Networks of Gratitude: Structures of Thanks and User Expectations in Workplace Appreciation Systems

Appreciation systems--platforms for users to exchange thanks and praise--are becoming common in the workplace, where employees share appreciation, managers are notified, and aggregate scores are sometimes made visible. Who do people thank on these systems, and what do they expect from each other and their managers? After introducing the design affordances of 13 appreciation systems, we discuss a system we call Gratia, in use at a large multinational company for over four years. Using logs of 422,000 appreciation messages and user surveys, we explore the social dynamics of use and ask if use of the system addresses the recognition problem. We find that while thanks is mostly exchanged among employees at the same level and different parts of the company, addressing the recognition problem, managers do not always act on that recognition in ways that employees expect.Comment: in Tenth International AAAI Conference on Web and Social Media, 201

The delta invariant and the various GIT-stability notions of toric Fano varieties

In this article, we give combinatorial proofs of the following two theorems: (1) If a Gorenstein toric Fano variety is asymptotically Chow semistable then it is Ding polystable. (2) For a smooth toric Fano manifold $X$, the delta invariant $\delta(X)$ defined by Fujita and Odaka coincides with the greatest Ricci lower curvature $R(X)$. In the proof, neither toric test configuration nor toric Minimal Model Program (MMP) we use. We also verify the reductivity of automorphism group of toric Fano $3$-folds by computing Demazure's roots for each. All the results are listed in Table $1$ with the value of $\delta(X)$ and $R(X)$.Comment: 19 pages, 2 figures, 1 table. Fixed an error in Proposition 4.3. Section 5 in the previous version removed. The appendix added. The title changed from the first versio

Knowledge sharing by entrepreneurs in a virtual community of practice (VCoP)

PurposeThis paper examines how entrepreneurs engage in a Virtual Community of Practice (VCoP) to share knowledge. Intensity of engagement is taken as a proxy to measure the strength of knowledge sharing.Design/methodology/approachThe archival data spanning over a three-year period from ‘Start-up-Nation©’ (a VCoP purposefully setup for entrepreneurs) is used for analysis. A set of indices are introduced to measure participants’ intensity of engagement in terms of message length, message frequency and reciprocity in the knowledge sharing process. Content analysis is employed to test a sample of ‘highly engaged’, ‘moderately engaged’, ‘low engaged’ and ‘not engaged’ discussion topics as part of the on-line discourse.FindingsWe find that entrepreneurs normally use short (fewer than 100 words) or medium (fewer than 250 words) message size to contribute to the discussions. In addition, we find that senior members and discussion moderators play important roles in igniting the ‘reciprocity’ behaviour in stimulating the interest of the community with the topic discussion. We also findthat highly engaged topics usually lead to further discussion threads.Originality/valueThis is the first study of its kind to explore how entrepreneurs engage in a VCoP to share their knowledge and experiences. The set of measurement indices tested here provide a tool for the owner, designer and moderator of the VCoP to measure the utility of their website in terms of its members’ participation. In addition, the set of textual and subjective interventions identified here enable the moderator (administrator) of a VCoP to design effective interventions to facilitate on-line discourse and augment the knowledge sharing process amongst its community members

ChatGPT and Persuasive Technologies for the Management and Delivery of Personalized Recommendations in Hotel Hospitality

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.Comment: 17 pages, 12 figure

Predictor Antenna Systems: Exploiting Channel State Information for Vehicle Communications

Vehicle communication is one of the most important use cases in the fifth generation of wireless networks (5G).\ua0 The growing demand for quality of service (QoS) characterized by performance metrics, such as spectrum efficiency, peak data rate, and outage probability, is mainly limited by inaccurate prediction/estimation of channel state information (CSI) of the rapidly changing environment around moving vehicles. One way to increase the prediction horizon of CSI in order to improve the QoS is deploying predictor antennas (PAs).\ua0 A PA system consists of two sets of antennas typically mounted on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In realistic PA systems, however, the actual benefit is affected by a variety of factors, including spatial mismatch, antenna utilization, temporal correlation of scattering environment, and CSI estimation error. This thesis investigates different resource allocation schemes for the PA systems under practical constraints, with main contributions summarized as follows.First, in Paper A, we study the PA system in the presence of the so-called spatial mismatch problem, i.e., when the channel observed by the PA is not exactly the same as the one experienced by the RA. We derive closed-form expressions for the throughput-optimized rate adaptation, and evaluate the system performance in various temporally-correlated conditions for the scattering environment. Our results indicate that PA-assisted adaptive rate adaptation leads to a considerable performance improvement, compared to the cases with no rate adaptation. Then, to simplify e.g., various integral calculations as well as different operations such as parameter optimization, in Paper B, we propose a semi-linear approximation of the Marcum Q-function, and apply the proposed approximation to the evaluation of the PA system. We also perform deep analysis of the effect of various parameters such as antenna separation as well as CSI estimation error. As we show, our proposed approximation scheme enables us to analyze PA systems with high accuracy.The second part of the thesis focuses on improving the spectral efficiency of the PA system by involving the PA into data transmission. In Paper C, we analyze the outage-limited performance of PA systems using hybrid automatic repeat request (HARQ). With our proposed approach, the PA is used not only for improving the CSI in the retransmissions to the RA, but also for data transmission in the initial round.\ua0 As we show in the analytical and the simulation results, the combination of PA and HARQ protocols makes it possible to improve the spectral efficiency and adapt transmission parameters to mitigate the effect of spatial mismatch

Predictor Antenna Systems: Exploiting Channel State Information for Vehicle Communications

Vehicle communication is one of the most important use cases in the fifth generation of wireless networks (5G). The growing demand for quality of service (QoS) characterized by performance metrics, such as spectrum efficiency, peak data rate, and outage probability, is mainly limited by inaccurate prediction/estimation of channel state information (CSI) of the rapidly changing environment around moving vehicles. One way to increase the prediction horizon of CSI in order to improve the QoS is deploying predictor antennas (PAs). A PA system consists of two sets of antennas typically mounted on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In realistic PA systems, however, the actual benefit is affected by a variety of factors, including spatial mismatch, antenna utilization, temporal correlation of scattering environment, and CSI estimation error. This thesis investigates different resource allocation schemes for the PA systems under practical constraints.Comment: Licentiate thesis, Chalmers University of Technolog

Turning Chatters into Donators: An Investigation of Topic-Based Bullet Screen Mode on a Livestreaming Platform Short Paper

Despite the importance of social interaction in virtual communities, scant research has investigated the outcomes of social interaction features. Our study aims to investigate the business value of social interaction features in the context of livestreaming platforms. Specifically, we investigate the effect of the activation of topic-based bullet screen mode – an interactive feature which allows the streamers to set a theme or topic for the viewers to send bullet screen comments about. Our results from the regression discontinuity estimation suggest that the activation of the topic-based bullet screen mode yields an immediate decrease in viewers\u27 chat interaction, which challenges the conventional wisdom social interaction features is a panacea for boosting increased user engagement. Nevertheless, we observe a compensatory effect whereby the decrease in chat interaction was accompanied by a surge in gift donations. This counterintuitive finding highlights the intricate interplay between social interaction features, user motivations, and platform affordances

Understanding the Emotional and Informational Influence on Customer Knowledge Contribution through Quantitative Content Analysis

Customer knowledge contribution is a vital source of business value. Existing studies paid limited attention to emotional influence on knowledge contribution. Drawing upon social support theory, this study attempts to elaborate the influence of emotional support and informational support on knowledge contribution of customers in a firm-hosted online community. Through quantitative content analysis including product feature extraction and sentiment analysis, we analyzed content data from 2318 users. A set of research hypotheses were tested via regression analysis of panel data. We found that informational support (information diagnosticity and source credibility) and emotional support (emotional consistency and emotional difference) significantly affect customer knowledge contribution. This study contributes to knowledge contribution literature by showing the emotional and informational influence, and provides insights for community managers

Does gratitude enhance prosociality: a meta-analytic review

Theoretical models suggest that gratitude is linked to increased prosociality. To date, however, there is a lack of a comprehensive quantitative synthesis of results to support this claim. In this review we aimed to 1) examine the overall strength of the association between gratitude and prosociality, and 2) to identify the theoretical and methodological variables that moderate this link. We identified 252 effect sizes from 91 studies across 65 papers— (Total N = 18,342 participants). The present meta-analysis revealed a statistically significant, and moderate positive correlation between gratitude and prosociality (r = 0.374). This association was significantly larger among studies that assessed reciprocal outcomes relative to non-reciprocal outcomes, and in particular among studies that examined direct—compared to indirect—reciprocity. Studies that examined gratitude as an affective state reported significantly larger effect size studies assessing gratitude as a trait. Studies that examined benefit-triggered gratitude (in response to other’s kindness) had a stronger effect that generalized gratitude that focuses on the appreciation of what is valued and cherished in life. Finally, studies that manipulated gratitude in-vivo (e.g., economic games) had larger effect sizes compared to those based on recalled incidents when the person felt grateful. We describe the theoretical and practical significance of the results