Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline

The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m

Decision support systems for sustainable development : an overview

In IDL-2492

National information infrastructure in Pacific Asia

Decision Support Systems213215-227DSSY

ARBAS: A formal language to support argumentation in network-based organizations

This paper proposes a formal language to support and document argumentation, claims, decision and negotiation, and coordination in network-based organizations. The purpose of the language, once implemented in the organization's Intranet, is to promote communication using structured arguments, claims and justified decisions and to preserve this information as corporate memory. We contend that organization effectiveness can be achieved by continuous exchange of information, arguments, and joint agreements on actions with appropriate knowledge of the organizational decision-making context. As a mechanism for group decision and negotiation support and as an organizational repository, the proposed language is constructed based on supporting discussions that focus on the relationship between actions and the resources required to implement these actions

The Perils of Using Social Media Data to Predict the Spread of Diseases

The data produced by social media engagement is of interest to various organizations and has been used in different applications like marketing, finance and healthcare. Though the potential of mining this data is high, standard data mining processes do not address the peculiarities of social media data. In this paper, we explore the perils of using social media data in predicting the spread of an infectious disease; perils that are mostly related to data quality, textual analysis and location information. We synthesize findings from a literature review and a data mining exercise to develop an adapted data mining process. This process has been designed to minimize the effects of the perils identified and is thus more aligned with the requirements of predicting disease spread using social media data. The process should be useful to data miners and health institutions

Understanding Leadership Challenges and Responses in Data-driven Transformations

The purpose of this study is to better understand challenges and typical responses of leadership in data-driven transformations. Increasingly organizations aspire to practices of data-driven decision making. In this context the transformational aspects transcend traditional change management processes and pose new and different challenges to leadership. To explore these challenges and typical responses we performed four in-depth case studies of organizations that are more mature in terms of their transformation towards a data-driven organization. Propositions derived from change management and digital transformation literature guide our exploration. Our findings help understand the key role of leadership in a data-driven transformation, particularly through (1) continuously communicating and explaining the value of being data-driven, (2) securing and managing critical resources, including data and analysts, and (3) creating a data-driven culture. Our study contributes to literature by combining insights from change management and data-driven transformations to better understand the dynamics of leadership in this context

The Champion of Images: Understanding the Role of Images in the Decision-making Process of Online Hotel Bookings

Images are vitally important in interesting consumers and helping them to make decisions. Images of a hotel are particularly important and were used to sell hotels even before the Internet, when travel agencies would often have brochures about hotel properties that they used to entice travelers. On many online travel agency (OTA) websites, the hotel's image can take up 33% of the space on the hotel property page, but the importance of this image in the decision-making process has yet to be studied. For many OTAs, there are currently no quantitative analytic methods that help determine which image to display in this critical location. In this research, we use deep learning to extract information directly from hotel images and we apply image analytics to understand the importance of this information in the online hotel booking process. To provide managerial insights, we will combine a prediction model, with the t-distributed Stochastic Neighbor Embedding (t-SNE) to classify and understand the types of images hotels generally use as their thumbnail or "champion" image and what aspects of these images elicit consumers to consider and book a hotel

Success Lies in the Eye of the Beholder: A Quantitative Analysis of the Mismatch Between Perceived and Real IT Project Management Performance

Building on an earlier exploratory study, this paper investigates the drivers of the possible mismatch between traditional "real" IT project management performance criteria - quality, time and cost - and "perceived" project management performance. We use partial least squares structural equation modeling to test five main hypotheses with survey data from 248 managers with extensive IT/IS project involvement. The results demonstrate that mismatches between real and perceived project management performance indeed occur. They are predominantly driven by poor expectation management before and during the execution of IT projects, as well as by a low project sponsor commitment. A discussion of the findings and limitations, as well as suggestions for future research, conclude the article

Rerouting Digital Transformations - Six Cases in the Airline Industry

The purpose of this study is to understand how and why success criteria evolve in the course of a digital transformation initiative. Evolving success criteria can cloud planning processes and lead to post-hoc rationalizations, an observation that is often made but the underlying processes are hardly researched. This exploratory study does so by employing a qualitative approach with six embedded case studies of different digital transformation initiatives (DTIs) within a large European airline company. Our findings show how traditional business case approval practices, the degree of involvement of different stakeholders -each using different metrics-, the closeness in collaboration between these stakeholders and lastly the degree to which key-users embrace the digital solution during a DTI, all contribute to evolving success criteria. A discussion of the findings and limitations, implications for practice and suggestions for future research conclude the article