Preface to selected papers from ACIS 2016

Prefac

PENGARUH CAR, NPL, LDR DAN BOPO TERHADAP PROFITABILITAS BANK BUMN DI BEI 2013-2017

Profitabilitas adalah aspek penting bagi bank karena menjadi tujuan utama bank didirikan. Profitabilitas dipengaruhi beberapa faktor yaitu kecukupan modal, masalah kredit, masalah likuiditas dan masalah efisiensi biaya. Tujuan penelitian ini untuk menguji pengaruh Capital Adequacy Ratio (CAR), Non Performing Loan (NPL), Loan to Deposit Ratio (LDR), Biaya Operasional terhadap Pendapatan Operasional (BOPO) Terhadap Profitabilitas. Jenis penelitian ini adalah kuantitatif. Populasi yang digunakan dalam penelitian ini adalah seluruh Bank BUMN yang terdaftar di Bursa Efek Indonesia. Pengambilan sampel menggunakan teknik sampel jenuh sehingga memperoleh sampel sebanyak 4 bank. Data yang dikumpulkan adalah data sekunder yang diperoleh dari Bursa Efek Indonesia. Teknik analisis data yang digunakan adalah analisis regresi linier berganda. Hasil dari teknik analisis data menunjukkan bahwa analisis regresi linier berganda memiliki hubungan negatif antara CAR, NPL, LDR dan BOPO dengan ROA; uji asumsi klasik yang digunakan telah memenuhi kriteria yang ditentukan; uji kelayakan model menunjukkan model ini layak untuk digunakan; uji t menunjukkan CAR dan LDR tidak berpengaruh terhadap ROA, Sedangkan NPL dan BOPO berpengaruh terhadap ROA. Manajemen bank sebaiknya memperhatikan total kredit yang akan diberikan kepada nasabah agar terjadinya kredit bermasalah dapat dihindari serta mengatur biaya operasional seefisien mungkin agar tidak berpengaruh negatif terhadap kinerja bank. Penelitian selanjutnya agar menambah rasio-rasio keuangan lainnya dan menambah periode tahun penelitian agar mendapatkan hasil penelitian yang lebih releva

Towards a knowledge leakage Mitigation framework for mobile Devices in knowledge-intensive Organizations

The use of mobile devices in knowledge-intensive organizations while effective and cost-efficient also pose a challenging management problem. Often employees whether deliberately or inadvertently are the cause of knowledge leakage in organizations and the use of mobile devices further exacerbates it. This problem is the result of overly focusing on technical controls while neglecting human factors. Knowledge leakage is a multidimensional problem, and in this paper, we highlight the different dimensions that constitute it. In this study, our contributions are threefold. First, we study knowledge leakage risk (KLR) within the context of mobile devices in knowledge-intensive organizations in Australia. Second, we present a conceptual framework to explain and categorize the mitigation strategies to combat KLR through the use of mobile devices grounded in the literature. And third, we apply the framework to the findings from interviews with security and knowledge managers. Keywords: Knowledge Leakage, Knowledge Risk, Knowledge intensive, Mobile device.Comment: 22 pages, ECIS full paper 201

TOWARDS A KNOWLEDGE LEAKAGE MITIGATION FRAMEWORK FOR MOBILE DEVICES IN KNOWLEDGE-INTENSIVE ORGANIZATIONS

The use of mobile devices in knowledge-intensive organizations while effective and cost-efficient also pose a challenging management problem. Often employees whether deliberately or inadvertently are the cause of knowledge leakage in organizations and the use of mobile devices further exacerbates it. This problem is the result of overly focusing on technical controls neglecting human factors. Knowledge leakage is a multidimensional problem, and in this paper, we highlight the different dimensions that constitute it. In this study, our contributions are threefold. First, we study knowledge leakage risk (KLR) within the context of mobile devices in knowledge-intensive organizations in Australia. Second, we present a conceptual framework to explain and categorize the mitigation strategies to combat KLR through the use of mobile devices grounded in the literature. And third, we apply the framework to the findings from interviews with security and knowledge managers. Keywords: Knowledge Leakage, Knowledge Risk, Knowledge intensive, Mobile device

Data Ecosystem Business Models: Value and control in Data Ecosystems

Purpose: Organizations evolve from using and governing data internally towards the exchange of data in multi-organizational data ecosystems. The purpose of this research is to determine a business model framework for actors operating in and/or entering a data ecosystem. Methodology: To determine a business model framework in data ecosystems. an analysis was made based on how the research fields of “business models”, “data governance”, “data ecosystems”, “data sharing”, “business ecosystem” complement each other. A business model framework was created, which was applied to three use case studies in the field of Smart Cities and Urban Digital Twins: The Helsinki Digital Twin, the Rotterdam Digital Twin, and the Smart Retail Dashboard in Flanders. Findings: The business model of actors in a data ecosystem is determined by value and control factors. Value is determined by the capability to create value through the exchange of data in the ecosystem, and to capture value through revenue (sharing) models and cost (sharing) models. Control is determined by ecosystem control. Governance models on the ecosystem level are required to enable the collaboration and to ensure trust to allow for the willingness to share data. Additionally, data governance on an ecosystem level is required, enabling the data exchange between the actors. Research Limitations: The model was applied to three use cases in Smart Cities and Urban Digital Twins. Consequently, the data ecosystems concern a high presence of public actors, yet also includes private companies. The applicability needs to be identified in other sectors in further research. Additionally, as the scope of the study was on business models, data governance, data-sharing and data ecosystems, abstraction was made of fields of study beyond these topics. Value and practical implications: The Data Ecosystem Business Model framework can serve as a guideline for organizations entering a data ecosystem, as well as for actors aiming to establish novel data ecosystems. Additionally, the framework can serve as a high-level overview for further research into the field of business models in data ecosystems.

Kolmiulotteisten tietokoneavusteisten mallien yksinkertaistaminen renderoinnin nopeuttamiseksi

Visualization of three-dimensional (3D) computer-aided design model is an integral part of the design process. Large assemblies such as plant or building designs contain a substantial amount of geometric data. New constraints for visualization performance and the amount of geometric data are set by the advent of mobile devices and virtual reality headsets. Our goal is to improve visualization performance and reduce memory consumption by simplifying 3D models while retaining the output simplification quality stable regardless of the geometric complexity of the input mesh. We research the current state of 3D mesh simplification methods that use geometry decimation. We design and implement our own data structure for geometry decimation. Based on the existing research, we select and use an edge decimation method for model simplification. In order to free the user from configuring edge decimation level per model by hand, and to retain a stable quality of the simplification output, we propose a threshold parameter, \textit{edge decimation cost threshold}. The threshold is calculated by multiplying the length of the model’s bounding box diagonal with a user-defined scale parameter. Our results show that the edge decimation cost threshold works as expected. The geometry decimation algorithm manages to simplify models with round surfaces with an excellent simplification rate. Based on the edge decimation cost threshold, the algorithm terminates the geometry decimation for models that have a large number of planar surfaces. Without the threshold, the simplification leads to large geometric errors quickly. The visualization performance improvement from the simplification scales almost at the same rate as the simplification rate

Survey of Template-Based Code Generation

L'automatisation de la génération des artefacts textuels à partir des modèles est une étape critique dans l'Ingénierie Dirigée par les Modèles (IDM). C'est une transformation de modèles utile pour générer le code source, sérialiser les modèles dans de stockages persistents, générer les rapports ou encore la documentation. Parmi les différents paradigmes de transformation de modèle-au-texte, la génération de code basée sur les templates (TBCG) est la plus utilisée en IDM. La TBCG est une technique de génération qui produit du code à partir des spécifications de haut niveau appelées templates. Compte tenu de la diversité des outils et des approches, il est nécessaire de classifier et de comparer les techniques de TBCG existantes afin d'apporter un soutien approprié aux développeurs. L'objectif de ce mémoire est de mieux comprendre les caractéristiques des techniques de TBCG, identifier les tendances dans la recherche, et éxaminer l'importance du rôle de l'IDM par rapport à cette approche. J'évalue également l'expressivité, la performance et la mise à l'échelle des outils associés selon une série de modèles. Je propose une étude systématique de cartographie de la littérature qui décrit une intéressante vue d'ensemble de la TBCG et une étude comparitive des outils de la TBCG pour mieux guider les dévloppeurs dans leur choix. Cette étude montre que les outils basés sur les modèles offrent plus d'expressivité tandis que les outils basés sur le code sont les plus performants. Enfin, Xtend2 offre le meilleur compromis entre l'expressivité et la performance.A critical step in model-driven engineering (MDE) is the automatic synthesis of a textual artifact from models. This is a very useful model transformation to generate application code, to serialize the model in persistent storage, generate documentation or reports. Among the various model-to-text transformation paradigms, Template-Based Code Generation (TBCG) is the most popular in MDE. TBCG is a synthesis technique that produces code from high-level specifications, called templates. It is a popular technique in MDE given that they both emphasize abstraction and automation. Given the diversity of tools and approaches, it is necessary to classify and compare existing TBCG techniques to provide appropriate support to developers. The goal of this thesis is to better understand the characteristics of TBCG techniques, identify research trends, and assess the importance of the role of MDE in this code synthesis approach. We also evaluate the expressiveness, performance and scalability of the associated tools based on a range of models that implement critical patterns. To this end, we conduct a systematic mapping study of the literature that paints an interesting overview of TBCG and a comparative study on TBCG tools to better guide developers in their choices. This study shows that model-based tools offer more expressiveness whereas code-based tools performed much faster. Xtend2 offers the best compromise between the expressiveness and the performance