330 Wirtschaft
Refine
Year of publication
- 2020 (5) (remove)
Document Type
- Doctoral thesis (5)
Institute
Language
- English (4)
- Multiple languages (1)
Keywords
- Audit Pricing (1)
- Auswahl (1)
- Bed Occupancy (1)
- Bed Planning (1)
- Controlling (1)
- Corporate Social Responsibility (1)
- Corporate Sustainability (1)
- Corporate purpose (1)
- Datenaufbereitung (1)
- Dienstleistungsbetrieb (1)
This study comprises three individual contributions to the research focus on pricing of auditing services. Using German and European data sets, the empirical investigation examines, among other things, how different dimensions of size, possible interactions from a so-called Big 4 premium or fee cutting behavior, as well as different financial risk factors affect the level of auditing services.
The study 'Size effects and audit pricing: Evidence from Germany' shows, among other things, that of those auditors who also provide consulting services for their clients, only Big 4 auditors achieve higher fees for auditing services. Furthermore, the results of the study suggest that the Big 4 premium shown in previous studies for the German market is strongly influenced by a market leader premium. Taking into account the high relevance of company size as a factor in the pricing of audit services, the study also concludes that company size should rather be represented by non-financial variables such as the natural logarithm of the number of employees in order to exclude any interdependencies with other financial variables.
The study entitled 'The Big 4 premium: Does it survive an auditor change? Evidence from Europe' presents new findings on the impact of auditor changes on audit fees, with a special focus on a possible Big 4 premium. This combines the previous research on the Big 4 premium and fee cutting. Matching analyses are used to compare the audit fees of companies that switch to a Big 4 auditor with those companies that switch to a non-Big 4 auditor. As a result, a Big 4 premium can only be shown for those companies that do not change auditors. In the case of a change of auditor, the results show that Big 4 auditors are willing to give up this bonus or even accept a discount on non-Big 4 auditors. This discount usually persists in the first few years. Accordingly, the assumption is that Big 4 auditors are pursuing a "foot-in-the-door" strategy to win new clients. Based on these results, the existence of a Big 4 premium is strongly dependent on the decision of the respective company to change auditors.
The study 'Variation of financial risk over time and the impact on audit pricing' focuses on the consideration of financial risk factors and their development over time. The key assumption of the study is that continuity of relevant financial ratios reduces the risk position of the auditor and should be positively reflected in the audit fees. For companies that do not change auditors, the results show an influence of increased volatility of financial ratios on the level of audit fees. This leads to the assumption that in case of increased volatility in the financial ratios, companies could deliberately not change the auditor in order to send a sign of stability to third parties and, if necessary, accept a premium on the existing auditor's fees. In the case of a change of auditor, however, new auditors could waive a corresponding premium in order to win new clients.
“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.
Article 1:
Despite the proliferation of healthier side items for children at fast food restaurants, many parents still do not make healthy choices for their children in this setting. The goal of this research is to identify the parents most likely to do so and develop an intervention to nudge these parents toward making healthier choices in retail outlets. Across four field studies conducted in a retail environment (i.e., locations of a fast food restaurant chain), the authors predict and find that parents with a high tendency to engage in social comparison and a malleable view of the self are most likely to conform to the norm in their parental social network. Given that the norm in the population studied is to order a less healthy side item (e.g., fries) versus a healthy side item (e.g., fruit), conforming results in significantly less healthy orders for the children of these individuals. The authors demonstrate that a social norm-based intervention designed to set a new healthy norm in this retail environment succeeded in increasing the overall proportion of parents that chose a healthy side item by over 29% by increasing the choice of healthy sides specifically for these individuals. The authors conclude with a discussion of implications for theory, retail managers, and policy makers.
Article 2:
Despite the popularity of Mystery Shopping (MS) to assess service performance relatively little research has been conducted on the practice. This article extends the research on Mystery Shopping by evaluating drivers and moderators of employee performance at Mystery Shopping checks. To do so, based on the results of in-depth interviews with 24 employees of a fast-food chain and existing theoretical knowledge, a model of employee performance at MS checks was developed. The developed model was then evaluated applying structural equitation modeling techniques. For this purpose, data of more than 200 employees from 9 different restaurants of the fast food restaurant chain were collected. Where perceived goal importance is identified as a direct driver of employee performance at Mystery Shopping checks results show that the effect of Job Satisfaction on MS performance is mediated by the satisfaction with MS as a service measurement tool and moderated by organizational commitment. Results further show that incentives are only effective under certain conditions to increase employee performance at Mystery Shopping checks.
Article 3:
Technologies that enable customers to produce services on their own (SSTs) have found their way into service delivery routines. The successful deployment of SSTs depends critically on employees’ attitudes toward the technologies, because their attitudes determine their willingness to use and introduce the SSTs to customers. A negative attitude toward SSTs instead can lead to employees’ dissatisfaction and generally poorer performance. Despite the importance of employees’ attitudes towards SSTs, relevant theoretical contributions are limited. This article addresses this lack of research. It introduces a holistic model of how employees’ attitudes toward SSTs form. Based on 30 in-depth interviews with frontline employees the model proposes that attitude formation towards SSTs depends on the perceived influence of SST deployment on job security, customer orientation, work design, and technological dependence. Second, by compiling practical findings from two different industries, this study identifies some moderating effects, according to the conditions for the SST deployment. Two key moderators influence the strength and even the direction of the effects of employees’ attitudes toward SSTs: employees’ job class and the level of automation implied by SSTs. Based on the conceptualized model this study offers managerial implications with regard to the successful introduction of SSTs, from an employee perspective. It also highlights some pertinent tactics, depending on the SST deployment condition, for improving employees’ attitudes toward SSTs.
In many countries today, a rising life expectancy and the associated demographic shift, coupled with the advancements of modern medicine, has fueled an ever-increasing cost pressure on healthcare systems. A driving factor for these rising costs can be seen in inpatient stays in hospitals that in many cases are connected to cost-intensive treatments. A central concern of any hospital management in such an environment is therefore to understand how to make the best possible use of available resources. A decisive factor in this regard is the management of bed capacities.
The present cumulative dissertation comprises four contributions, which address
open research questions in the field of strategic, tactical and operative bed planning:
1 Walther, M., 2020. Strategical, tactical, and operational aspects of bed
planning problems in hospital environments. Submission planned to
Social Science Research Network (SSRN)
2 Hübner, A., Kuhn, H., Walther, M., 2018. Combining clinical departments
and wards in maximum-care hospitals. OR Spectrum 40, 679-709
3 Schäfer, F., Walther, M., Hübner, A., Kuhn, H., 2019. Operational
patient-bed assignment problem in large hospital settings including overflow
and uncertainty management. Flexible Services and Manufacturing
Journal 31, 1012–1041
4 Schäfer, F., Walther, M., Hübner, A., Grimm, D., 2020. Machine learning
and pilot method: tackling uncertainty in the operational patient-bed
assignment problem. Submitted to OR Spectrum on 13 February 2020
The first contribution sets out to provide an overview over the different hierarchical planning levels on which bed planning problems may be addressed. It should be noted in this context that several different aspects may be combined under the collective term “bed planning”. These may be delimited in terms of their scope and their planning horizon. A frequently used taxonomy in this context is the hierarchical subdivision of typical problems in health care into strategical, tactical and operational levels as provided by Hulshof et al. (2012). In the context of bed planning, a typical strategical problem is how to combine departments and wards to obtain benefits from pooled ward capacity. On a tactical level, an exemplary problem setting related to bed planning can be seen in devising master surgery schedules that optimize downstream bed occupancy levels as patients returning from surgery will require a bed for post-surgical recovery and monitoring. Finally,
on an operational level, patient-bed allocations need to be optimized while taking the objectives and constraints of patients and medical staff alike into account.
To start, the second contribution deals with the strategical problem of combining departments into groups and assigning pooled ward capacity to these groups with the goal of balancing bed occupancy levels within a hospital. Specifically, one of the underlying goals is to minimize the amount of beds required to meet a predetermined service level. However, merging ward capacities with the aim of simultaneously accommodating patients from different medical departments increases the complexity of organizing and ensuring proper care for these patients. This leads to so-called pooling costs. To tackle this problem, a modeling and solution approach is developed which is based on a generalized partitioning problem and is solved by integer
linear programming (ILP). This enables hospital management to determine the cost-optimal combination of all departments and wards in a hospital, while ensuring that predetermined thresholds with regard to maximum
walking distances for doctors and patients are adhered to.
Once pooled ward capacities are established, the solution space for allocating incoming patients to beds is greatly increased and the underlying allocation problem quickly becomes too complex to be handled without computational support. In this regard the third contribution ties in with the second contribution in that it deals with optimizing the operational patient-bed allocation problem. In order to enable optimal allocation of patients to beds, it is important to identify and take into account the individual needs and
limitations of the three main stakeholders involved, namely patients, doctors, and nursing staff. All of these stakeholders exhibit different and sometimes contradicting objectives and constraints, such that a trade-off has to be made that maximizes the overall utility for the hospital. In addition, the complexity of the problem is increased by the high volatility and uncertainty regarding patient arrivals, types of illnesses, and the resulting remaining lengths of stay of newly arriving patients. In order to address this situation,
a mathematical model and solution approach for the patient-bed allocation problem is developed that is designed to generate solutions for large, real-life operative planning situations. In addition to being able to deal with overflow situations, this solution approach further takes different patient types into account, for example by anticipating emergency patient arrivals.
Finally, the fourth contribution builds on the third contribution in that the modeling and solution approach to allocate patients to beds is extended by several aspects. As mentioned above, hospitals have to deal with uncertainty regarding the actual demand for beds. Here, the fourth contribution improves the anticipation of emergency patients by using machine learning. Specifically, weather data, seasons, important local and regional events, and current and historical occupancy rates are combined to better anticipate emergency inpatient arrivals. In addition, a hyper-heuristic approach is developed based on the pilot method defined by Voß et al. (2005). By combining the improved anticipation of emergency patients with this hyperheuristic approach significant improvements can be achieved compared to the solution approach presented in the third contribution.
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.