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A quick search of the headlines of major newspapers reveals a treasure trove of technology procurement gone wrong. While the private sector seems to adopt and implement new technology seamlessly and quickly to deliver for customers, the government struggles to accomplish technology purchases and integrations with the same ease. As governments in the United States are looking to retain their current workforce and attract the next generation of workers, the technological capabilities and ethos of governments will be paramount. With nearly every industry being transformed by technology and Generation T being the first generation to have an ingrained “technology first” mindset, the ability of governments to attract these workers depends, in large part, on the ability to transform their government technology culture, policies, and practices.

In this chapter, the authors examine the administrative branch and observe two key components at the root of most technology failures: poor organizational structure in the bureaucracy and the lack of an empowered Chief Information/Technology Officer. Building upon case studies from Massachusetts and California, this chapter looks at the factors related to failure or success to understand the technology procurement culture. The chapter concludes by presenting four key “best practice” principles of public policy and administration that can be implemented by almost any governmental entity to improve their acquisition and implementation of technology.

The case was developed from two 2-h interviews with the Chief Operating Officer of A-Basin, Alan Henceroth; there is no CEO of A-Basin. The second interview was recorded on a Zoom call to provide accuracy of quotations and information. A variety of secondary sources were used in terms of better understanding the current state of the ski industry, as well as its history.

Arapahoe Basin (A-Basin) is a historic, moderately sized, ski area with proximity to metropolitan Denver, Colorado. For over 20 years A-Basin partnered with Vail, allowing skiers to use the Vail Epic Pass, for which A-Basin received some revenue from Vail for each skier visit. The Epic Pass allowed pass holders unlimited days of skiing at A-Basin. More and more skiers were buying the Epic Pass, thus increasing the customer traffic to A-Basin. However, the skier experience was compromised due inadequate parking, long lift lines and crowded restaurants. The renewal of the contract with Vail was coming due, and A-Basin had to consider whether to renew the contract with Vail. The case is framed primarily as a strategic marketing case. The authors use Porter’s five forces model to assess the external environment of A-Basin, and the authors use the resource-based view and the VRIO tool to assess A-Basin’s internal strengths. Both frameworks provide useful analysis in terms of deciding whether to continue A-Basin’s arrangement with Vail or end the contract and pursue a different strategy. In 2019, after consultation with the Canadian parent company Dream, A-Basin made the decision to disassociate itself from the Epic Pass and Vail to restore a quality ski experience for A-Basin’s customers. No other partner had ever left its relationship with Vail. An epilogue details some of A-Basin’s actions, as well as the outcomes for the ski area. Generally A-Basin’s decision produced positive results and solidified its competitive position among competitors. Other ski areas have since adopted a similar strategy as A-Basin. A-Basin’s success is reflected in a pending offer from Alterra, Inc., to purchase the ski area.

The A-Basin case can be used in both undergraduate and graduate strategic (or marketing) management courses. It is probably best considered during the middle of an academic term, as the case requires students to apply many of the theoretical concepts of strategy. One of the best books to enable students to use Porter’s five forces is Understanding Michael Porter by Joan Magretta (Boston: Harvard Business Review Press, 2012). Magretta was a colleague of Porter for many years and was an Editor of the Harvard Business Review. For a discussion of the VRIN/VRIO concept, see Chapter 4 of Essentials of Strategic Management by Gamble, Peteraf and Thompson (New York: McGraw-Hill Education, 2019).

Image Processing is an emerging field that is used to extract information from images. In recent years, this field has received immense attention from researchers, especially in the research domains of object detection, Biomedical Imaging and Semantic segmentation. In this study, a bibliometric analysis of publications related to image processing in the Science Expanded Index Extended (SCI-Expanded) has been performed. Several parameters have been analyzed such as annual scientific production, citations per article, most cited documents, top 20 articles, most relevant authors, authors evaluation using y-index, top and most relevant sources (journals) and hot topics.

The Bibliographic data has been extracted from the Web of Science which is well known and the world's top database of bibliographic citations of multidisciplinary areas that covers the various journals of computer science, engineering, medical and social sciences.

The research work in image processing is meager in the past decade, however, from 2014 to 2019, it increases dramatically. Recently, the IEEE Access journal is the most relevant source with an average of 115 publications per year. The USA is most productive and its publications are highly cited while China comes in second place. Image Segmentation, Feature Extraction and Medical Image Processing are hot topics in recent years. The National Natural Science Foundation of China provides 8% of all funds for Image Processing. As Image Processing is now becoming one of the most critical fields, the research productivity has enhanced during the past five years and more work is done while the era of 2005–2013 was the area with the least amount of work in this area.

This research is novel in this regard that no previous research focuses on Bibliometric Analysis in the Image Processing domain, which is one of the hot research areas in computer science and engineering.