A proposed role for models and simulations in management research

Overall, computer models and simulations have a rather disappointing record within the management sciences as a tool for predicting the future. Social and market environments can be influenced by an overwhelming number of variables, and it is therefore difficult to use computer models to make forecasts or to test hypotheses concerning the relationship between individual behaviours and macroscopic outcomes. At the same time, however, advocates of computer models argue that they can be used to overcome the human mind's inability to cope with several complex variables simultaneously or to understand concepts that are highly counterintuitive. This paper seeks to bridge the gap between these two perspectives by suggesting that management research can indeed benefit from computer models by using them to formulate fruitful hypotheses.

Understanding the phenomenon of IS success in China through the lens of the IS-Impact model

This study is conducted within the IS-Impact Research Track at Queensland University of Technology (QUT). The goal of the IS-Impact Track is, “to develop the most widely employed model for benchmarking information systems in organizations for the joint benefit of both research and practice” (Gable et al, 2006). IS-Impact is defined as “a measure at a point in time, of the stream of net benefits from the IS, to date and anticipated, as perceived by all key-user-groups” (Gable Sedera and Chan, 2008). Track efforts have yielded the bicameral IS-Impact measurement model; the “impact” half includes Organizational-Impact and Individual-Impact dimensions; the “quality” half includes System-Quality and Information-Quality dimensions. The IS-Impact model, by design, is intended to be robust, simple and generalizable, to yield results that are comparable across time, stakeholders, different systems and system contexts. The model and measurement approach employ perceptual measures and an instrument that is relevant to key stakeholder groups, thereby enabling the combination or comparison of stakeholder perspectives. Such a validated and widely accepted IS-Impact measurement model has both academic and practical value. It facilitates systematic operationalization of a main dependent variable in research (IS-Impact), which can also serve as an important independent variable. For IS management practice it provides a means to benchmark and track the performance of information systems in use. The objective of this study is to develop a Mandarin version IS-Impact model, encompassing a list of China-specific IS-Impact measures, aiding in a better understanding of the IS-Impact phenomenon in a Chinese organizational context. The IS-Impact model provides a much needed theoretical guidance for this investigation of ES and ES impacts in a Chinese context. The appropriateness and soundness of employing the IS-Impact model as a theoretical foundation are evident: the model originated from a sound theory of IS Success (1992), developed through rigorous validation, and also derived in the context of Enterprise Systems. Based on the IS-Impact model, this study investigates a number of research questions (RQs). Firstly, the research investigated what essential impacts have been derived from ES by Chinese users and organizations [RQ1]. Secondly, we investigate which salient quality features of ES are perceived by Chinese users [RQ2]. Thirdly, we seek to answer whether the quality and impacts measures are sufficient to assess ES-success in general [RQ3]. Lastly, the study attempts to address whether the IS-Impact measurement model is appropriate for Chinese organizations in terms of evaluating their ES [RQ4]. An open-ended, qualitative identification survey was employed in the study. A large body of short text data was gathered from 144 Chinese users and 633 valid IS-Impact statements were generated from the data set. A generally inductive approach was applied in the qualitative data analysis. Rigorous qualitative data coding resulted in 50 first-order categories with 6 second-order categories that were grounded from the context of Chinese organization. The six second-order categories are: 1) System Quality; 2) Information Quality; 3) Individual Impacts;4) Organizational Impacts; 5) User Quality and 6) IS Support Quality. The final research finding of the study is the contextualized Mandarin version IS-Impact measurement model that includes 38 measures organized into 4 dimensions: System Quality, information Quality, Individual Impacts and Organizational Impacts. The study also proposed two conceptual models to harmonize the IS-Impact model and the two emergent constructs – User Quality and IS Support Quality by drawing on previous IS effectiveness literatures and the Work System theory proposed by Alter (1999) respectively. The study is significant as it is the first effort that empirically and comprehensively investigates IS-Impact in China. Specifically, the research contributions can be classified into theoretical contributions and practical contributions. From the theoretical perspective, through qualitative evidence, the study test and consolidate IS-Impact measurement model in terms of the quality of robustness, completeness and generalizability. The unconventional research design exhibits creativity of the study. The theoretical model does not work as a top-down a priori seeking for evidence demonstrating its credibility; rather, the study allows a competitive model to emerge from the bottom-up and open-coding analysis. Besides, the study is an example extending and localizing pre-existing theory developed in Western context when the theory is introduced to a different context. On the other hand, from the practical perspective, It is first time to introduce prominent research findings in field of IS Success to Chinese academia and practitioner. This study provides a guideline for Chinese organizations to assess their Enterprise System, and leveraging IT investment in the future. As a research effort in ITPS track, this study contributes the research team with an alternative operationalization of the dependent variable. The future research can take on the contextualized Mandarin version IS-Impact framework as a theoretical a priori model, further quantitative and empirical testing its validity.

Classification of packet contents for malware detection

Many existing schemes for malware detection are signature-based. Although they can effectively detect known malwares, they cannot detect variants of known malwares or new ones. Most network servers do not expect executable code in their in-bound network traffic, such as on-line shopping malls, Picasa, Youtube, Blogger, etc. Therefore, such network applications can be protected from malware infection by monitoring their ports to see if incoming packets contain any executable contents. This paper proposes a content-classification scheme that identifies executable content in incoming packets. The proposed scheme analyzes the packet payload in two steps. It first analyzes the packet payload to see if it contains multimedia-type data (such as . If not, then it classifies the payload either as text-type (such as or executable. Although in our experiments the proposed scheme shows a low rate of false negatives and positives (4.69% and 2.53%, respectively), the presence of inaccuracies still requires further inspection to efficiently detect the occurrence of malware. In this paper, we also propose simple statistical and combinatorial analysis to deal with false positives and negatives.

Evaluation framework for focused link discovery

Information has no value unless it is accessible. Information must be connected together so a knowledge network can then be built. Such a knowledge base is a key resource for Internet users to interlink information from documents. Information retrieval, a key technology for knowledge management, guarantees access to large corpora of unstructured text. Collaborative knowledge management systems such as Wikipedia are becoming more popular than ever; however, their link creation function is not optimized for discovering possible links in the collection and the quality of automatically generated links has never been quantified. This research begins with an evaluation forum which is intended to cope with the experiments of focused link discovery in a collaborative way as well as with the investigation of the link discovery application. The research focus was on the evaluation strategy: the evaluation framework proposal, including rules, formats, pooling, validation, assessment and evaluation has proved to be efficient, reusable for further extension and efficient for conducting evaluation. The collection-split approach is used to re-construct the Wikipedia collection into a split collection comprising single passage files. This split collection is proved to be feasible for improving relevant passages discovery and is devoted to being a corpus for focused link discovery. Following these experiments, a mobile client-side prototype built on iPhone is developed to resolve the mobile Search issue by using focused link discovery technology. According to the interview survey, the proposed mobile interactive UI does improve the experience of mobile information seeking. Based on this evaluation framework, a novel cross-language link discovery proposal using multiple text collections is developed. A dynamic evaluation approach is proposed to enhance both the collaborative effort and the interacting experience between submission and evaluation. A realistic evaluation scheme has been implemented at NTCIR for cross-language link discovery tasks.

The child, the text and the teacher : reading primers and reading instruction

From the late sixteenth century, in response to the problem of how best to teach children to read, a variety of texts such as primers, spellers and readers were produced in England for vernacular instruction. This paper describes how these materials were used by teachers to develop first, a specific religious understanding according to the stricture of the time and second, a moral reading practice that provided the child with a guide to secular conduct. The analysis focuses on the use of these texts as a productive means for shaping the child-reader in the context of newly emerging educational spaces which fostered a particular, morally formative relation among teacher, child and text.

Some empirical results for neo-Piagetian reasoning in novice programmers and the relationship to code explanation questions

Recent research on novice programmers has suggested that they pass through neo-Piagetian stages: sensorimotor, preoperational, and concrete operational stages, before eventually reaching programming competence at the formal operational stage. This paper presents empirical results in support of this neo-Piagetian perspective. The major novel contributions of this paper are empirical results for some exam questions aimed at testing novices for the concrete operational abilities to reason with quantities that are conserved, processes that are reversible, and properties that hold under transitive inference. While the questions we used had been proposed earlier by Lister, he did not present any data for how students performed on these questions. Our empirical results demonstrate that many students struggle to answer these problems, despite the apparent simplicity of these problems. We then compare student performance on these questions with their performance on six explain in plain English questions.

A two-stage decision model for information filtering

Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems.

Rule-based approach for identifying assertions in clinical free-text data

A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.

Trust-based automated recommendation making

Recommender systems are one of the recent inventions to deal with ever growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbours, generated from a database made up of the preferences of past users. With sufficient background information of item ratings, its performance is promising enough but research shows that it performs very poorly in a cold start situation where there is not enough previous rating data. As an alternative to ratings, trust between the users could be used to choose the neighbour for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world "friend of a friend" recommendations. To extend the boundaries of the neighbour, an effective trust inference technique is required. This thesis proposes a trust interference technique called Directed Series Parallel Graph (DSPG) which performs better than other popular trust inference algorithms such as TidalTrust and MoleTrust. Another problem is that reliable explicit trust data is not always available. In real life, people trust "word of mouth" recommendations made by people with similar interests. This is often assumed in the recommender system. By conducting a survey, we can confirm that interest similarity has a positive relationship with trust and this can be used to generate a trust network for recommendation. In this research, we also propose a new method called SimTrust for developing trust networks based on user's interest similarity in the absence of explicit trust data. To identify the interest similarity, we use user's personalised tagging information. However, we are interested in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbours used in the automated recommender system. Our experimental results show that our proposed tag-similarity based method outperforms the traditional collaborative filtering approach which usually uses rating data.

The role of education and awareness in workplace alcohol and drug use in the Australian construction industry : proposed program of research and preliminary results

The main aim of this paper is to outline a proposed program of research which will attempt to quantify the extent of the problem of alcohol and other drugs in the Australian construction industry, and furthermore, develop an appropriate industry-wide policy and cultural change management program and implementation plan to address the problem. This paper will also present preliminary results from the study. The study will use qualitative and quantitative methods (in the form of interviews and surveys, respectively) to evaluate the extent of the problem of alcohol and other drug use in this industry, to ascertain the feasibility of an industry-wide policy and cultural change management program, and to develop an appropriate implementation plan. The study will be undertaken in several construction organisations, at selected sites in South Australia, Victoria and Northern Territory. It is anticipated that approximately 500 employees from the participating organisations across Australia will take part in the study. The World Health Organisation’s Alcohol Use Disorders Identification Test (AUDIT) will be used to measure the extent of alcohol use in the industry. Illicit drug use, ‘‘readiness to change’’, impediments to reducing impairment, feasibility of proposed interventions, and employee attitudes and knowledge regarding workplace AOD impairment, will also be measured through a combination of interviews and surveys. Among the preliminary findings, for 51% (n=127) of respondents, score on the AUDIT indicated alcohol use at hazardous levels. Of the respondents who were using alcohol at hazardous levels, 76% reported (n97) that they do not have a problem with drinking and 54% (n=68) reported that it would be easy to ‘‘cut down’’ or stop drinking. Nearly half (49%) of all respondents (n=122) had used marijuana/cannabis at some time prior to being surveyed. The use of other illicit substances was much less frequently reported. Preliminary interview findings indicated a lack of adequate employee knowledge regarding the physical effects of alcohol and other drugs in the workplace. As for conclusions, the proposed study will address a major gap in the literature with regard to the extent of the problem of alcohol and other drug use in the construction industry in Australia. The study will also develop and implement a national, evidence-based workplace policy, with the aim of mitigating the deleterious effects of alcohol and other drugs in this industry.

Loss and grief at the heart of text messages [REVIEW]

WHAT if you lost someone you loved? What if you had to let go for the sake of your own sanity? Lachlan Philpott's Colder and Dennis Kelly's Orphans, playing as part of La Boite's and Queensland Theatre Company's independents programs, are emotionally and textually dense theatrical works...

Scalability of encryption techniques for electronic health record (EHR) retrieval

Electronic Health Record (EHR) retrieval processes are complex demanding Information Technology (IT) resources exponentially in particular memory usage. Database-as-a-service (DAS) model approach is proposed to meet the scalability factor of EHR retrieval processes. A simulation study using ranged of EHR records with DAS model was presented. The bucket-indexing model incorporated partitioning fields and bloom filters in a Singleton design pattern were used to implement custom database encryption system. It effectively provided faster responses in the range query compared to different types of queries used such as aggregation queries among the DAS, built-in encryption and the plain-text DBMS. The study also presented with constraints around the approach should consider for other practical applications.

The proposal on the effective query mechanism for the encrypted database

In the recent past, there are some social issues when personal sensitive data in medical database were exposed. The personal sensitive data should be protected and access must be accounted for. Protecting the sensitive information is possible by encrypting such information. The challenge is querying the encrypted information when making the decision. Encrypted query is practically somewhat tedious task. So we present the more effective method using bucket index and bloom filter technology. We find that our proposed method shows low memory and fast efficiency comparatively. Simulation approaches on data encryption techniques to improve health care decision making processes are presented in this paper as a case scenario.

Sequential fusion of decisions with favorable dependence for controlled verification errors

Statistical dependence between classifier decisions is often shown to improve performance over statistically independent decisions. Though the solution for favourable dependence between two classifier decisions has been derived, the theoretical analysis for the general case of 'n' client and impostor decision fusion has not been presented before. This paper presents the expressions developed for favourable dependence of multi-instance and multi-sample fusion schemes that employ 'AND' and 'OR' rules. The expressions are experimentally evaluated by considering the proposed architecture for text-dependent speaker verification using HMM based digit dependent speaker models. The improvement in fusion performance is found to be higher when digit combinations with favourable client and impostor decisions are used for speaker verification. The total error rate of 20% for fusion of independent decisions is reduced to 2.1% for fusion of decisions that are favourable for both client and impostors. The expressions developed here are also applicable to other biometric modalities, such as finger prints and handwriting samples, for reliable identity verification.

Unsupervised multi-label text classification using a world knowledge ontology

The development of text classification techniques has been largely promoted in the past decade due to the increasing availability and widespread use of digital documents. Usually, the performance of text classification relies on the quality of categories and the accuracy of classifiers learned from samples. When training samples are unavailable or categories are unqualified, text classification performance would be degraded. In this paper, we propose an unsupervised multi-label text classification method to classify documents using a large set of categories stored in a world ontology. The approach has been promisingly evaluated by compared with typical text classification methods, using a real-world document collection and based on the ground truth encoded by human experts.