Event history, spatial analysis and count data methods for empirical research in information systems

A large number of interesting business and technology problems in IS and e-commerce research center around events and the associated variables that influence them. Researchers are often interested in studying the timing, patterns, and frequencies of events. Some of those events are related to the timing of strategic decisions such as new technology adoption, functionality upgrades to established software products, new outsourcing contracts, and the termination of failing IS projects. Still others are external events that have significant implications on the performance of firms, the structure of industries affected by IT, and the viability of various aspects of the economy. Event history methods, also known as survival analysis and duration analysis methods, spatial analysis, and count data analysis in the medical sciences, public health and biostatistics literature, offer rigorous methods for empirical analysis that can provide rich insights into research issues that arise in association with identifiable events. This article provides a current survey of these methods and in-depth discussion of how researchers can apply them to study technology adoption problems and related issues in IS and e-commerce. We offer a framework for mapping the methods to applicable problems, and discuss the relevant variants of the methods. We also illustrate the range of research questions that can be asked and answered through the use of the methods.     

Process-mining enabled feedback: “Tell me what I did wrong” vs. “tell me how to do it right”

Fast advancement of technology has led to an increased interest for using information technology to provide feedback based on learning behavior observations. This work outlines a novel approach for analyzing behavioral learner data through the application of process mining techniques specifically targeting a complex problem solving process. We realize this in the context of one particular learning case, namely, domain modeling. This work extends our previous research on process-mining analysis of domain modeling behavior of novices by elaborating with new insights from a replication study enhanced with an extra observation on how novices verify/validate models. The findings include a set of typical modeling and validation patterns that can be used to improve teaching guidance for domain modeling courses. From a scientific viewpoint, the results contribute to improving our knowledge on the cognitive aspects of problem-solving behavior of novices in the area of domain modeling, specifically regarding process-oriented feedback as opposed to traditional outcome feedback (is a solution correct? Why (not)?) usually applied in this type of courses. Ultimately, the outcomes of the work can be inspirational outside of the area of domain modeling as learning event data is becoming readily available through virtual learning environments and other information systems.     

Computer-mediated group influence on ethical behavior

Drawing on previous research in ethical behavior in information technology, this study examines the effects of group discussion, using virtual teams, on an individual’s intention to behave ethically/unethically. It was hypothesized that behavioral intention would be influenced by an individual’s attitude (toward ethical behavior), personal normative beliefs, ego strength, locus of control, perceived importance, gender and the scenario, and that computer-mediated group discussion would impact an individual’s ethical behavioral intention. This was tested through an experiment using five different ethical scenarios involving information technology. The results show that for two of the five scenarios, individual behavioral intention was significantly more unethical after computer-mediated group discussion than before, while for one scenario, individual behavioral intention was significantly more ethical after computer-mediated group discussion than before. The results of this study may help organizations to develop realistic training programs for IT professionals that account for changes in employee’s personal ethical models after interacting with others.     

严肃游戏中虚拟角色行为建模综述

严肃游戏是计算机游戏一个新的发展方向,可以提供形象互动的模拟教学环境,已经广泛应用于科学教育、康复医疗、应急管理、军事训练等领域。虚拟角色是严肃游戏中模拟具有生命特征的图形实体,行为可信的虚拟角色能够提升用户使用严肃游戏的体验感。严肃游戏中的图形渲染技术已经逐步成熟,而虚拟角色行为建模的研究尚在初级阶段。可信的虚拟角色必须能够具有感知、情绪和行为能力。本文分别从游戏剧情与行为、行为建模方法、行为学习和行为建模评价等4个方面来分析虚拟角色行为建模研究。分析了有限状态机和行为树的特点,讨论了虚拟角色的行为学习方法。指出了强化学习的关键要素,探讨了深度强化学习的应用途径。综合已有研究,归纳了虚拟角色行为框架,该框架主要包括感觉输入、知觉分析、行为决策和动作4大模块。从情感计算的融入、游戏剧情和场景设计、智能手机平台和多通道交互4个角度讨论需要进一步研究的问题。虚拟角色的行为建模需要综合地考虑游戏剧情、机器学习和人机交互技术,构建具有自主感知、情绪、行为、学习能力、多通道交互的虚拟角色能够极大地提升严肃游戏的感染力,更好地体现寓教于乐。     

Avatar creation in virtual worlds: Behaviors and motivations

Avatar creation has become common for people to participate and interact in virtual worlds. Using an online survey (N = 244), we investigated both the behavioral characteristics and major motivations for avatar creation in virtual worlds. Our results suggest that a majority of the participants had multiple avatars; these avatars’ appearance did not merely resemble the human players; and their personality did not necessarily mirror the player’s real personality. Furthermore, participants on average spent over 20 h per week and often interacting with others in the virtual worlds. Our exploratory factor analysis yielded four major motivations: virtual exploration, social navigation, contextual adaptation, and identity representation.     

Defining the virtual self: Personality,behavior, and the psychology of embodiment

Although researchers have discussed the existence of a virtual self, or embodiment of human characteristics within an avatar, little known about how the virtual self influences a player’s behavior within a virtual environment. To better understand this relationship, World of Warcraft game players were asked to complete personality-rating scales for both themselves and their avatars. In addition, in-world behavior was recorded and then analyzed using a behavioral assessment checklist. Results suggested a relationship between personality and behavior within the domain of agreeableness. Based on these findings, the researchers discuss implications for the construct known as the virtual self, as well as the inclusion of psychological systems design into the overall game design process.     

基于概念邮件系统的犯罪数据挖掘新方法

将数据挖掘技术应用于反犯罪和反恐怖是目前各国安全部门的研究热点。目前国内在分析犯罪和恐怖团伙之间联系行为等方面的研究工作有限。本文主要做了下列探索：（1）建立了一个可用的基于邮件用户个性特征和情报属性的概念仿真邮件系统CEM（Conceptual based EMail system）,模拟潜在的犯罪和恐怖组织利用电子邮件进行通信的规律;（2）利用符合个性特征和情报属性上的正态分布,模拟真实的邮件进行数据的收发;（3）使用社会网络分析和时间序列分析方法对邮件通信量进行深层次分析,挖掘有意义的邮件通信模式,进而发现异常通信行为;（4）通过实验证明CEM系统具有很好的鲁棒性和伸缩性,可以准确地模拟大量用户的邮件收发,解决了目前仿真数据不足的缺点,并用于发现不同性格特征群体收发邮件的规律。     

Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach

Cloud computing is an innovative information technology that has been applied to education and has facilitated the development of cloud computing classrooms; however, student behavioral intention (BI) toward cloud computing remains unclear. Most researchers have evaluated, integrated, or compared only few theories to examine user BI. In this study, we tested, compared, and unified six well-known theories, namely service quality (SQ), self-efficacy (SE), the motivational model (MM), the technology acceptance model (TAM), the theory of reasoned action or theory of planned behavior (TRA/TPB), and innovation diffusion theory (IDT), in the context of cloud computing classrooms. This empirical study was conducted using an online survey. The data collected from the samples (n = 478) were analyzed using structural equation modeling. We independently analyzed each theory, by formulating a united model. The analysis yielded three valuable findings. First, all six theoretical models and the united model exhibited adequate explanatory power. Second, variance explanation, Chi-squared statistics, effect size, and predictive relevance results revealed the ranking importance of the theoretical models. Third, the united model provided a comprehensive understanding of the factors that significantly affect the college students’ BI toward a cloud computing classroom. The discussions and implications of this study are critical for researchers and practitioners.     

Assessing and governing IT-staff behavior by performance-based simulation

When optimizing IT operations organizations typically aim to optimize resource usage. In general, there are two kinds of IT resources – IT infrastructures and IT staff. An optimized utilization of these resources requires both quantitative and qualitative analysis. While IT infrastructures can offer raw data for such analyses, data about IT staff often requires additional preparation and augmentation. One source for IT staff-related data can be provided by incident management and ticketing systems. While performance data from such systems is often stored in logfiles it is rarely evaluated extensively. In this article we propose the usage of such data sources for IT staff behavior evaluation and also present the relevant augmentation techniques. We claim that our approach is able to provide more in-depth insights as compared to typical data visualization and dashboard techniques. Our modeling methodology is based on the approach of system dynamics.     

Mining the change of customer behavior in fuzzy time-interval sequential patterns

Comprehending changes of customer behavior is an essential problem that must be faced for survival in a fast-changing business environment. Particularly in the management of electronic commerce (EC), many companies have developed on-line shopping stores to serve customers and immediately collect buying logs in databases. This trend has led to the development of data-mining applications. Fuzzy time-interval sequential pattern mining is one type of serviceable data-mining technique that discovers customer behavioral patterns over time. To take a shopping example, (Bread, Short, Milk, Long, Jam), means that Bread is bought before Milk in a Short period, and Jam is bought after Milk in a Long period, where Short and Long are predetermined linguistic terms given by managers. This information shown in this example reveals more general and concise knowledge for managers, allowing them to make quick-response decisions, especially in business. However, no studies, to our knowledge, have yet to address the issue of changes in fuzzy time-interval sequential patterns. The fuzzy time-interval sequential pattern, (Bread, Short, Milk, Long, Jam), became available in last year; however, is not a trend this year, and has been substituted by (Bread, Short, Yogurt, Short, Jam). Without updating this knowledge, managers might map out inappropriate marketing plans for products or services and dated inventory strategies with respect to time-intervals. To deal with this problem, we propose a novel change mining model, MineFuzzChange, to detect the change in fuzzy time-interval sequential patterns. Using a brick-and-mortar transactional dataset collected from a retail chain in Taiwan and a B2C EC dataset, experiments are carried out to evaluate the proposed model. We empirically demonstrate how the model helps managers to understand the changing behaviors of their customers and to formulate timely marketing and inventory strategies.     

B model: A browsing behavior model based on High-Level Petri Nets to generate behavioral patterns for e-learning

As Internet use has proliferated, e-learning systems have become increasingly popular. Many researchers have taken a great deal of effort to promote high quality e-learning environments, such as adaptive learning environments, personalized/adaptive guidance mechanisms, and so on. These researches need to collect large amounts of behavioral patterns for the verification and/or experimentation. However, collecting sufficient behavioral patterns usually takes a great deal of time and effort. To solve this problem, this paper proposes a browsing behavior model (B² model) based on High-Level Petri Nets (HLPNs) to model and generate students’ behavioral patterns. The adopted HLPN contains (1) Colored Petri Nets (CPNs), in which colored tokens can be used to identify and separate student, learning content and assessment, and (2) Timed Petri Nets (TPNs), in which time variable can be used to represent the time at which a student reads learning content. Besides, to validate the viability of the B² model, this paper implements a B² modeling tool to generate behavioral patterns. The generated behavioral patterns are compared with actual behavioral patterns collected from elementary school students. The results confirm that the generated behavioral patterns are analogous to actual behavioral patterns.     

Purchase behavior in virtual worlds: An empirical investigation in Second Life

Virtual worlds are an emerging online transaction context in which millions of players around the world participate and trade virtual items with one another. However, little research has been conducted into purchase behavior in this new context. To address this gap, we developed and tested a conceptual model of purchase behavior in virtual worlds using a combination of existing and new constructs. An online survey was conducted within Second Life (n = 250) and tested using structural equation modeling. We conclude with implications for practice and research limitations.     

The effects of group size and task complexity on deadline reactivity

In contemporary organizations, people are often required to work and learn under increasing time pressures as organizations set deadlines in order to respond to stiffer competition. Human behavioral patterns in the presence of deadlines have been studied quantitatively to show that relatively little time is devoted to tasks early on and that most work is performed in close time proximity to a deadline. This phenomenon, called deadline rush, can be explained by a hyperbolic behavioral model. By employing a decision-making task based on an Anti-Air Warfare Coordinator (AAWC) simulator, the experiment had two group size levels (individuals and teams) and two task complexity levels (low and high). The experimental results showed that deadline reactivity is greater for individuals than teams on low-complexity tasks and task complexity is negatively related to deadline reactivity. The results of this study suggest that different group sizes and task types have a significant impact on production performance and that the setting of deadlines, to the degree possible, may be a relevant means towards managing or improving system performance.     

Deploying a modeling framework for reusable robot behavior to enable informed strategies for domestic service robots

For the development of multi-purpose robots that can operate in a wide range of different situations we will need sophisticated behavioral building blocks to compose the desired performances. In this paper we argue for a behavior modeling framework which provides specific behavior interfaces for implementing robot skills that stay close to evaluation cycles and to rich fused sensory data. This way we ensure reusability and facilitate what we call Informed Strategies. As a specific application we deploy the framework to an object search task for a domestic service robot. The presented behavior involves an attention mapping mechanism based on 2D and 3D visual cues. We show the advantages of the proposed approach by conducting an evaluation in a real-world apartment scenario as well as by successfully taking part in the RoboCup@HOME competition.     

A framework for the operationalization of monitoring in business intelligence requirements engineering

Business intelligence (BI) is perceived as a critical activity for organizations and is increasingly discussed in requirements engineering (RE). RE can contribute to the successful implementation of BI systems by assisting the identification and analysis of such systems’ requirements and the production of the specification of the system to be. Within RE for BI systems, we focus in this paper on the following questions: (i) how the expectations of a BI system’s stakeholders can be translated into accurate BI requirements, and (ii) how do we operationalize specifically these requirements in a system specification? In response, we define elicitation axes for the documentation of BI-specific requirements, give a list of six BI entities that we argue should be accounted for to operationalize business monitoring, and provide notations for the modeling of these entities. We survey important contributions of BI to define elicitation axes, adapt existing BI notations issued from RE literature, and complement them with new BI-specific notations. Using the i* framework, we illustrate the application of our proposal using a real-world case study.     

A design-time data-centric maturity model for assessing resilience in multi-party business processes

Nowadays, every business organization operates in ecosystems and cooperation is mandatory. If, on the one hand, this increases the opportunities for the involved organizations, on the other hand, every business partner is a potential source of failures with impacts on the entire ecosystem. To avoid that these failures, which are local to one of the organizations, would block the whole cooperation, resilience is a feature that multi-party business processes currently support at run-time, to cope with unplanned situations caused by those failures.In this work, we consider awareness of resilience in multi-party business processes during design-time, by focusing on the role of available – as an alternative to unreliable – data as a resource for increasing resiliency, as data exchange usually drives the cooperation among the parties. In fact, a proper analysis of involved data allows the process designer to identify (possible) failures, their impact, and thus improve the process model at the outset. A maturity model for resilience awareness is proposed, based on a modeling notation extending OMG CMMN — Case Management Model and Notation, and it is organized in different resiliency levels, which allow designers (i) to model at an increasing degree of detail how data and milestones should be defined in order to have resilient by-design process models and (ii) to quantify the distance between a process model and the complete achievement of a resiliency level.     

Web usage mining for analysing elder self-care behavior patterns

The rapid growth of the elderly population has increased the need to support elders in maintaining independent and healthy lifestyles in their homes rather than through more expensive and isolated care facilities. Self-care can improve the competence of elderly participants in managing their own health conditions without leaving home. This main purpose of this study is to understand the self-care behavior of elderly participants in a developed self-care service system that provides self-care service and to analyze the daily self-care activities and health status of elders who live at home alone.To understand elder self-care patterns, log data from actual cases of elder self-care service were collected and analysed by Web usage mining. This study analysed 3391 sessions of 157 elders for the month of March, 2012. First, self-care use cycle, time, function numbers, and the depth and extent (range) of services were statistically analysed. Association rules were then used for data mining to find relationship between these functions of self-care behavior. Second, data from interest-based representation schemes were used to construct elder sessions. The ART2-enhance K-mean algorithm was then used to mine cluster patterns. Finally, sequential profiles for elder self-care behavior patterns were captured by applying sequence-based representation schemes in association with Markov models and ART2-enhanced K-mean clustering algorithms for sequence behavior mining cluster patterns for the elders. The analysis results can be used for research in medicine, public health, nursing and psychology and for policy-making in the health care domain.     

Communication Optimizations for Parallel C Programs

This paper presents algorithms for reducing the communication overhead for parallel C programs that use dynamically allocated data structures. The framework consists of an analysis phase called possible-placement analysis, and a transformation phase called communication selection. The fundamental idea of possible-placement analysis is to find all possible points for insertion of remote memory operations. Remote reads are propagated upwards, whereas remote writes are propagated downwards. Based on the results of the possible-placement analysis, the communication selection transformation selects the “best” place for inserting the communication and determines if pipelining or blocking of communication should be performed. The framework has been implemented in the EARTH-McCAT optimizing C compiler, and experimental results are presented for five pointer-intensive benchmarks running on the EARTH-MANNA distributed-memory parallel processor. These experiments show that the communication optimization can provide performance improvements of up to 16% over the unoptimized benchmarks.     

Host-based intrusion detection using dynamic and static behavioral models

Intrusion detection has emerged as an important approach to network security. In this paper, we adopt an anomaly detection approach by detecting possible intrusions based on program or user profiles built from normal usage data. In particular, program profiles based on Unix system calls and user profiles based on Unix shell commands are modeled using two different types of behavioral models for data mining. The dynamic modeling approach is based on hidden Markov models (HMM) and the principle of maximum likelihood, while the static modeling approach is based on event occurrence frequency distributions and the principle of minimum cross entropy. The novelty detection approach is adopted to estimate the model parameters using normal training data only, as opposed to the classification approach which has to use both normal and intrusion data for training. To determine whether or not a certain behavior is similar enough to the normal model and hence should be classified as normal, we use a scheme that can be justified from the perspective of hypothesis testing. Our experimental results show that the dynamic modeling approach is better than the static modeling approach for the system call datasets, while the dynamic modeling approach is worse for the shell command datasets. Moreover, the static modeling approach is similar in performance to instance-based learning reported previously by others for the same shell command database but with much higher computational and storage requirements than our method.