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An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms
This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea
On Evidence-based Risk Management in Requirements Engineering
Background: The sensitivity of Requirements Engineering (RE) to the context
makes it difficult to efficiently control problems therein, thus, hampering an
effective risk management devoted to allow for early corrective or even
preventive measures. Problem: There is still little empirical knowledge about
context-specific RE phenomena which would be necessary for an effective
context- sensitive risk management in RE. Goal: We propose and validate an
evidence-based approach to assess risks in RE using cross-company data about
problems, causes and effects. Research Method: We use survey data from 228
companies and build a probabilistic network that supports the forecast of
context-specific RE phenomena. We implement this approach using spreadsheets to
support a light-weight risk assessment. Results: Our results from an initial
validation in 6 companies strengthen our confidence that the approach increases
the awareness for individual risk factors in RE, and the feedback further
allows for disseminating our approach into practice.Comment: 20 pages, submitted to 10th Software Quality Days conference, 201
Harnessing Collaborative Technologies: Helping Funders Work Together Better
This report was produced through a joint research project of the Monitor Institute and the Foundation Center. The research included an extensive literature review on collaboration in philanthropy, detailed analysis of trends from a recent Foundation Center survey of the largest U.S. foundations, interviews with 37 leading philanthropy professionals and technology experts, and a review of over 170 online tools.The report is a story about how new tools are changing the way funders collaborate. It includes three primary sections: an introduction to emerging technologies and the changing context for philanthropic collaboration; an overview of collaborative needs and tools; and recommendations for improving the collaborative technology landscapeA "Key Findings" executive summary serves as a companion piece to this full report
Naming the Pain in Requirements Engineering: A Design for a Global Family of Surveys and First Results from Germany
For many years, we have observed industry struggling in defining a high
quality requirements engineering (RE) and researchers trying to understand
industrial expectations and problems. Although we are investigating the
discipline with a plethora of empirical studies, they still do not allow for
empirical generalisations. To lay an empirical and externally valid foundation
about the state of the practice in RE, we aim at a series of open and
reproducible surveys that allow us to steer future research in a problem-driven
manner. We designed a globally distributed family of surveys in joint
collaborations with different researchers and completed the first run in
Germany. The instrument is based on a theory in the form of a set of hypotheses
inferred from our experiences and available studies. We test each hypothesis in
our theory and identify further candidates to extend the theory by correlation
and Grounded Theory analysis. In this article, we report on the design of the
family of surveys, its underlying theory, and the full results obtained from
Germany with participants from 58 companies. The results reveal, for example, a
tendency to improve RE via internally defined qualitative methods rather than
relying on normative approaches like CMMI. We also discovered various RE
problems that are statistically significant in practice. For instance, we could
corroborate communication flaws or moving targets as problems in practice. Our
results are not yet fully representative but already give first insights into
current practices and problems in RE, and they allow us to draw lessons learnt
for future replications. Our results obtained from this first run in Germany
make us confident that the survey design and instrument are well-suited to be
replicated and, thereby, to create a generalisable empirical basis of RE in
practice
Preliminary Results in a Multi-site Empirical Study on Cross-organizational ERP Size and Effort Estimation
This paper reports on initial findings in an empirical study carried out with representatives of two ERP vendors, six ERP adopting organizations, four ERP implementation consulting companies, and two ERP research and advisory services firms. Our study’s goal was to gain understanding of the state-of-the practice in size and effort estimation of cross-organizational ERP projects. Based on key size and effort estimation challenges identified in a previously published literature survey, we explored some difficulties, fallacies and pitfalls these organizations face. We focused on collecting empirical evidence from the participating ERP market players to assess specific facts about the state-of-the-art ERP size and effort estimation practices. Our study adopted a qualitative research method based on an asynchronous online focus group
How can SMEs benefit from big data? Challenges and a path forward
Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities.
The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft
Specification of vertical semantic consistency rules of UML class diagram refinement using logical approach
Unified Modelling Language (UML) is the most popular modelling language use for
software design in software development industries with a class diagram being the
most frequently use diagram. Despite the popularity of UML, it is being affected by
inconsistency problems of its diagrams at the same or different abstraction levels.
Inconsistency in UML is mostly caused by existence of various views on the same
system and sometimes leads to potentially conflicting system specifications. In
general, syntactic consistency can be automatically checked and therefore is
supported by current UML Computer-aided Software Engineering (CASE) tools.
Semantic consistency problems, unlike syntactic consistency problems, there exists
no specific method for specifying semantic consistency rules and constraints.
Therefore, this research has specified twenty-four abstraction rules of class‟s relation
semantic among any three related classes of a refined class diagram to semantically
equivalent relations of two of the classes using a logical approach. This research has
also formalized three vertical semantic consistency rules of a class diagram
refinement identified by previous researchers using a logical approach and a set of
formalized abstraction rules. The results were successfully evaluated using hotel
management system and passenger list system case studies and were found to be
reliable and efficient
Software Engineers' Information Seeking Behavior in Change Impact Analysis - An Interview Study
Software engineers working in large projects must navigate complex
information landscapes. Change Impact Analysis (CIA) is a task that relies on
engineers' successful information seeking in databases storing, e.g., source
code, requirements, design descriptions, and test case specifications. Several
previous approaches to support information seeking are task-specific, thus
understanding engineers' seeking behavior in specific tasks is fundamental. We
present an industrial case study on how engineers seek information in CIA, with
a particular focus on traceability and development artifacts that are not
source code. We show that engineers have different information seeking
behavior, and that some do not consider traceability particularly useful when
conducting CIA. Furthermore, we observe a tendency for engineers to prefer less
rigid types of support rather than formal approaches, i.e., engineers value
support that allows flexibility in how to practically conduct CIA. Finally, due
to diverse information seeking behavior, we argue that future CIA support
should embrace individual preferences to identify change impact by empowering
several seeking alternatives, including searching, browsing, and tracing.Comment: Accepted for publication in the proceedings of the 25th International
Conference on Program Comprehensio
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