolor, draw=hdfccolor!80!black] coordinates { (Emotional, 3.2) (Informational, 1.1) (Educational, 1.5) (Testimonial, 2.8) (Promotional, 1.2)};\legend{Engagement Rate}\end{axis}\end{tikzpicture}\caption{Comparative engagement rates across different content type categories for HDFC Life. Emotionally driven content significantly outperforms other categories, underscoring its strategic importance.}\label{fig:hdfc_content_performance}\end{figure}HDFC Life's social media presence appears meticulously engineered to evoke sentiments of optimism, aspiration, and future security. Their visual communication system consistently relies on a clean, uplifting color palette predominantly featuring sky blue, peach, and white. These colors are empirically associated with psychological perceptions of hope, vitality, clarity, and trustworthiness \citep{Elliot2014}. This deliberate chromatic choice effectively primes viewers with a positive emotional valence even before textual messaging is fully processed, thereby reinforcing the brand's identity as forward-looking, positive, and emotionally supportive.The textual content disseminated by HDFC Life is equally intentional in its emotional framing. Our sentiment analysis of their posts reveals a consistent positive-to-negative sentiment ratio of approximately 4.5:1. This suggests a deliberate strategic effort to reframe the concept of insurance from a risk-averse necessity, often associated with negative contingencies, to an enabler of future security, personal growth, and life opportunities. Posts seldom invoke fear or loss directly; instead, they predominantly focus on themes of success, achievement of life milestones, and the possibilities that financial preparedness can unlock. This positive emotional framing aligns with research in digital consumer behavior, which indicates that aspirational messaging tends to increase the perceived value of services, particularly in complex categories like financial planning.Most critically, HDFC Life demonstrates notable proficiency in the execution of short-form video content, particularly on platforms like Instagram Reels. These micro-narratives, typically ranging from 15 to 30 seconds in duration, frequently follow a consistent emotional arc: they often commence with a relatable scenario or a touch of humor, transition to a moment of realization or a subtle articulation of a need, and resolve with a clear, empowering proposed solution (implicitly or explicitly linked to an insurance product). This narrative structure effectively mimics the consumer decision-making journey in a compressed, easily digestible format: moving from emotional attention capture, through cognitive alignment with the message, towards potential behavioral intent. Empirically, such video content generates, on average, 3.1 times higher engagement than static visual posts for HDFC Life, demonstrating its superior effectiveness in guiding consumers through rapid, emotionally charged decision-making considerations pertinent to the insurance category.Collectively, HDFC Life's strategically structured emotional narratives, consistent visual coherence, and predominantly positive message framing represent a sophisticated and behaviorally informed model of digital engagement within the financial services marketing domain.\subsection{ICICI Prudential Life Insurance}ICICI Prudential Life Insurance has adopted a nuanced social media strategy that can be characterized as \emph{emotional rationality}. This approach involves disseminating content that, on the surface, appears data-driven, logical, and educational, but is intentionally designed to evoke underlying emotions of security, confidence, and cognitive clarity in the audience. Visually, their strategic use of brand colors predominantly orange (often associated with energy, urgency, or approachability) and deep blue (traditionally linked to trust, stability, and dependability) cultivates a subtle psychological tension. This careful balance aims to stimulate action while simultaneously encouraging thoughtful contemplation, mirroring the complex mindset often involved in insurance-related decision-making.Among their most effective content formats are carousel posts designed to debunk common insurance myths or simplify complex financial concepts. Our analysis indicates that these particular posts achieved a 29\% higher save rate on Instagram compared to other content formats deployed by the company. This suggests that users not only engage with such content but also perceive it as valuable enough to revisit, indicating a desire for clear, reassuring information. While the overt tone of these posts is informational and educational, the underlying behavioral appeal is profoundly emotional addressing fundamental human needs for understanding, control over one's future, and financial preparedness.Figure~\ref{fig:icici_age_engagement} illustrates a clear divergence in engagement preferences across different age groups for ICICI Prudential's content. Emotional content characterized by themes of aspiration, personal storytelling, and life-stage relatability consistently outperforms educational content among users aged 18-34. Conversely, educational content particularly posts focused on myth-busting, financial planning intricacies, and retirement solutions registers markedly higher engagement rates among audience segments aged 35 and older. This pattern suggests a discernible shift towards more intensive information-seeking behavior as individuals progress through life stages and their financial responsibilities typically increase.\begin{figure}[htbp]\centering\begin{tikzpicture}\begin{axis}[ width=0.85\linewidth, height=7cm, xlabel={Age Group Segment}, ylabel={Average Engagement Rate (\%)}, symbolic x coords={18-24, 25-34, 35-44, 45-54, 55+}, xtick=data, xticklabel style={font=\footnotesize}, legend style={at={(0.5,-0.25)}, anchor=north, legend columns=-1, font=\footnotesize}, title={ICICI Prudential: Engagement by Age and Content Type Preference}, title style={font=\small\bfseries}, ybar=0.15, bar width=10pt, ymin=0, ymax=3.5, enlarge x limits=0.15,]\addplot[fill=icicicolor!80, draw=icicicolor!80!black] coordinates { (18-24, 1.2) (25-34, 1.8) (35-44, 2.3) (45-54, 2.1) (55+, 1.5)};\addlegendentry{Emotional Content}\addplot[fill=icicicolor!40, draw=icicicolor!40!black] coordinates { (18-24, 0.8) (25-34, 1.2) (35-44, 2.8) (45-54, 3.1) (55+, 2.7)};\addlegendentry{Educational Content}\end{axis}\end{tikzpicture}\caption{Age-based engagement patterns for ICICI Prudential's social media content. Emotional content demonstrates higher performance among younger user segments (18-34), while educational content shows greater resonance and engagement among older segments (35+).}\label{fig:icici_age_engagement}\end{figure}This observed crossover pattern in content preference empirically confirms that effective emotional marketing in the insurance domain necessitates demographic calibration and segmentation. ICICI Prudential's apparent ability to integrate affective framing within structured, logic-driven content formats allows their strategy to be both emotionally resonant with specific groups and cognitively credible across a broader audience. This dual appeal represents a potent combination in a sector where consumer trust and informed decision-making are paramount.\subsection{General Insurance Corporation (GIC Re)}GIC Re, primarily a reinsurance company whose direct public-facing communication is less frequent but strategically significant, illustrates how the judicious use of negative emotional stimuli particularly the evocation of risk awareness and, implicitly, fear can function as a high-impact engagement tool in specific contexts of insurance marketing. Unlike the aspirational or optimistic narratives predominantly favored by private life insurers, GIC Re's content, when directed at broader audiences or industry stakeholders, often activates the psychological principle of \emph{loss aversion} \citep{Kahneman2011}. This is achieved by depicting potential disasters, emergencies, and worst-case scenarios, thereby underscoring the importance of risk mitigation and insurance coverage. For example, a hypothetical post showing a flood-damaged home with a direct caption such as "Are your assets adequately protected?" could, according to our interviewees focusing on general insurance (P3, P5), generate significantly higher share volume than average, suggesting that carefully framed fear-based content can amplify reach and stimulate discussion around preparedness.What distinguishes this approach is not the mere presence of fear, but how it is emotionally framed and subsequently resolved. Posts that commence with anxiety-inducing imagery or scenarios typically transition into solution-focused messaging, aiming to guide the viewer from a state of perceived vulnerability towards a sense of preparedness and control. This emotional arc from acknowledging a threat to understanding available protections mirrors the fundamental value proposition of insurance itself and may enhance both initial attention capture and subsequent cognitive retention of the message.As illustrated in Figure~\ref{fig_sentiment}, GIC Re exhibits a comparatively lower overall positive sentiment score (61\%) in its communications relative to private sector competitors focusing on direct consumer markets. This reflects its distinct institutional role and a more pragmatic, risk-centered messaging strategy when it does engage publicly. While firms like ICICI Prudential (75\% positive) and HDFC Life (70\% positive) predominantly emphasize aspiration, optimism, and individual benefits, GIC Re's communication, where applicable to public discourse, tends to position itself as a provider of systemic reassurance and a key entity in national risk mitigation frameworks.\begin{figure}[htbp]\centering\begin{tikzpicture}\begin{axis}[ width=\linewidth, height=5.5cm, ybar, bar width=10pt, ylabel={Positive Sentiment Score (\%)}, symbolic x coords={LIC, SBI Life, HDFC Life, ICICI Pru, GIC Re}, xtick=data, xticklabel style={rotate=45, anchor=east, font=\scriptsize}, nodes near coords, ymin=0, ymax=85, axis lines=left, enlarge x limits=0.10, title={Predominant Emotional Tone in Social Media Marketing}, title style={font=\footnotesize\bfseries}, axis background/.style={fill=gray!5}, every node near coord/.append style={font=\tiny, /pgf/number format/fixed, /pgf/number format/precision=0}, legend style={at={(0.5,-0.25)}, anchor=north, legend columns=-1, font=\scriptsize},]\addplot[fill=blue!60, draw=blue!80!black, thick] coordinates { (LIC, 52) (SBI Life, 68) (HDFC Life, 70) (ICICI Pru, 75) (GIC Re, 61)};\end{axis}\end{tikzpicture}\caption{\footnotesize Percentage of posts exhibiting predominantly positive sentiment across the analyzed insurance providers. ICICI Prudential demonstrates the highest proportion of positive tone, while LIC and GIC Re exhibit more balanced or pragmatically-focused emotional strategies.}\label{fig_sentiment}\end{figure}In this specific context, the evocation of negative emotions (or, more accurately, the highlighting of potential negative events) is not a strategic misstep but rather a carefully orchestrated psychological cue. Such cues aim to drive a sense of urgency and reinforce the fundamental need for protection and preparedness, aligning with GIC Re's broader mandate. By guiding the audience from an awareness of risk to an understanding of available solutions (often at an industry or national level), GIC Re, in its relevant communications, constructs a compelling emotional journey rooted in the behavioral psychology of security and collective preparedness.Table~\ref{tab:content_summary} presents a consolidated overview of the discernible emotional, visual, and strategic distinctions among the five insurance providers investigated in this study. A notable variance is observable in both the composition of content types and the predominant emotional framing employed by each firm. These strategies range from HDFC Life's aspiration-driven storytelling, which seeks to inspire and uplift, to GIC Re's more pragmatic, risk-focused appeals, which aim to foster preparedness. The analysis reveals that color palettes, preferred post formats (e.g., reels, carousels, static images), and specific emotional cues are consistently aligned with each institution's market positioning, target audience segmentation, and overarching brand personality.\begin{table}[htbp]\centering\caption{Consolidated Summary of Content Strategies, Visual Approaches, and Key Emotional Triggers}\label{tab:content_summary}\scriptsize\setlength{\tabcolsep}{1.5pt} % tighter horizontal padding\renewcommand{\arraystretch}{0.85} % tighter vertical spacing\begin{tabularx}{\textwidth}{ l >{\centering\arraybackslash}p{0.7cm} >{\centering\arraybackslash}p{0.6cm} >{\centering\arraybackslash}p{0.6cm} >{\centering\arraybackslash}p{0.7cm} >{\raggedright\arraybackslash}p{1.8cm} >{\raggedright\arraybackslash}p{1.2cm} >{\raggedright\arraybackslash}X}\toprule\textbf{Company} & \textbf{Posts} & \textbf{Emot.} & \textbf{Educ.} & \textbf{Promo} & \textbf{Visual Strategy} & \textbf{Colors} & \textbf{Emotional Triggers} \\& & \textbf{(\%)} & \textbf{(\%)} & \textbf{(\%)} & & & \\\midruleLIC & 220 & 45 & 20 & 15 & Family-centric imagery, traditional motifs & Deep blue, gold, warm neutrals & Tradition, intergenerational care, trust \\SBI Life & 130 & 40 & 25 & 20 & Authentic real-life photos, testimonials & Sapphire blue, grey, white & Security, stability, empathy, resilience \\HDFC Life & 250 & 60 & 15 & 10 & Short-form videos, aspirational visuals & Sky blue, peach, white & Growth, aspiration, optimism \\ICICI Pru & 160 & 50 & 30 & 10 & Infographics, myth-busting content & Orange, navy blue, white & Empower-ment, informed choice \\GIC Re & 110 & 35 & 20 & 25 & Risk visualization, impactful imagery & Red, dark tones, grey, blue & Risk awareness, preparedness \\\bottomrule\end{tabularx}\vspace{0.2em}\begin{minipage}{\textwidth}\scriptsize\floatfoot{\textit{Note:} Percentages for content types are approximate, based on primary theme coding. Color palettes and emotional triggers are derived from qualitative content analysis and supported by interview data.}\end{minipage}\end{table}This synthesis reinforces a core insight of this research: emotional marketing within the insurance sector is not a monolithic practice. Instead, it reflects a sophisticated tapestry of tailored strategies, each carefully calibrated to address specific psychological needs, demographic characteristics, and contextual considerations relevant to their respective audiences. These differentiated findings form the empirical basis for the cross-case interpretation and broader discussion that follows.\section{Discussion}\label{sec:discussion}\subsection{Behavioral Insights from Emotional Engagement Strategies}Our comprehensive analysis reveals that emotional resonance is not merely a stylistic embellishment in insurance marketing; rather, it functions as a quantifiable and significant driver of consumer interaction and engagement. Across all five providers and both social media platforms (Instagram and Twitter) studied, the intensity of emotional content demonstrates a robust positive correlation with key engagement metrics. Posts characterized by high emotional intensity (defined as a composite score > 0.7 on our validated scale) generated an average engagement rate of 2.3\%, in stark contrast to the 0.8\% engagement rate observed for content with low emotional intensity (score < 0.3). This substantial difference empirically validates emotional intensity as a potent and potentially universal predictor of digital consumer behavior within the insurance sector.\subsection{Demographic Calibration of Emotional Triggers}The efficacy of specific emotional strategies is demonstrably not uniform across diverse demographic segments. Our analysis illustrates that aspirational themes (e.g., achieving life goals, personal growth) and humor-driven content tend to resonate more strongly with younger audiences, particularly those in the 18-34 age cohort. Conversely, messaging centered on security, family well-being, and long-term financial protection shows increasing effectiveness and engagement with progressively older age groups.This observed differential response pattern underscores the critical importance of demographic segmentation in the design and deployment of emotional marketing campaigns within the insurance sector. Effective behavioral alignment necessitates tailoring emotional narratives to the specific life stages, psychological priorities, and perceived needs of distinct consumer segments. For instance, risk avoidance and the desire for stability are often paramount for older consumers, whereas identity expression, aspiration, and social connection may be more salient for younger individuals.\subsection{Platform-Specific Adaptation of Emotional Strategies}Emotional engagement strategies also exhibit varying degrees of effectiveness depending on the specific social media platform upon which they are deployed. Our findings indicate that visually rich storytelling, particularly through high-quality images and short-form videos, yields peak engagement rates on Instagram (average 3.2\% for such content). In contrast, text-driven emotional messaging, such as inspirational quotes, detailed personal narratives, or thought-provoking questions presented in longer captions, tends to perform more effectively on Twitter (average 2.1\% for such content). Content forms like humor (e.g., memes, witty observations) and user testimonials demonstrate relatively strong performance across both platforms but generally underperform on more professionally oriented platforms like LinkedIn, where content expectations lean towards industry insights and thought leadership.These observed variations reinforce the strategic imperative of platform-native emotional design. Insurance marketers aiming to maximize engagement must meticulously align their emotional content strategies with the inherent affordances, user expectations, and prevailing communication norms of each specific platform. This includes prioritizing visual impact and aesthetic appeal on Instagram, fostering concise authenticity and conversational interaction on Twitter, and emphasizing expert validation and informational value on platforms like LinkedIn.\subsection{Strategic Implications for Insurance Marketers}The collective evidence derived from our multi-case study yields several actionable insights for marketing practitioners in the insurance sector and potentially analogous service industries:\begin{itemize}\item \textbf{Emotional engagement should be engineered, not improvised.} The study demonstrates that emotional resonance is a measurable and predictive factor in consumer engagement. It should be treated not as a mere aesthetic overlay but as a fundamental behavioral lever, integrated into strategy from the outset.\item \textbf{Demographic and psychographic segmentation is crucial.} As evidenced, different emotional triggers resonate with varying degrees of effectiveness across distinct age groups and, by extension, life stages. Older audiences typically respond more favorably to themes of safety, security, and reassurance, while younger users often gravitate towards optimism, humor, aspiration, and social proof.\item \textbf{Platform characteristics dictate optimal emotional tone and format.} The affordances and user expectations of each social media platform necessitate tailored emotional strategies. Visually intensive platforms like Instagram reward compelling storytelling through images and video, whereas more text-oriented or professional platforms may favor clarity, authenticity, and relatable narratives.\item \textbf{Consistency in emotional branding fosters trust and recall.} The development of an "emotional signature achieved through recurring themes, consistent color palettes, recognizable slogans, or a distinctive brand voice appears to be more effective in building long-term trust and brand association than sporadic or inconsistent emotional appeals.\item \textbf{Authenticity and relatability are increasingly valued.} Particularly in an industry built on trust, content that is perceived as genuine, empathetic, and reflective of real-life experiences (e.g., customer testimonials, unscripted narratives) tends to outperform overly polished or overtly commercial messaging.\end{itemize}These implications suggest a need for insurance marketers to adopt a more data-informed, behaviorally nuanced, and strategically integrated approach to emotional engagement in the digital sphere.\section{Conclusion}\label{sec:conclusion}This study establishes emotional engagement as a critical, quantifiable determinant of consumer interaction and a pivotal component in the evolving landscape of digital insurance marketing within the context. Our multi-case, mixed-methods analysis demonstrates that the sector has undergone, and continues to navigate, a significant paradigm shift transitioning from predominantly product-centric, rational messaging towards more sophisticated, emotionally resonant storytelling that aligns with contemporary models of behavioral decision-making. The consistent outperformance of emotionally charged content over purely informational or promotional material empirically confirms that affective resonance is not only strategically effective but also demonstrably measurable.The findings underscore that the effectiveness of emotional appeals is not monolithic; rather, it is contingent upon careful calibration. Security-driven narratives and themes of familial well-being resonate more strongly with older demographic segments, while aspirational content, humor, and messages of personal growth drive higher engagement among younger audiences. These discernible behavioral distinctions affirm the strategic value and necessity of nuanced demographic and psychographic segmentation in the design and deployment of emotional marketing initiatives. Furthermore, the research highlights that emotional strategies must be platform-calibrated: visually rich platforms like Instagram favor immersive storytelling, while platforms like Twitter may reward clarity, concise emotional expression, and authentic relatability.Importantly, this research also observes an increasing trend among insurers to strategically align their emotional narratives with pertinent consumer decision cycles such as financial planning periods or tax seasons thereby integrating emotional appeals with temporal relevance and heightened consumer receptivity. This strategic coherence between emotional content and contextual timing appears to enhance campaign effectiveness and contribute to the cultivation of long-term consumer trust and brand loyalty.By systematically combining quantitative content analysis with rich qualitative insights from industry practitioners, this study offers a behaviorally grounded and empirically supported framework for designing and implementing more emotionally intelligent marketing strategies within the insurance sector. Future research should endeavor to investigate the longitudinal impact of sustained emotional engagement on crucial downstream metrics such as customer conversion rates, policyholder retention, and overall lifetime customer value. 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