A Knowledge-Based Approach for Business Process Risk Management

In order to support effective and efficient business process management, it is imperative that the process management lifecycle be integrated with risk management knowledge. In this regard, this article presents a knowledge-based approach to integrating risk management with business process management. The adopted approach is based on conversational case-based reasoning (CCBR) with the objective to provide support in developing an appropriate risk management strategy for an ongoing workflow instance. This approach builds on the notion of integrating risks within business process models. A prototype is currently under development, which will assess the feasibility of this approach. We then intend to validate this approach using case studies

Collaboration Process Patterns and Integrated Assessment in E-Learning Environments

Collaboration activities are particularly difficult in e-learning environments, where the intention is to provide students with valuable learning experiences through working in teams and sharing a common goal. These activities are often conducted in an ad hoc manner with lack of proper assessment and control over learning outcomes. In this article, we propose the idea of enhancing the effectiveness of collaborative e-learning practices through structured collaborative e-learning processes and integrated assessment mechanisms. The structuring of collaboration processes is suggested through the application of successful collaboration process patterns, while the integrated assessment is suggested through assessing not just the end learning outcomes, but also the process leading to those learning outcomes. These structured templates are regarded as collaborative e-learning templates (CET) that may be instantiated using common collaboration tools to generate desired collaboration patterns among elearners. Thus, the research objective involves improving the learning outcomes as well as the collaboration process dynamics through novel application of collaboration process patterns and integrated assessment techniques. This research is currently in progress and we are conducting a pilot study to test the feasibility of the proposed ideas

The Antecedents of Community Commitment in Online Communities of Practice

Online Communities of Practice offer their members the ability to communicate about a topic of interest in a way that transcends the limitations of geography. However, many communities of practice fail due to a lack of community commitment. This research examines the types of commitments that group members make to a community and what factors influence members to make a commitment to the community. A community commitment survey was distributed to online communities of practice. The results suggest that members make continuance (need-based), affective (emotion-based) and normative (obligation-based) commitments to the online communities of practice. Usefulness and system reliability lead members to make a continuance commitment. Social interaction and identification encourage members to make an affective commitment. Positive social influence and altruism influence members to make a normative commitment. The implications of this research for practitioners are discussed

Toward a framework for mitigating object detection decision making bias in driverless cars

Bias in machine learning is a significant problem that demands industry-wide attention, and in the case of driverless vehicles, life and death are at stake. The debate is whether autonomous vehicles are safe, yet more likely to strike a pedestrian of color than a person of white skin color. It is essential to obtain a greater understanding of the algorithmic bias that occurs during driving-centric object recognition. Major automakers plan to develop cars with a degree of autonomy between Level 4 and Level 5 within the next decade. At Level 5, the system is meant to behave similarly to a human driver; it can drive anyplace lawful and can make independent decisions (SAE International 2016)

Information Buried in B2B Contracts: Towards Identifying Interdependencies in IT Service Processes

A key aspect of Information Technology Service Management (ITSM) is the monitoring and evaluation of service performance – a task that is complicated by the presence of interrelationships among different service processes in a multiservice contract. While success in the service arrangement requires participant organizations’ knowledge about the nature of service dependency and their subsequent effect on performance measures; such information is often tacitly present in the service level agreement/contract documents. In this context, the aim of our research is extracting information that might be hidden in the service contracts to assist in better process management and contract (re)negotiation. We propose an information extraction driven framework for analyzing Service Level Agreements (SLA) for IT services. Our framework consists of three stages – 1) Service Entity Recognition, 2) Service Entity Context Recognition, and 3) Service Interdependency Analysis. In this article the focus is on stage 1, where we identify interrelationships by using domain ontology on a set of annotated industry-standard SLAs. Our ongoing research is aimed towards the creation and subsequent validation of process models from the information extracted from SLAs that will help both customer and service provider organizations in contract and compensation formulation, resource allocation, and SLA life cycle management

ONTOLOGY-BASED INFORMATION EXTRACTION FOR ANALYZING IT SERVICES

Service Level Agreements (SLA) for multi-service Information Technology (IT) outsourcing contracts contain vast amounts of textual information. The SLAs provide details about a specific service, Key Performance Indicators (KPI) to measure its performance; as well as process elements, such as activities, events, and resources that are integral in achieving performance goals. However, KPIs and the process elements may be interrelated. The knowledge of such interrelationships is often tacitly present in the SLAs. The aim of our research is to extract this hidden information from IT service contracts and analyze them to empower customers of IT services to make better performance management and incentive decisions. We apply an Ontology- Based Information Extraction (OBIE) approach in developing a prototype decision support framework, named SLA-Miner. The results, obtained from analyzing a set of Industry SLAs, demonstrate the utility of SLA-Miner in identifying KPI interrelationships, deficiencies, and impacts of various process elements on individual KPIs

Role of Knowledge Asset Indicators in Understanding Knowledge-Intensive Processes

Organizations are increasingly engaging in implementing knowledge management systems to create, store, retrieve, and apply knowledge in achieving organizational goals and objectives. In that regard, the ability to identify knowledge assets is essential. Knowledge-intensive business processes rely heavily on appropriate utilization of knowledge and are also a source for new knowledge creation. As such, understanding the value of knowledge assets through various indicators can prove immensely helpful in better managing knowledge-intensive processes and deriving value from knowledge management systems. This paper presents a conceptual framework, which emphasizes the link between knowledge asset indicators and management of knowledge-intensive processes within an organization. Through a pilot case study used to emphasis these connections, this paper discusses the importance of developing organizational understanding of both internal and external knowledge asset indicators in enabling knowledge-intensive processes, and to ultimately achieve organizational strategic goals

Understanding the order effect of online review sentiments and product features

Online reviews have played an increasingly important role in the popularity and success of e-commerce (Yin et al. 2014). The decreasing digital divide and ubiquitous internet access, along with the proliferation of smart mobile devices, have resulted in an exponential increase in the online purchase of goods and services. Additionally, customers are encouraged and incentivized to share their personal experiences using the product or service. Such experiences represented on internet platforms are captured through electronic word-of-mouth, typically in the form of online reviews. Prior studies on online reviews have shown that the experience of consumers plays an important role as an information source when potential consumers make purchasing decisions (Luo et al. 2013). Researchers have also revealed that opinion mining and sentiment analysis of online reviews can be used to predict the pricing power (Archak et al. 2011) and sales (Chevalier and Mayzlin 2006; Gu et al. 2012) of the product

On Understanding Preference for Agile Methods Among Software Developers

Agile methods are gaining widespread use in industry. Although management is keen on adopting agile, not all developers exhibit preference for agile methods. The literature is sparse in regard to why developers may show preference for agile. Understanding the factors informing the preference for agile can lead to more effective formation of teams, better training approaches, and optimizing software development efforts by focusing on key desirable components of agile. This study, using a grounded theory methodology, finds a variety of categories of factors that influence software developer preference for agile methods including self-efficacy, affective response, interpersonal response, external contingencies, and personality contingencies. Each of these categories contains multiple dimensions. Preference rationalization for agile methods is the core category that emerges from the data. It informs that while the very essence of agile methods overwhelmingly and positively resonates with software developers, the preference is contingent on external and personality factors as well