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Leveraging Wikipedia
(2018)
Licensing digital content
(2018)
This dissertation assesses the characteristics and viability of the emerging longhaul Low Cost Carriers (LCCs). In particular, the aim is to understand their business model, evaluate the cost and revenue performance, and investigate its impact on other carriers. Existing academic literature is inconclusive about characteristics and viability of the business model. To validate its defining characteristics, 37 airlines flying on North Atlantic routes are clustered using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) along a newly constructed long-haul airline business model framework. To contribute to the evaluation of business model viability, cost differences between clusters are uncovered followed by a discussion of their sustainability. Key findings include the characterization of the emerging long-haul LCC business model and its significant differences from Full-Service Network Carrier (FSNC) and leisure carrier models. On a cluster average, 33% lower unit costs compared to FSNCs are identified, of which 24 percentage points are evaluated as sustainable. As these cost advantages over FSNCs are smaller compared to the original savings of short-/medium-haul LCCs, revenue competitiveness on the longhaul becomes more critical. To assess long-haul revenue performance, a new metric for benchmarking the revenue per equivalent flight capacity is defined. Subsequently, a revenue model combining traffic, fare, load factor, and seat data from the North Atlantic is developed to determine the revenue per flight capacity across a sample of city-pairs. The results show that LCCs earn revenue per flight capacity comparable to FSNCs on shorter long-haul routes. Key factors to compensate lower direct yields are fewer low-yield connecting passengers, sales of ancillary services, higher load factors, and significantly more passengers per aircraft. Long-haul LCC market impact, and in particular their impact on incumbents’ fare levels, has not yet been assessed and short-/medium-haul LCC-related findings cannot be readily applied. To evaluate the impact of long-haul LCC presence on the incumbents’ pricing, a stylized analytical model is proposed for hypotheses development. Subsequently, Two-Steps Least Squares (2SLS) regressions with Instrumental Variables (IVs) are performed, distinguishing between economy, premium economy, and business classes, based on a sample of North Atlantic routes. Confirming the first hypothesis, LCC presence reduces incumbents’ long-haul economy and premium economy fares by -10% and -13%, respectively, ceteris paribus. LCC presence, however, does not significantly impact long-haul business class fare levels, confirming the second hypothesis. These findings are particularly relevant as the North Atlantic market represents to date one of the remaining profit pools for North American and European legacy carriers. The findings of this research implicate that the long-haul LCC model is economically viable, at least on trunk routes with high demand. FSNC management should be aware of the rising competition and fare impact on North Atlantic routes. Potential reactions could include the de-bundling of entry fares with the option of ancillary sales even on long-haul routes, a re-evaluation of the revenue impact of low-yield connecting passengers, a focus on premium passengers, and a continuous reduction of operating costs.
This dissertation investigates the application of machine learning to improve decision making in airline operations. In an introductory overview, we1 discuss the airline industry and the challenges of decision making in airline operations: e. g., complex IT infrastructure, interconnected resources, delay management, fuel price volatility and future environmental regulation. Machine learning can efficiently integrate a large volume of data from a variety of data sources and formats to generate accurate predictions. To assess the viability of using machine learning models for decision making in airline operations, we develop a model based on linear regression and gradient boosting to predict aircraft arrival time. Furthermore, we integrate cost index optimization to model the impact of aircraft speed on arrival time. While we find that machine learning can improve prediction accuracy by more than 30 %, the optimal cost index varies according to fuel cost, flight distance and delay costs. We propose an overall reduction in cost index for short-haul flights to reduce fuel cost while maintaining punctual operations. Arrival time predictions serve as input for daily operations planning and control. For network carriers accurate arrival time predictions are key for efficient hub operations. In a next step, we focus on aircraft arrival time prediction for intercontinental flights. We analyze the accuracy of en-route weather data provided by the flight plan, generate features based on en-route weather data and integrate them in our prediction model. We evaluate three machine learning models: linear regression, random forest and gradient boosting. Through our approach, we can assess the impact of en-route weather data. Overall, an increase in prediction accuracy of 25 % is achieved. By including en-route weather data, prediction accuracy is improved by 5 %. Our model outlines the essential features for intercontinental arrival time predictions and assess the value of en-route weather data. Furthermore, we outline organizational challenges in implementing predictive analytics. Future environmental regulations are a challenge for the airline industry. From 2020 onwards net growth in CO2 emissions is prohibited. Thus, airlines need to focus on initiatives to limit fuel consumption. We develop a prediction model for fuel consumption considering ten different aircraft types. Our results show that fuel consumption can be improved by more than 30 % for short and long-haul flights resulting in an annual reduction of 10.5 million e in fuel costs and a reduction on green house gas emissions of 66.000 tons. Furthermore, we assess the pilots’ willingness to integrate our prediction model in their fuel decision. Our analysis shows that high prediction accuracy, model understanding and a long testing phase are essential for acceptance of the prediction model. The main implication is that decision making in airline operations can be substantially improved through machine learning. Therefore, more prediction.
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Achenbach and Spinler (2018a,b), Achenbach et al. (2017).
Open versus closed organizational design options of sharing economy models and sharing communities
(2018)
With the recent emergence of the sharing economy, novel and complex organizational forms are founded on individuals and sharing communities that micro-manage and self-organize their own, private resources for social or commercial outcomes within an organizational scope.
These novel and complex forms reflect sharing economy models which are designed along a range from decentralization to centralization. In decentralized designs, individuals control and organize their own, private resources in sharing communities. In centralized designs, the organization controls and organizes the resources which are shared within communities.
This dissertation addresses the complexity of sharing economy models by exploring them on the organizational, the individual and the system level.
On the organizational level, this dissertation specifies the various configurations of sharing economy models. These configurations are based on representative design options which are traded off between decentralization and centralization. The Sharing Economy Spectrum is conceptualized as framework which integrates these characteristics-based design options. On the individual level, this dissertation focuses on how individuals’ resource endowment and trust in organization builders, in community members and in institutions individually influence individuals’ expectations regarding the beneficiaries of a sharing economy model. A regression model and a couple of hypotheses are developed and explored through a survey conducted at the Philippine social organization Gawad Kalinga.
On the system level, this dissertation acknowledges the sharing economy as a socioeconomic ecosystem and addresses the leadership paradox that resources are decentralized but require central leadership. Complexity leadership theory captures sharing economy models as complex adaptive systems whose leadership is organized for a common purpose. The specific roles of dynamic interaction and trust for complexity leadership are explored jointly with social entrepreneurship and sharing economy research. An ethnography with Gawad Kalinga reveals a novel complexity leadership model that fosters bottom-up leadership.
The Veblen effect revisited
(2018)
In the transformation driven by technology and stream-video challenges, media giants have taken acquisition strategies to uphold their current status and seek for vertical business expansion via entering content creation area. AT&T, the telecom and media giant, initiated an acquisition of Time Warner in October 2016 at a bid of USD 85bn, which was considered as the biggest M&A deal that year worldwide. But the process was blocked mainly by the US Department of Justice (DOJ) and stagnant for nearly 2 years. In June 2018, the vertical merger was approved by the U.S. District Judge and completed on 14.06.2018. Concerns about legitimate regulation should be largely taken into account of the evaluation of success of acquisitions.
Deal Logic Linde / Praxair
(2018)
Currently in news for the last moment approval from the Federal Trade Commission to make the Praxair and Linde merger a reality, the development of the deal has not been short of hurdles. Initially, Praxair-Linde faced resistance from the European Union under the accusation that the merger would hinder competition. Later, both companies had to undertake multiple selling transactions to meet the anti-trust requirements of the countries in which they operated. Praxair and Linde were finally able to meet the requirements in early November for a successful completion of the merger.
Linde and Praxair, the world’s no. 2 and no. 3 industrial gas suppliers respectively, were in the spotlight due to the size of the merger and the impact that it would have on the gas industry, almost making them a monopoly firm.