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"Present Bias in Choices over Food and Money: Evidence from a Framed Field Experiment" - this paper presents results from a field experiment investigating dynamically inconsistent time preferences over real food choices and commitment take-up in a natural environment. We implement a longitudinal consumption experiment with a continuous convex budget: College students repeatedly choose and consume lunch menus at their college canteen. We not only allow for a full continuum of healthy or unhealthy foods; we also explicitly design the consumption stage to comply with the consume-on-receipt assumption: utility is created right after handing out the food. The paper also focuses on the implications for effective policy design: It sheds light on consumers' tendency to utilize different types of self-control devices to commit to personal consumption plans. We further compare inconsistent behavior between convex food and money choices to investigate the applicability of monetary reward studies to natural behavior. We find no correlation between time parameters in food vs. money tasks. Utility estimates from food choices suggest that dynamically consistent individuals substitute internal self-control with commitment take-up. Non-committing individuals tend to be present-biased and naive about their inconsistency.
"Dynamic Inconsistencies and Food Waste: Assessing Food Waste from a Behavioral Economics Perspective" - this paper investigates the link between dynamically inconsistent time preferences and individual food consumption and waste behavior. Food waste is conceptualized as unintended consequence of consumption choices along the food consumption chain: Because inconsistent individuals postpone the consumption of healthier food at home, storage time is prolonged and food more likely to be wasted. Capitalizing on a rich data set from a nationally representative survey, the paper constructs targeted measures of time-related food consumption and waste behaviors. In line with the theory, the paper finds that more present-biased individuals waste more food. The mechanism can be empirically supported: Although dynamically inconsistent individuals plan their food consumption, they deviate from their consumption plans more compared to consistent individuals. Deviation behavior is significantly associated with food waste. The paper points to the importance of considering dynamic inconsistencies at different stages of the food consumption process to foster the intended effects of food policies (increase in healthy nutrition) and diminish unintended consequences (increase in food waste).
"Economic Behavior under Containment: How do People Respond to Covid-19 Restrictions?" - this paper investigates the effects of policy interventions on individual decision-making and preferences by looking at a period of unprecedentedly strict regulation: the Covid-19 pandemic. Focusing on Germany, we measure regulatory strictness by calculating a policy stringency index at the federal state level. Capitalizing on exogenous variation in predetermined election cycles across states, we instrument policy stringency with the distance to the next election. Results support a positive relation between weeks to next election and state policy stringency. Leveraging a nationally representative short panel, the paper exploits this exogenous variation in policy stringency across states and over time to assess behavioral changes in different economic domains. The paper finds that government restrictions increase working from home and the provision of childcare at home, while it reduces the number of grocery shopping trips. Containment policies also affect risk preferences and fear of Covid-19: individuals become more risk-averse and afraid of Covid-19 as a consequence of stricter policies. The results are robust against various different model specifications.
This cumulative dissertation investigates the newly emerged phenomenon of customer success management (CSM). CSM has received a significant amount of attention from academics and practitioners alike. However, the body of academic literature is still in its infancy, especially when comparing CSM’s relevance to the field of relationship marketing.
Manuscript 1: Zakrzewski, K., Krause, V., Pfaff, C. & Seidenstricker, S. (2023). Is customer success management a new relationship marketing practice? A review, definition, and future research agenda based on practice theory.
Manuscript 2: Zakrzewski, K., Krause, V., Eberl, D. & Rangarajan, D. (2023). Dynamic & proactive segmentation of post-sale customer relationships in B2B subscription settings: The customer health score.
Manuscript 3: Zakrzewski, K. (2023). Customer success management - closing a capability gap in the customer-focused structure toward customer centricity in B2B service contexts.
The first manuscript adopts a new approach to the investigation of CSM, providing a practice-based definition that distinguishes CSM from other relationship marketing constructs. By utilizing the 3 Ps framework of practice theory (praxis, practices, and practitioners), the definition of CSM as a strategic function was refined. In addition, the second manuscript utilized praxis to develop the customer health score, which can be used as an indicator for the dynamic and proactive segmentation of customers. The third manuscript builds on this functional definition to show how CSM’s organizational embeddedness can help overcome barriers to customer centricity by enabling adaptive organizational capability.
This cumulative dissertation consists of five contributions considering different research questions in the field of multi- and omni-channel retailing and retail logistics.
The first contribution provides an overview of the areas of multi- and omni-channel retail logistics which have already been explored up to and including the year 2015. The focus is on empirical research published in academic journals. The findings are used to derive future areas of research which are addressed in the remainder of this dissertation.
The focus of the contributions 2 and 3 is on retailing with non-perishable goods (non-food retailing). On the one hand, the transition from a channel-specific and thus separated multi-channel logistics system towards a channel-integrated omni-channel logistics system is described and analyzed (contribution 2). On the other hand, retailers who have put time and effort into the integration of their sales channels are striving to use their integrated channels as best as possible, too. Because of the higher sales generated by customers on multiple channels, retailers steer their customers through the channels and use specific methods for order processing and logistics to their advantage (contribution 3).
Contributions 4 and 5 are especially concerned with food retailing on multiple distribution channels. On the one hand, a framework for the delivery of grocery on the last mile to the customer is presented (contribution 4) and on the other hand a supply-chain-wide perspective is taken in order to pinpoint logistics networks and network structures for different geographic regions and order volumes in omni-channel grocery retailing (contribution 5).
With this cumulative dissertation unexplored research in the field of multi- and omni-channel retail logistics is addressed. The overall contribution of this work is to analyze and solve challenges faced by retailers in terms of logistical integration and handling of multiple channels. Both the retail context (food and non-food) as well as a specific topic within this context are focused. In addition, value is added for practitioners in retail enterprises by depicting concrete recommendations in each of the individual contributions. By publishing in international academic journals the research results are made accessible to a broad readership and thus help to understand and solve logistical challenges in dealing with multiple channels in a retail company.
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.
Abstract paper 1:
This paper examines how service firms (i.e., car dealers) should manage the remedy for a recalled vehicle, specifically the timing of the repair and the message on its outcome. Drawing on the Protective Action Decision Model (PADM), we consider the repair for a recalled product as a protective action and propose a central role of customers' risk concerns for managing this process. Across multiple studies (including interviews with car dealers, archival data, and experiments with car owners), we show that firms' service processes are not well-aligned with these concerns. Regarding the timing, firms often delay the repair service until the next regular inspection for convenience, while customers prefer an immediate repair if they perceive a high risk and if risk exposure time (time until next inspection) is long. Regarding the outcome message, firms often communicate a benefit such as a value increase (a camouflage signal), which is ineffective. Instead, customers' satisfaction and loyalty with the service firm are increased by communicating a risk reduction (a need signal), mediated by perceived credibility. We recommend that service firms should let customers choose the timing for the repair and frame its outcome as a risk reduction, rather than concealing it as a benefit.
Abstract paper 2:
This paper examines the remedial effect of complaint process recovery (CPR) on customer outcomes (trust and repatronage intentions) after double deviations. CPR refers to improvements in complaint-handling processes aimed at avoiding another recovery failure. Drawing on the stereotype content model, we propose that CPR communication (i.e., informing customers about the improved complaint-handling processes after a double deviation) and CPR verification (i.e., providing evidence of these improvements when customers experience another service failure) foster repatronage intentions, serially mediated by perceived competence and trust. Further, it is proposed that CPR communication reinforces the latter mediation effect. Findings from one scenario-based experiment and two longitudinal field experiments support these hypotheses. Further, the effects of CPR communication and verification are robust across different levels of perceived failure severity and across strong- vs. weak-relationship customers. As a major managerial takeaway, firms learn how to signal their competence and remedy a double deviation, even when accepting and admitting that service failures can reoccur.
Abstract paper 3:
Purpose:
This paper examines how customer expectations of service quality change if a service provider is forced to adapt its service provision due to the social distancing rules applied during pandemic times.
Design/methodology/approach: This research draws on the concept of zone of tolerance for service quality, using field data to determine the zones of tolerance for different service quality dimensions in traditional and adapted service settings for mass events. Customers’ zones of tolerance are compared through repeated measures analyses of variance. Further, the effect of perceived service quality on customer outcomes (i.e., satisfaction with the adapted service and repatronage intention after the pandemic) is examined through a mediation analysis, with usage frequency as the mediating variable.
Findings: This paper shows that customers’ zones of tolerance narrow following service adaptations, which can be explained by rising expectations of minimum tolerable service levels. If the perceived service exceeds this lower threshold of the zone of tolerance, customers show higher satisfaction with the adapted service. Additionally, satisfaction with the adapted service has a positive effect on repatronage intention for the traditional service (i.e., after the pandemic), mediated by usage frequency of the adapted service.
Originality: The topic of service adaption is examined from a customer point of view.
Practical implications: The findings of this study provide important implications for service providers about how to handle service adaption during times of social distancing.
In forecast combination, multiple predictions are linearly combined through the assignment of weights to individual forecast models or forecasters. Various approaches exist for defining the weights, which typically involve determining the number of models to be used for combination (selection), choosing an appropriate weighting function for the forecast scenario (weighting), and using regularization techniques to adjust the calculated weights (shrinkage). The papers listed address the integration of the three approaches into holistic data analytical models.
The first paper develops a two-stage model in which weights are first calculated based on the in-sample error covariances to minimize the error on the available data by combining the individual models. Based on the selection status of a model, the weight of an individual model is then linearly shrunk either toward the mean or toward zero. The selection status is thereby derived apriori from information criteria, where Contribution 1 introduces the selection based on the model’s in-sample accuracy and performance robustness under uncertainty.
Contribution 2 modifies the two-stage model to shrink the forecasters’ weights non-proportionally to the mean or zero. Further, a new information criterion based on forward feature selection is proposed that iteratively selects the forecaster that is expected to achieve the largest increase in accuracy when combined.
Contribution 3 extends the iteration-based information criterion presented in the second contribution to consider diversity gains in addition to accuracy gains when selecting and combining forecasters.
A one-stage model for simultaneous weighting, shrinking, and selection is finally built and evaluated on simulated data in Contribution 4. Instead of requiring a prior selection criterion, the model itself learns which forecasters to shrink to the mean or to zero, while relying on a new sampling procedure to tune the model.
The essays of the cumulative dissertation are concerned with the distribution in retailing with multi-compartment vehicles.
An effective distribution planning is a central aspect for the success of retailers. Due to a highly competitive market, retailers are forced to constantly optimize processes along the supply chain to satisfy the given requirements. On the one hand, customers expect the constant availability of various products while asking for high quality. On the other, an ever increasing pressure on prices exists. The involved logistic processes therefore need to be highly efficient to fulfil the given requirements. An important part plays thereby the distribution of goods to stores, where consumers are supplied. Here, multi-compartment vehicles offer a variety of new possibilities for distribution. The core attribute of multi-compartment vehicles is the simultaneous delivery of different temperature zones with the same vehicle. Besides the new options that therefore open up for distribution, further processes and costs incurred by the use of multi-compartment vehicles need to be taken into account.
The first essay presents the basic attributes of multi-compartment vehicles as well as the differences to processes with single-compartment vehicles. The distribution with both single- and multi-compartment vehicles is illustrated and the differences are discussed. Further, the essay discusses current literature for the distribution with multi-compartment vehicles in retailing. Based on these findings, a formal model for the distribution with multi-compartment vehicles is presented in the second essay. The model considers cost and process differences, especially for loading and unloading actions. The use of multi-compartment vehicles is analysed and saving potentials revealed, using the model and a tailor-made solution approach. The remaining essays (essays 3, 4 and 5) further extend the introduced model to consider additional aspects relevant in practice. More precisely, the third essay considers the optimal fleet mix if both single- and multi-compartment vehicles are available for distribution. The fourth essay identifies loading restrictions if multi-compartment vehicles are used and presents an innovative solution approach to address the arising problem and which enables a distribution planning without loading and unloading problems. Finally, the concluding essay (essay 5) extends the discussed planning problem for multiple periods. In particular, it examines the impact of consistent deliveries for all considered product segments across the complete planning horizon.
This dissertation consists of three articles that point out new perspectives on customer experience in service recovery, and that enrich the current state of research through considerable efforts to better understand customer responses to service failure and recovery:
1. Hogreve, J., Bilstein, N., & Mandl, L. (2017). Unveiling the recovery time zone of tolerance: when time matters in service recovery. Journal of the Academy of Marketing Science, doi: 10.1007/s11747-017-0544-7, 1-18.
2. Mandl, L. (2017). Customer conflict styles in service recovery: an empirical analysis. In Buettgen, M. (Ed.), Beiträge zur Dienstleistungsforschung 2016 (pp. 97-113). Wiesbaden: Springer.
3. Mandl, L., & Hogreve, J. (2017). The buffering effect of brand community identification in service failure episodes: the role of customer citizenship behaviors. Paper currently under review at the Journal of Business Research.
The first article takes a new perspective on recovery time and compensation, which service research commonly describes and investigates as two independent service recovery strategies. In contrast, this article discovers and specifies a direct link between recovery time and customers’ compensation expectations about service failures.
The second article presents a new perspective on measuring customer responses to inadequate service recovery. Based on conflict theory, this study conceptually develops and empirically tests a model that examines a broad range of different customer responses to service recovery instead of solely measuring customers’ satisfaction with service recovery.
The third article provides a new perspective on the role of close relationships in service recovery. Whereas most research focuses on the impact of strong customer-firm relationships on customer responses to service recovery, this is the first empirical study to investigate the role of strong relationships in a service recovery and brand community context.
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.
“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.