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Enterprise social media (ESM) platforms are rapidly diffusing in the business context because they can bring substantial benefits to companies by enhancing their knowledge management (KM) processes. However, such benefits materialize only if active employee participation is ensured. Therefore, it is crucial to understand how individual employees use an ESM platform to assist their knowledge-related activities. This paper contributes to this topic by proposing a classification of ESM users based on two dimensions: frequency and type (active or passive) of use.

The paper presents the results of a survey of 262 employees of an international engineering service company that has adopted an ESM platform to support its KM processes. Statistical methods (e.g. ANOVA, Tukey’s b) were applied to verify the usefulness of the proposed typology and identify the main aspects that characterize the different user groups.

The survey results confirm the existence of different types of ESM users and provide the empirical basis for developing a bi-dimensional classification from which four user groups were derived and characterized: frequent contributors, sporadic contributors, frequent lurkers and sporadic lurkers.

The main limitation is that only one company in one sector with specific knowledge needs and capabilities was investigated.

The study provides useful suggestions for how to promote the use of an ESM and particularly for how to encourage less frequent and less active users to increase their participation in a platform.

The paper contributes to a better understanding of how employees approach ESM by identifying factors that characterize different user groups.

The purpose of this article is to explore how participants with different motivations (educational or leisure), familiarity with the medium (newbies and active Twitter users), and participating instructions respond to a highly structured digital social reading (DSR) activity in terms of intensity of engagement and social interaction.

A case study involving students and teachers of 211 Italian high school classes and 242 other Twitter users, who generated a total of 18,962 tweets commenting on a literary text, was conducted. The authors performed both a quantitative analysis focusing on the number of tweets/retweets generated by participants and a network analysis exploiting the study of interactions between them. The authors also classified the tweets with respect to their originality, by using both automated text reuse detection approaches and manual categorization, to identify quotations, paraphrases and other forms of reader response.

The decoupling (both in space and time) of text read (in class) and comments (on Twitter) likely led users to mainly share text excerpts rather than original personal reactions to the story. There was almost no interaction outside the classroom, neither with other students nor with generic Twitter users, characterizing this project as a shared experience of “audiencing” a media event. The intensity of social interactions is more related to the breadth of the audience reached by the user-generated content and to a strong retweeting activity. In general, better familiarity with digital (social) media is related to an increase in the level of social interaction.

The authors analyzed one of the largest educational social reading projects ever realized, contributing to the still scarce empirical research about DSR. The authors employed state-of-the-art automated text reuse detection to classify reader response.

Although knowledge sharing in online communities has been studied for many years, little is known about the determinants for individuals' knowledge sharing in online health communities (OHCs) surrounding smoking cessation. Examining the determinants of knowledge sharing in such OHCs from the social capital perspective may prove particularly enlightening.

A questionnaire-based online user survey of two smoking cessation OHCs, one based in Finland and one based in China, was performed. Performing data analysis with partial least squares (SmartPLS 3.0), the authors developed a model conceptualizing the structural, cognitive and relational dimensions of social capital as drivers for knowledge sharing in smoking cessation OHCs, with users' stage in giving up smoking as a moderator.

The results show that structural capital (social ties) and relational capital (reciprocity) are important motivators behind knowledge sharing in smoking cessation OHCs, and the authors found a moderating effect of the stage in quitting on the antecedents' relationship with knowledge sharing in these OHCs.

The study enriches understanding of knowledge sharing in smoking cessation OHCs, contributing to theory and identifying practical implications for such groups' administration.

Human group behavior is the driving force behind many complex social and economic phenomena. Few studies have integrated multi-dimensional travel patterns and city interest points to construct urban security risk indicators. This paper combines traffic data and urban alarm data to analyze the safe travel characteristics of the urban population. The research results are helpful to explore the diversity of human group behavior, grasp the temporal and spatial laws and reveal regional security risks. It provides a reference for optimizing resource deployment and group intelligence analysis in emergency management.

Based on the dynamics index of group behavior, this paper mines the data of large shared bikes and ride-hailing in a big city of China. We integrate the urban interest points and travel dynamic characteristics, construct the urban traffic safety index based on alarm behavior and further calculate the urban safety index.

This study found significant differences in the travel power index among ride-sharing users. There is a positive correlation between user shared bike trips and the power-law bimodal phenomenon in the logarithmic coordinate system. It is closely related to the urban public security index.

Based on group-shared dynamic index integrated alarm, we innovatively constructed an urban public safety index and analyzed the correlation of travel alarm behavior. The research results fully reveal the internal mechanism of the group behavior safety index and provide a valuable supplement for the police intelligence analysis.

Mobile services are expanding rapidly, and in this tremendously dynamic environment, companies should provide value-added services to meet users’ demand. In this context, the study aims to determine whether different user groups exist in this market and profile them.

Based on the information of 568 mobile service users, a research was developed in the context of a mature mobile services market – Spain. A behavior-based cluster analysis is developed by means of confirmatory factor analysis (CFA), followed by two-step clustering. Then, an ANOVA and post hoc Tukey tests are conducted to confirm differences among the obtained clusters.

The study findings show that mobile service users cannot be perceived as a homogenous group, as different users with different behaviors coexist in this market. More specifically, four behavior-based segments emerge in the mobile service sector: “service connoisseurs,” “uninvolved pragmatics,” “potential switchers” and “delighted loyal”; “potential switchers” being the most challenging segment for mobile service companies.

This study reports mobile service users’ heterogeneity; and in turn, mobile service managers should consider customers as four different types, instead of considering them as one single customer.

Los servicios móviles se están expandiendo rápidamente y en este entorno tremendamente dinámico, las compañías de servicios móviles deberían de proporcionar servicios de valor añadido para satisfacer la demanda de sus usuarios. En este contexto, esta investigación tiene como objetivo determinar si existen diferentes grupo de usuarios en este mercado y aportar su pefil.

Sobre la base de información de 568 usuarios de servicios móviles se lleva a cabo una investigación en el contexto de un mercado de servicios móviles maduro –España-. Se lleva a cabo un análisis clúster basado en el comportamiento de los usuarios, mediante un análisis factorial confirmatorio (CFA), seguido de un análisis clúster bi-etápico. A continuación, se realizan un test Anova y un test pos hoc de Tuckey para confirmar las diferencias entre los grupos obtenidos.

Los resultados de la investigación muestran que los usuarios de servicios móviles no pueden ser percibidos como un grupo homogéneo, ya que en este mercado coexisten diferentes usuarios con diferentes comportamientos. Más específicamente, cuatro segmentos de usuarios surgen en el sector de los servicios móviles basados en su comportamiento: “conocedores del servicio”, “pragmáticos no involucrados”, “potenciales cambiadores de servicio” y “leales encantados”, siendo “potenciales cambiadores de servicio” los que presentan un mayor desafío las empresas de servicios móviles.

Este estudio muestra la heterogeneidad de los usuarios de servicios móviles, y por tanto, los gestores de los servicios móviles deben considerar a sus clientes como cuatro tipos diferentes, en lugar de considerarlos como un único tipo de cliente.

移动服务正在迅速扩张，在这个巨大的动态环境中，企业应该提供增值服务来满足用户的需求。在此背景下，本研究旨在确定该市场是否存在不同的用户群体，并对其进行分析。

基于568名移动服务用户的信息，在一个成熟的移动服务市场（西班牙）的背景下开展了本次研究。通过确认性因子分析（CFA）进行基于行为的聚类分析，然后进行两步聚类。最后，方差分析和事后Tukey检验被用于确认获得的聚类之间的差异。

研究结果表明，移动服务用户不能被看作是一个同质的群体，因为在这个市场上共存着不同的用户，他们具有不同的行为。具体来说，移动服务领域可以划分为基于用户行为的四个细分市场："服务行家"、"不参与的实用主义者"、"潜在的转换者 "和 "愉悦的忠诚者"；其中，"潜在的转换者 "是对移动服务企业来说最具挑战性的细分市场。

本研究报告了移动服务用户的异质性；反过来，移动服务管理者应该考虑把客户划分为四种不同的类型，而不是将他们视为一个单一的整体。

Instigated by the trend of collaborative economics, taking into consideration the particularities pointed out by Simis (2015), such as a new social, economic and technological scenario, this paper aims to identify which factors serve as motivators for collaboration among the technologies of this scenario.

In this paper, the authors analyzed Waze, an application that works based on traffic information made available by the users of the platform. For the development of this qualitative work, aspects that served as the basis for the interview script applied were classified into three different categories: utilization, personal values and gamification; the latter being an important aspect for collaboration in the application in question. Two distinct groups were interviewed; one of regular users of Waze, and the other of advanced users, who also work voluntarily in the maintenance of the app.

The results indicate that personal values are determinant factors for both groups; however, there are distinct incentives for the different users interviewed, like reciprocity, altruism and empathy. The first one is an important aspect to share information by the regular users, while the other ones are characteristics advanced users.

The study presents some limitations in terms of the restricted group and focus on only one of the many collaborative technological social, as well as lacking on-site analysis of the use of Waze.

This is a valid research when taking into consideration studies that approach the app Waze, as the issues explored in this paper are new in the academic studies considering previous app’s analyses that are related to functionality.

The results presented in this study help to understand the current relationship between sharing economy and the technology provided to those who use these platforms.

This research opens the horizon for future studies applied on other collaborative technological user platforms, to see if the results would be similar and construct a user journey in relation to information sharing through experimental monitoring.

The key concern nowadays is smartphone addiction and user profiles. Following the risk and protective factors framework, the authors aim to characterize smartphone users according to two levels: (1) individual: referred to the use (i.e. boredom proneness, compulsive app downloading smartphone addiction) and (2) microsystem: referred to family and peers (i.e. family harmony and phubbing). Besides, the authors will derive useful managerial implications and strategies.

First, an extensive literature revision and in-depth interviews with experts were employed to identify the addiction-related variables at the individual and microsystem level. Second, information was collected from a sample of 275 Spanish smartphone users, and a K-means clustering algorithm was employed to classify smartphone users.

The proposed traffic lights schema identifies three users’ profiles (red, yellow and green) regarding their smartphone addiction and considering individual and microsystem critical variables.

This study proposes a practical and pioneer traffic lights schema to classify smartphone users and facilitate each cluster's strategies development.

一個大家現時頗關注的事項就是智慧型手機癮和用戶檔案。遵循著風險和保護因子框架,我們擬根據兩個層面,去描述智慧型手機使用者的特性:(一) 個人層面: 這是指手機的使用方面 (即是:無聊傾向、強制性應用程式的下載、智慧型手機癮), 以及(二)微系統層面:這是指家庭和同儕 (即是:家庭和諧和低頭族)。此外,我們也會探討從中得到管理方面的啟示和一些有用的策略。

我們首先進行廣泛的文獻回顧和深入訪問有關的專家,以確定在個人和微系統層面上與入迷成癮有關的變數。繼而從275名西班牙智慧型手機使用者中收集資料,然後以K均值聚類算法把智慧型手機使用者分類。

我們提出的紅綠燈圖表,就用戶的智慧型手機癮,並考慮了個人和微系統的關鍵變數,識別了三個用戶檔案(紅、黃、綠)。

本研究提出一個實用及有倡導性的紅綠燈圖表,把智慧型手機使用者分類,並促進每個群集戰略的制定。

To cope with the COVID-19 pandemic, contact tracing mobile apps (CTMAs) have been developed to trace contact among infected individuals and alert people at risk of infection. To disrupt virus transmission until the majority of the population has been vaccinated, achieving the herd immunity threshold, CTMA continuance usage is essential in managing the COVID-19 pandemic. This study seeks to examine what motivates individuals to continue using CTMAs.

Following the coping theory, this study proposes a research model to examine CTMA continuance usage, conceptualizing opportunity appraisals (perceived usefulness and perceived distress relief), threat appraisals (privacy concerns) and secondary appraisals (perceived response efficacy) as the predictors of individuals' CTMA continuance usage during the pandemic. In the United States, an online survey was administered to 551 respondents.

The results revealed that perceived usefulness and response efficacy motivate CTMA continuance usage, while privacy concerns do not.

This study enriches the understanding of CTMA continuance usage during a public health crisis, and it offers practical recommendations for authorities.

In the presence of coronavirus disease 2019 (COVID-19), due to the social distance restriction, consumers' regular consumption behaviors and patterns have been changing fundamentally. Thereafter, an innovative group buying model has emerged and developed explosively with a specific focus on consumer's location, known as community-based group buying (CGB). The purpose of this paper is to investigate the transfer mechanism of user's trust in dyadic contexts of social and commercial role-playing in the CGB program.

This study adopts an empirical research method, with an online and offline questionnaire survey, a total of 382 responses have been obtained. Then, both descriptive analysis and hierarchical regression analysis are conducted to explore the dual roles of group leader and its corresponding effects on consumers' trust (i.e. emotional trust and behavioral trust) and engagement actions (i.e. purchase and share) in the CGB program.

Results indicate that resident's trust and their perception of group leader's friend role can positively enhance their engagement actions in the CGB programs. Meanwhile, for the purpose of profit maximization, the group leader is more willing to play a friend role in transactions no matter whether the role conflict exists.

Research findings provide some managerial insights for CGB platform on the selection and training of group leaders and the incentive mechanism design.

Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users.

The authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review.

The authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases.

Based on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent.

This literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.