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<p>This study examines how marketing professionals publicly articulate total rewards perceptions through LinkedIn discourse, extending natural language processing methodologies via domain-specific model development. Analyzing 35,691 comments from 2023, we identify significant shifts toward digital skill-based compensation discourse (+ 31.2%) and strong positive sentiment associations with flexible work arrangements (<i>r</i> = .48, <i>p</i> &lt; .001), particularly among technology sector professionals. Hierarchical regression analysis confirms that digital skill discourse orientation and industry sector interact to predict sentiment variation (ΔR² = 0.04, <i>p</i> &lt; .001), with digital competency emphasis demonstrating differential effects across technology, retail, and consumer goods contexts. Drawing on Skill-Based Total Rewards Theory, we provide preliminary empirical support for three propositions: Digital Skill-Reward Congruence, Market Value of Skills, and Skills-Based Flexibility principles. The sentiment analysis model achieved an F1-score of 0.89, while industry-specific analysis revealed significant variations (F(2,35688) = 27.32, <i>p</i> &lt; .001) across sectors. Recognizing that LinkedIn discourse reflects publicly constructed professional identity positions rather than unfiltered latent attitudes, we interpret these patterns as expressions of signaled reward preferences within professional networking contexts. Methodologically, the study demonstrates NLP applications for large-scale professional sentiment analysis. Practically, findings inform industry-specific compensation strategies recognizing digital competencies and workplace flexibility as critical retention factors. This research contributes to compensation theory through quantitative operationalization of SBTRT propositions while establishing methodological frameworks for discourse-based organizational research.</p>

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Analyzing total rewards perceptions of marketing professionals through linkedin discourse: a natural language processing approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Sezai Tunca,
  • Yavuz Selim Balcioglu,
  • Ceren Cubukcu Cerasi,
  • Ayse Bilgen,
  • Ayse Ilgun-Kamanli

摘要

This study examines how marketing professionals publicly articulate total rewards perceptions through LinkedIn discourse, extending natural language processing methodologies via domain-specific model development. Analyzing 35,691 comments from 2023, we identify significant shifts toward digital skill-based compensation discourse (+ 31.2%) and strong positive sentiment associations with flexible work arrangements (r = .48, p < .001), particularly among technology sector professionals. Hierarchical regression analysis confirms that digital skill discourse orientation and industry sector interact to predict sentiment variation (ΔR² = 0.04, p < .001), with digital competency emphasis demonstrating differential effects across technology, retail, and consumer goods contexts. Drawing on Skill-Based Total Rewards Theory, we provide preliminary empirical support for three propositions: Digital Skill-Reward Congruence, Market Value of Skills, and Skills-Based Flexibility principles. The sentiment analysis model achieved an F1-score of 0.89, while industry-specific analysis revealed significant variations (F(2,35688) = 27.32, p < .001) across sectors. Recognizing that LinkedIn discourse reflects publicly constructed professional identity positions rather than unfiltered latent attitudes, we interpret these patterns as expressions of signaled reward preferences within professional networking contexts. Methodologically, the study demonstrates NLP applications for large-scale professional sentiment analysis. Practically, findings inform industry-specific compensation strategies recognizing digital competencies and workplace flexibility as critical retention factors. This research contributes to compensation theory through quantitative operationalization of SBTRT propositions while establishing methodological frameworks for discourse-based organizational research.