Indian Institute of Management Kozhikode

Purpose Peer-to-peer (P2P) service platforms often face challenges in aligning customer experience with their brand promises due to the influence of independent service providers. Customer experiences with individual service providers spill over to the evaluation of the platform, affecting its brand image. This conceptual paper aims to propose a differential positioning framework to manage these spillover effects and mitigate brand free-riding, offering a strategic approach to brand management in decentralized P2P services. Design/methodology/approach This study develops a differential positioning framework for platform service providers using a process model with a narrative-based approach. The process model, as a conceptual approach, is more useful for studying complex phenomena, where rich narratives and broader explanations are more valuable than formal, testable propositions. Findings This study highlights brand free-riding on P2P service platforms, where service providers benefit from the platform’s brand equity without proportionally contributing through brand-aligned service delivery. The authors examine how customers’ experiences with service providers spill over to evaluations of the platform brand and identify the underlying mechanisms and key moderators driving this process. Grounded in brand management literature, the authors propose a differential positioning framework that leverages service providers’ alignment with the platform brand to manage spillover effects and mitigate brand free-riding. Originality/value This research adopts a brand-centric perspective to examine how effectively individual service providers deliver the experiential dimensions of a platform’s value proposition. Moving beyond traditional platform governance, this study introduces an actionable framework that proactively manages the platform’s brand image while giving service providers greater flexibility. The framework also fosters value co-creation on P2P service platforms by improving customer–provider matching and reducing uncertainty, ultimately enhancing the overall customer experience.

This paper focuses on selecting the arm with the highest variance from a set of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> independent arms. Specifically, we focus on two settings: (i) misallocation minimization setting, that penalizes the number of pulls of suboptimal arms in terms of variance, and (ii) fixed-budget best arm identification setting, that evaluates the ability of an algorithm to determine the arm with the highest variance after a fixed number of pulls. We develop a novel online algorithm called UCB–VV for the misallocation minimization (MM) and show that its upper bound on misallocation for bounded rewards evolves as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(log n)$</tex-math></inline-formula> where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the horizon. By deriving the lower bound on the misallocation, we show that UCB–VV is order optimal. For the fixed budget best arm identification (BAI) setting we propose the SHVV algorithm. We show that the upper bound of the error probability of SHVV evolves as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm exp}(-\frac{n}{{\rm log}(K)H})$</tex-math></inline-formula>, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H$</tex-math></inline-formula> represents the complexity of the problem, and this rate matches the corresponding lower bound. We extend the framework from bounded distributions to sub-Gaussian distributions using a novel concentration inequality on the sample variance and standard deviation. Leveraging the same, we derive a concentration inequality for the empirical Sharpe ratio (SR) for sub-Gaussian distributions, which was previously unknown in the literature. Empirical simulations show that UCB–VV consistently outperforms <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ϵ$</tex-math></inline-formula>–<monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">greedy</monospace> across different sub-optimality gaps though it is surpassed by VTS, which exhibits the lowest misallocation, albeit lacking in theoretical guarantees. We also illustrate the superior performance of SHVV, for a fixed budget setting under 6 different setups against uniform sampling. Finally, we conduct a case study to empirically evaluate the performance of the UCB–VV and SHVV in call option trading on 100 stocks generated using geometric Brownian motion (GBM).

Purpose This study aims to investigate how perceived organizational justice affect workplace dynamics, focusing on their cascading effects on workplace loneliness, knowledge hiding and psychological well-being. Drawing on cognitive appraisal theory, this study proposes that perceived organizational justice can trigger negative emotional responses such as loneliness, which in turn may exacerbate defensive behaviors like knowledge hiding and reduce psychological well-being. Design/methodology/approach This study used a two-wave, time-lagged design, which yielded a sample size of 216 full-time employees. The final data was analyzed using SPSS and AMOS. Findings The results revealed a negative association between perceptions of organizational justice and workplace loneliness. Workplace loneliness was positively related to knowledge hiding and negatively related to psychological well-being. The analysis of indirect effects indicated that perceptions of organizational justice negatively affect knowledge hiding through workplace loneliness, while its relationship with psychological well-being was positive. Originality/value By advancing an understanding of the social and emotional mechanisms underlying justice perceptions, this study contributes to the organizational justice literature and provides actionable insights for fostering fair, inclusive and supportive workplaces to mitigate knowledge hiding and enhance employee well-being.