Personalization Improves Gamification

Personalization of gamification is an alternative to overcome the shortcomings of the one-size-fits-all approach, but the few empirical studies analyzing its effects do not provide conclusive results. While many user and contextual information affect gamified experiences, prior personalized gamification research focused on a single user characteristic/dimension. Therefore, we hypothesize if a multidimensional approach for personalized gamification, considering multiple (user and contextual) information, can improve user motivation when compared to the traditional implementation of gamification. In this paper, we test that hypothesis through a mixed-methods sequential explanatory study. First, 26 participants completed two assessments using one of the two gamification designs and self-reported their motivations through the Situational Motivation Scale. Then, we conducted semi-structured interviews to understand learners' subjective experiences during these assessments. As result, the students using the personalized design were more motivated than those using the one-size-fits-all approach regarding intrinsic motivation and identified regulation. Furthermore, we found the personalized design featured game elements suitable to users' preferences, being perceived as motivating and need-supporting. Thus, informing i) practitioners on the use of a strategy for personalizing gamified educational systems that is likely to improve students' motivations, compared to OSFA gamification, and ii) researchers on the potential of multidimensional personalization to improve single-dimension strategies. For transparency, dataset and analysis procedures are available at https://osf.io/grzhp.

Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems

One-way carsharing system has been widely adopted in the carsharing field due to its flexible services. However, as one of the main limitations of the one-way carsharing system, the imbalance between supply and demand needs to be solved. Predicting pick-up demand has been studied to achieve the goal, but using returned vehicles to reduce unnecessary relocation is also one of the important methods. Nowadays, trajectory data and other data are available for real-time prediction for return demand. Based on the return demand prediction, the relocation response can be more reasonable. Thus, the balance of demand and supply can be largely improved. The multisource data include trajectory data, user application log data, order data, station data, and user characteristic data. Based on these data, a return demand prediction model was used to predict whether the user will return the vehicle in 15 min in real time, and a destination station prediction model was applied to forecast which station the user will park at. Finally, a case study using ten stations’ one-week field data was conducted to test the benefit of the dynamic return demand prediction. The results showed that the return demand prediction improves the efficiency of the relocations by mitigating the condition that the station parking space is full or empty. The potential application of this study would effectively reduce unnecessary relocation and further formulate an active operation optimization strategy to reduce the system’s operational cost and improve the service quality of the system.

Understanding the evaluation of mHealth app features based on a cross-country Kano analysis

While mobile health (mHealth) apps play an increasingly important role in digitalized health care, little is known regarding the effects of specific mHealth app features on user satisfaction across different healthcare system contexts. Using personal health record (PHR) apps as an example, this study identifies how potential users in Germany and Denmark evaluate a set of 26 app features, and whether evaluation differences can be explained by the differences in four pertinent user characteristics, namely privacy concerns, mHealth literacy, mHealth self-efficacy, and adult playfulness. Based on survey data from both countries, we employed the Kano method to evaluate PHR features and applied a quartile-based sample-split approach to understand the underlying relationships between user characteristics and their perceptions of features. Our results not only reveal significant differences in 14 of the features between Germans and Danes, they also demonstrate which of the user characteristics best explain each of these differences. Our two key contributions are, first, to explain the evaluation of specific PHR app features on user satisfaction in two different healthcare contexts and, second, to demonstrate how to extend the Kano method in terms of explaining subgroup differences through user characteristic antecedents. The implications for app providers and policymakers are discussed.

The effect of task characteristics on enterprise resource planning system success with user characteristics and organizational characteristics as mediating variables

Enterprise Resource Planning(ERP) is an integrated system that provides a wide range of benefits for the company, but there are still many companies who have unsuccessfully implemented ERP system. This study aimed to analyze the effect of the task characteristic and the success of Enterprise Resource Planning (ERP) on the success of ERP, with the user characteristic and organizational characteristic as mediating variables. The data were collected by using a questionnaire distributed directly to users of Enterprise Resource Planning (ERP) in Indonesian companies listed in Indonesia Stock Exchange (ISE). The data were processed by using Partial Least Square (PLS). It was found that the suitability of the task significantly affects the success of an ERP system, either directly or mediated by the user variable characteristic and organizational characteristic. Task difficulty also significantly affects the success of an ERP system if mediated by the convenience variable (user characteristic). The success of the ERP system is determined by based tasks and task complexity through the system the user's convenience in using the information system.

Towards Personalized Review Summarization via User-Aware Sequence Network

We address personalized review summarization, which generates a condensed summary for a user’s review, accounting for his preference on different aspects or his writing style. We propose a novel personalized review summarization model named User-aware Sequence Network (USN) to consider the aforementioned users’ characteristics when generating summaries, which contains a user-aware encoder and a useraware decoder. Specifically, the user-aware encoder adopts a user-based selective mechanism to select the important information of a review, and the user-aware decoder incorporates user characteristic and user-specific word-using habits into word prediction process to generate personalized summaries. To validate our model, we collected a new dataset Trip, comprising 536,255 reviews from 19,400 users. With quantitative and human evaluation, we show that USN achieves state-ofthe-art performance on personalized review summarization.

Rencana Zoning Kawasan Wisata Air di Hutan Kota pada Lahan Bekas Tambang Batubara PT Bukit Asam, (Tbk) Tanjung Enim, Kabupaten Muara Enim

Mine activity can give adverse environmental impacts significantly. Land conversion as mine land demanding reclamation activities on demanding reclamation activities. Reclamation is an activity that is required for mining entrepreneurs. PT Bukit Asam us one of the largest mining company in South Sumatera, district Muara Enim committed to undertake reclamation activities which will be implemented in urban forest development H.Kalamudin Djinab, SH, MH on post land mining. The land area that will be built into the urban forest of 50 hectares consisting some zoning.The design method is carried out from field survey activities, and observation to obtain the required data as well as in compiling secondary and primary data. The analyzes in preparing zoning plan of urban forest consist of space requirement analysis, activity analysis, user characteristic analysis, space organization analysis, site analysis and zoning area. So that zoning of urban forest area will be developed into several different functions in each area

Perceived CSR initiatives and intention to purchase game items

PurposeThe purpose of this paper is to investigate the effect of perceived corporate social responsibility (CSR) initiatives moderated by a user characteristic (heavy users) on game-item purchase intention and uniquely propose that this moderation is serially mediated by self-esteem and compassion.Design/methodology/approachA 2 (CSR initiatives: high vs low) by 2 (user characteristic: heavy vs non-heavy users) experimental design was employed to test the propositions in the context of an online mobile game.FindingsThe results demonstrate that heavy users with high-perceived CSR initiatives have a higher intention to purchase game items. The results also show that self-esteem and compassion fully and serially mediate the effect of moderation on the intention to purchase game items.Originality/valueThis serial mediation mechanism has rarely been proposed and tested in previous studies and may contribute to extending the literature.