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Modeling ETL for Web Usage Analysis and Further Improvements of the Web Usage Analysis Process
(2006)
Currently, many organizations are trying to capitalize on the Web channel by integrating the Internet in their corporate strategies to respond to their customers’ wishes and demands more precisely. New technological options boost customer relationships and improve their chances in winning over the customer. The Web channel provides for truly duplex communications between organizations and their customers and at the same time, provides the technical means to capture these communications entirely and in great detail. Web usage analysis (WUA) holds the key to evaluating the volumes of behavioral customer data collected in the Web channel and offers immense opportunities to create direct added value for customers. By employing it to tailor products and services, organizations gain an essential potential competitive edge in light of the tough competitive situation in the Web. However, WUA cannot be deployed offhand, that is, a collection of commercial and noncommercial tools, programming libraries, and proprietary extensions is required to analyze the collected data and to deploy analytical findings. This dissertation proposes the WUSAN framework for WUA, which not only addresses the drawbacks and weaknesses of state-of-theart WUA tools, but also adopts the standards and best practices proven useful for this domain, including a data warehousing approach. One process is often underestimated in this context: Getting the volumes of data into the data warehouse. Not only must the collected data be cleansed, they must also be transformed to turn them into an applicable, purposeful data basis for Web usage analysis. This process is referred to as the extract, transform, load (ETL) process for WUA. Hence, this dissertation centers on modeling the ETL process with a powerful, yet realizable model – the logical object-oriented relational data storage model, referred to as the LOORDSM. This is introduced as a clearly structured mathematical data and transformation model conforming to the Common Warehouse Meta-Model. It provides consistent, uniform ETL modeling, which eventually supports the automation of the analytical activities. The LOORDSM significantly simplifies the overall WUA process by easing modeling ETL, a sub-process of the WUA process and an indispensable instrument for deploying electronic customer relationship management (ECRM) activities in the Web channel. Moreover, the LOORDSM fosters the creation of an automated closed loop in concrete applications such as a recommendation engine. Within the scope of this dissertation, the LOORDSM has been implemented and made operational for practical and research projects. Therefore, WUSAN, the container for the LOORDSM, and the LOORDSM itself can be regarded as enablers for future research activities in WUA, electronic commerce, and ECRM, as they significantly lower the preprocessing hurdle – a necessary step prior to any analysis activities – as yet an impediment to further research interactions.
Revenue management has proven successful in service industries. This dissertation tries to answer the question if revenue management can also be applied successfully to manufacturing companies. For this purpose, a survey was conducted which showed that there is significant potential for revenue management in the steel, aluminium and paper industries. Furthermore, a number of mathematical decision models were developed and solved by heuristic procedures which showed that revenue management can improve profits for manufacturing companies substantially.
The essays of the cumulative dissertation are concerned with the topic of retail shelf-space optimization.
Increasing product proliferation as well as decreasing space productivity force retailers to efficiently use the limited retail shelf space and assign the right shelf quantities to products they offer to customers. Optimization models support retailers with these decisions.
The first essay analyzes how demand volatility can be integrated into shelf-space optimization models. The developed optimization model furthermore accounts for space- and cross-space elasticities as well as vertical shelf positions. A specialized heuristic solves the model efficiently and returns near-optimal results. The second essay builds on the optimization model developed in the first essay and conducts extensive numerical analyses to examine the impact of cross-space elasticities on optimal shelf quantities. The essay shows that cross-space elasticities have a negligible impact on space decisions. Therefore, the complex measurement as well as the development of corresponding optimization models are of minor relevance for future research.
The third and fifth essay extend the optimization model from the first essay by integrating adjacent decision problems. The third essay accounts for the assortment decision, i.e., supports retailers in selecting the products to offer. Apart from space elasticities, the model accounts for substitution behavior in case items are unavailable and shows that both demand effects reinforce each other, which is why they should be accounted for simultaneously. The fifth essay shows how retails can furthermore guarantee the efficient replenishment of shelves and examines the impact of the availability of a backroom on optimal shelf quantities.
The fourth essay improves the algorithm developed for the optimization model from the third essay. The improved heuristic yields higher retail profits in shorter runtime.
The cumulative dissertation "Optimization models for shelf space allocation in retail stores" consists of the four individual scientific contributions listed below:
1. Düsterhöft, Tobias, Hübner, Alexander and Schaal, Kai (2020): A practical approach to the shelf-space allocation and replenishment problem with heterogeneously sized shelves.
2. Hübner, Alexander, Düsterhöft, Tobias and Ostermeier, Manuel (2020): An optimization approach for product allocation with integrated shelf space dimensioning in retail stores.
3. Ostermeier, Manuel, Düsterhöft, Tobias and Hübner, Alexander (2020): A model for the store wide shelf space allocation.
4 Düsterhöft, Tobias (2020): Retail shelf space planning - Differences, problems and opportunities of applied optimization models.
The planning and best possible utilization of the available shelf space is of central importance for retailers. Shelf space is a scarce resource in stores today. Shelf planners need to determine optimal shelf spaces for each item within product allocation. Researchers have already developed several decision support models. Usually, these models have in common that they can make a decision on the number of facings per product. A facing is a visible sales unit of a product on the shelf. Behind a facing, depending on the depth of the shelf further sales units are located. With an increase in the number of facings the visibility of the product for customers is also increasing, which is associated with a certain demand effect, the so-called space elasticity.
The content of this dissertation are optimization models that extend existing approaches to product allocation significantly and thus also enable a practical application of these approaches. Within the framework of a practical project substantial new contents for the product allocation can be determined. The resulting optimization models build on each other. Initially, in the 1st article the product allocation is extended by an exact consideration of the shelf space dimensions. The resulting question of the optimal shelf layout is the central part of the 2nd article. If the layouts of shelves are determined on the shelf, the total shelf space per category must be known beforehand. This question is dealt with in the 3rd article. Finally, new research fields are identified in the 4th article based on current real processes and requirements.
Recommending products that are helpful to customers and tailored to their needs is of pivotal importance for successful online retailing. Online purchase data is typically used to generate such recommendations. This dissertation studies two topic models that use purchase data to make product recommendations. The Author Topic Model (ATM) and Sticky Author Topic Model (Sticky ATM) are applied to the purchase data of an online retailer of animal health products, and their predictive performances are contrasted with those of the benchmark methods Unigram, Bigram, and Collaborative Filtering (CF). This work focuses on the generation of new product recommendations. To increase novelty in recommendations, a new pre-processing approach is presented. The data is prepared prior to model application such that more novel products are included in the recommendations. A total of six data preparation variants are tested. The key finding is that topic models are very competitive with the benchmark methods and outperform them with the data preparation variant, where repetitively purchased items (repeat items) and customers with one item transaction (single-item customers) are eliminated from the data. Marketing practitioners should consider this pre-processing when implementing topic models as recommender models in their online shops.
Automation technologies and human capital are fundamental drivers of economic growth. The motivation of this thesis is to advance the literature by providing novel insights into the determinants of automation innovation and human capital. For this purpose, this thesis exploits immigration episodes, uses empirical methods for causal inference and introduces new sources of data on immigrants and automation innovation. Chapter 2 and Chapter 3 each examine the relationship between labor endowments and automation innovation. Chapter 4 examines the effects of ethnic concentration on immigrant childrens' acquisition of human capital.
“While each of our individual companies serves its own corporate purpose, we share a fundamental commitment to all of our stakeholders” (Business Roundtable, 2019). With this statement, 181 managers of the Business Roundtable renewed their fundamental view about the purpose of business. This symbolic statement generated a vivid discussion and proved the importance and actuality of addressing organisational purpose as a research topic.
From an academic perspective the discussion about organizational purpose is not new but diverse (Bartlett & Ghoshal, 1994; Basu, 1999; Canals, 2010; George, 1999; Henderson & Steen, 2015; Hollensbe, Wookey, Hickey, George, & Nichols, 2014; Koslowski, 2001; Loza Adaui & Mion, 2016; Porter & Kramer, 2011; Quinn & Thakor, 2018; Sisodia, Wolfe, & Sheth, 2006). There are many overlapping points between the discussion on the organisational purpose and the study of corporate sustainability. Because from a sustainability management perspective, companies are administrated and valued, taking into consideration not only their economic performance but also the ecological and social impact that they generate (Elkington, 1994).
This cumulative dissertation addresses in a broad perspective the overlapping points of the discussion on organizational purpose and sustainability and entails four modules:
Module I: Matthias S. Fifka, Anna-Lena Kühn, Cristian R. Loza Adaui & Markus Stiglbauer (2016) Promoting Development in Weak Institutional Environments: The Understanding and Transmission of Sustainability by NGOs in Latin America. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27(3), 1091-1122.
Module II: Giorgio Mion & Cristian R. Loza Adaui (2020) Understanding the Purpose of Benefit Corporations: An Empirical Study on the Italian Case. International Journal of Corporate Social Responsibility, 5(4), 1-15.
Module III: Giorgio Mion & Cristian R. Loza Adaui (2019) Mandatory Nonfinancial Disclosure and Its Consequences on the Sustainability Reporting Quality of Italian and German Companies. Sustainability, 11(17), 4612.
Module IV: Cristian R. Loza Adaui (2020) Sustainability Reporting Quality of Peruvian Listed Companies and the Impact of Regulatory Requirements of Sustainability Disclosures. Sustainability, 2020, 12(3), 1135.
Die kumulative Dissertation „The Effectiveness of Segment Disclosures under the Management Approach: Empirical Evidence from Europe” untersucht die Determinanten und ökonomischen Auswirkungen von Segmentberichtsangaben, welche nach International Financial Reporting Standard (IFRS) 8 aufgestellt wurden. Die Dissertation erweitert die bestehende Literatur zur Wirksamkeit des IFRS 8 in Europa und besteht dabei aus drei Beiträgen.
Der erste Beitrag untersucht den Einfluss von Kultur als Determinante für die Quantität und Qualität der Segmentberichtsangaben europäischer Unternehmen. Im Rahmen der empirischen Analyse werden darüber hinaus die ökonomischen Auswirkungen der kulturinduzierten Veröffentlichungsmuster nachgewiesen.
Der zweite Beitrag beantwortet die Forschungsfrage welche Auswirkungen Non-IFRS Segmentdaten auf die Prognosegenauigkeit von Finanzanalysten haben. In der empirischen Analyse wird dieser Wirkungszusammenhang zudem für spezielle Abweichungen von IFRS Rechnungslegungsgrundsätzen untersucht.
Der dritte Beitrag stellt Änderungsvorschläge an IFRS 8 seitens des internationalen Standardsetters vor und untersucht deren potenzielle Auswirkungen aus Perspektive der Jahresabschlussersteller.
The cumulative dissertation consists of three articles.
The first article analyses the use of vertical manager interlocks for tax avoidance purposes.
The second article studies the effect of tax department organization on tax avoidance and tax risk.
The third article contains an analysis of the effects of tax evasion penalties levied on corporations or managers on aggressive tax avoidance.
Topic models such as latent Dirichlet allocation (LDA) aim to identify latent topics within text corpora. However, although LDA-type models fall into the category of Natural Language Processing, the actual model input is heavily modified from the original natural language. Among other things, this is typically done by removing specific terms, which arguably might also remove information. In this paper, an extension to LDA is proposed called uLDA, which seeks to incorporate some of these formerly eliminated terms -- namely stop words -- to match natural topics more closely. After developing and evaluating the new extension on established fit measures, uLDA is then tasked with approximating human-perceived topics. For this, a ground truth for topic labels is generated using a human-based experiment. These values are then used as a reference to be matched by the model output. Results show that the new extension outperforms traditional topic models regarding out-of-sample fit across all data sets and regarding human topic approximation for most data sets. These findings demonstrate that the novel extension can extract valuable information from the additional data conveyed by stop words and shows potential for better modeling natural language in the future.