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Prof. Dr. Michael Scholz

Zentrum für angewandte Forschung

Technologie Campus Grafenau

Professor

Grafenau

08552/975620-19

Publikationen

Sortierung:

Beitrag in Sammelwerk/Tagungsband

S. Wildner
Michael Scholz

Managing Knowledge Methodically.

In:
P. Kleinschmidt
F. Lehner
H. Nösekabel

(2006)

Monographie

F. Lehner
Michael Scholz
S. Wildner

Wirtschaftsinformatik. Eine Einführung.

München: Hanser, Carl

(2008)

Beitrag in Sammelwerk/Tagungsband

Michael Scholz

From Consumer Preferences Towards Buying Decisions - Conjoint Analysis as Preference Measuring Method in Product Recommender Systems.

(2008)

Monographie

Michael Scholz

Die Conjoint-Analyse als Instrument zur Nutzenmessung in Produktempfehlungssystemen.

Berlin: Logos-Verlag

(2009)

Beitrag in Sammelwerk/Tagungsband

M. Giamattei
Michael Scholz

Exploiting Correspondence Analysis to Visualize Product Spaces.

In:
P. Spagnoletti
M. Ferrara
J. George
A. DAtri

(2010)

Beitrag in Sammelwerk/Tagungsband

Michael Scholz

Identifying Recommendable Products based on Signal Detection Theory.

In:
J. Telhada
F. Adam
A. Respício
C. Teixeira
G. Phillips-Wren

Amsterdam; Washington, D.C.: IOS Press

(2010)

Beitrag in Sammelwerk/Tagungsband

Michael Scholz

Implications of Consumer Information Behaviour to Construct Utility-based Recommender Systems - A Prototypical Study.

(2010)

Beitrag in Sammelwerk/Tagungsband

Michael Scholz
N. Haas

Determinants of Reverse Auction Results - An Empirical Examination of Freelancer.com.

In:
V. Tuunainen
M. Rossi
J. Nandhakumar

(2011)

Beitrag in Sammelwerk/Tagungsband

Michael Scholz
V. Dorner

Estimating Optimal Recommendation Set Sizes for Individual Consumers.

In:
G. Piccoli
M.-H. Huang
V. Sambamurthy

(2012)

Zeitschriftenartikel

B. Türk
Michael Scholz
P. Berresheim

Measuring Service Quality in Online Luxury Goods Retailing.

In: Journal of Electronic Commerce Research (vol. 13) , pg. 88-103

(2012)

Abstract anzeigen

Service quality has been identified as a crucial factor to successfully and sustainably manage online shops. In this paper, we introduce anadaptation oftheE-S-Qualinstrumentto measure service quality in online luxury goods retailing. Based on a literature review,we identify efficiency, design, fulfillment, information, contact and responsiveness as factorsof service quality in online luxury goods retailing. We found empirical evidence that these factors should be considered as dimensions rather than antecedents. A survey conducted in cooperation with the HUGO BOSS AG indicates that our proposed instrument is valid and reliable. Implications for research and practice as well as limitations of our study are discussed.

Beitrag in Sammelwerk/Tagungsband

V. Dorner
O. Ivanova
Michael Scholz

Think Twice Before You Buy! How Recommendations Affect Three-Stage Purchase Decision Processes.

In:
R. Baskerville
M. Chau

(2013)

Beitrag in Sammelwerk/Tagungsband

O. Ivanova
Michael Scholz
V. Dorner

Does Amazon Scare Off Customers? The Effect of Negative Spotlight Reviews on Purchase Intention..

In:
R. Alt
B. Franczyk

Leipzig

(2013)

Zeitschriftenartikel

J. Pfeiffer
Michael Scholz

A Low-Effort Recommendation System with High Accuracy.

In: Business & Information Systems Engineering (vol. 5) , pg. 397-408

(2013)

DOI: 10.1007/s12599-013-0295-z

Abstract anzeigen

In recent studies on recommendation systems, the choice-based conjoint analysis has been suggested as a method for measuring consumer preferences. This approach achieves high recommendation accuracy and does not suffer from the start-up problem because it is also applicable for recommendations for new consumers or of new products. However, this method requires massive consumer input, which causes consumer reluctance. In a simulation study, we demonstrate the high accuracy, but also the high user’s effort for using a utility-based recommendation system using a choice-based conjoint analysis with hierarchical Bayes estimation. In order to reduce the conflict between consumer effort and recommendation accuracy, we develop a novel approach that only shows Pareto-efficient alternatives and ranks them according to the number of dominated attributes. We demonstrate that, in terms of the decision accuracy of the recommended products, the ranked Pareto-front approach performs better than a recommendation system that employs choice-based conjoint analysis. Furthermore, the consumer’s effort is kept low and comparable to that of simple systems that require little consumer input. In a simulation study, we demonstrate that recommendation systems using a choice-based conjoint analysis with hierarchical Bayes estimation require up to three times higher mental effort for the consumer than simple sorting mechanisms. However, consumers benefit from a choice-based conjoint analysis in terms of a significantly higher utility of the selected product. We further introduce the concept of a ranked Pareto-front which allows consumers to select a product with a better utility than they will select when using a choice-based conjoint analysis for the same low costs that using a simple sorting mechanism require.

Zeitschriftenartikel

Michael Scholz
V. Dorner

The Recipe for the Perfect Review?.

In: Business & Information Systems Engineering (vol. 5) , pg. 141-151

(2013)

DOI: 10.1007/s12599-013-0259-3

Abstract anzeigen

Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools. We analyze determinants of review helpfulness in online retailing based on Wang and Strong’s (1996) data quality framework. Helpful reviews consist of 9 % of adjectives, display high product feature entropy, and present opinions that differ from previous reviews for the product in question. Other helpfulness determinants depend on whether experiential or utilitarian products are reviewed. Our research points e-shop providers towards two major improvements in their review systems.

Beitrag in Sammelwerk/Tagungsband

V. Dorner
Michael Scholz

Predicting and Economically Exploiting Utility Thresholds with Utility-based Recommendation Systems.

Utrecht

(2013)

Beitrag in Sammelwerk/Tagungsband

Michael Scholz
V. Dorner
A. Landherr
F. Probst

Awareness, Interest, and Purchase: the Effects of User- and Marketer-Generated Content on Purchase Decision Processes.

In:
R. Baskerville
M. Chau

(2013)

Beitrag in Sammelwerk/Tagungsband

Michael Scholz
F. Lehner
V. Dorner

A Respecification of the DeLone and McLean Model to Measure the Success of an Electronic Mediated Learning System..

In:
L. Beckmann
L. Suhl
D. Kundisch

(2014)

Beitrag in Sammelwerk/Tagungsband

M. Franz
Michael Scholz
O. Hinz

2D versus 3D Visualizations in Decision Support - The Impact of Decision Makers' Perceptions.

In:
C. Urquhart
A. Heinzl
T. Carte

(2015)

Zeitschriftenartikel

Michael Scholz
V. Dorner
M. Franz
O. Hinz

Measuring consumers' willingness to pay with utility-based recommendation systems.

In: Decision Support Systems (vol. 72)

(2015)

DOI: 10.1016/j.dss.2015.02.006

Abstract anzeigen

Our paper addresses two gaps in research on recommendation systems: first, leveraging them to predict consumers' willingness to pay; second, estimating non-linear utility functions – which are generally held to provide better approximations of consumers' preference structures than linear functions – at a reasonable level of cognitive consumer effort. We develop an approach to simultaneously estimate exponential utility functions and willingness to pay at a low level of cognitive consumer effort. The empirical evaluation of our new recommendation system's utility and willingness to pay estimates with the estimates of a system based on linear utility functions indicates that exponential utility functions are better suited for predicting optimal recommendation ranks for products. Linear utility functions perform better in estimating consumers' willingness to pay. Based on our experimental data set, we show how retailers can use these willingness to pay estimates for profit-maximizing pricing decisions.

Zeitschriftenartikel

Michael Scholz
M. Franz
O. Hinz

The Ambiguous Identifier Clustering Technique.

In: Electron Markets (vol. 26) , pg. 143-156

(2016)

DOI: 10.1007/s12525-016-0217-2

Abstract anzeigen

Investigations of online transaction data often face the problem that entries for identical products cannot be identified as such. There is, for example, typically no unique product identifier in online auctions; retailers make their offers at price comparison sites hardly comparable and online stores often use different identifiers for virtually equal products. Existing studies typically use data sets that are restricted to one or only a few products in order to avoid product heterogeneity if a unique product identifier is not available. We propose the Ambiguous Identifier Clustering Technique (AICT) that identifies online transaction data that refer to virtually the same product. Based on a data set of eBay auctions, we demonstrate that AICT clusters online transactions for identical products with high accuracy. We further show how researchers benefit from AICT and the reduced product heterogeneity when analyzing data with econometric models.

Zeitschriftenartikel

Michael Scholz

A Note on the Power of Multiattribute One-switch Utility Functions.

In: Journal of Multi-Criteria Decision Analysis (vol. 23) , pg. 63-71

(2016)

DOI: 10.1002/mcda.1560

Abstract anzeigen

One‐switch utility functions model situations in which the preference between two alternatives switches only once as the outcome of one attribute of both alternatives changes from low to high. Recent research cites evidence that the sum of exponential functions (sumex) is the most convincing type for modelling one‐switch utility functions. Sumex functions allow to model exactly one preferential switch and they are convenient for estimating one‐switch utility functions. However, it is unclear so far if sumex functions are suitable to model preferential switches that are perceivable by a decision maker. This paper first analyses how different the utility of two alternatives before and after a preferential can be modelled with sumex functions given that the preferential switch is caused by a particular attribute outcome improvement. It thereafter investigates how accurately decision makers perceive such utility differences.

Zeitschriftenartikel

Michael Scholz

R Package clickstream : Analyzing Clickstream Data with Markov Chains.

In: Journal of Statistical Software (vol. 74) , pg. 1-17

(2016)

DOI: 10.18637/jss.v074.i04

Abstract anzeigen

Clickstream analysis is a useful tool for investigating consumer behavior, market research and software testing. I present the clickstream package which provides functionality for reading, clustering, analyzing and writing clickstreams in R. The package allows for a modeling of lists of clickstreams as zero-, first- and higher-order Markov chains. I illustrate the application of clickstream for a list of representative clickstreams from an online store.

Beitrag in Sammelwerk/Tagungsband

M. Bergmeier
O. Ivanova
D. Totzek
Michael Scholz

What Makes a Hot Deal? Drivers of Deal Popularity in Online Deal Communities.

In:
S. Kien
P. Agerfalk
N. Levina

Curran

(2016)

Beitrag in Sammelwerk/Tagungsband

Michael Scholz
F. Lauri

The Effect of Producer Descriptions on Demand of Mobile Applications.

In:
V. Tuunainen
I. Ramos
H. Krcmar

Guimaraes

(2017)

Zeitschriftenartikel

Michael Scholz
J. Pfeiffer
F. Rothlauf

Using PageRank for non-personalized default rankings in dynamic markets.

In: European Journal of Operational Research (vol. 260) , pg. 388-401

(2017)

DOI: 10.1016/j.ejor.2016.12.022

Abstract anzeigen

Default ranking algorithms are used to generate non-personalized product rankings for standard consumers, for example, on landing pages of online stores. Default rankings are created without any information about the consumers’ preferences. This paper proposes using the product centrality ranking algorithm (PCRA), which solves some problems of existing default ranking algorithms: Existing approaches either have low accuracy, because they rely on only one product attribute, or they are unable to estimate ranks for new or updated products, because they use past consumer behavior, such as previous sales or ratings. The PCRA uses the PageRank centrality of products in a product domination graph to determine their ranks. The product domination graph models products as nodes and the dominance relations between the products’ attribute levels as edges. In a laboratory experiment with three product categories (energy saving lamps, hotel rooms, and washing machines), the PCRA leads to more accurate rankings than existing approaches provide. The PCRA ranks the lamps and washing machines that consumers prefer up to 1.5 positions higher in the default ranking than any of the existing algorithms. Only sorting hotel rooms’ price in ascending order beats the PCRA. Price is by far the most important attribute of hotel rooms for our consumer sample; therefore, a ranking that only considers price can beat a multi-attribute ranking like the PCRA, which assumes equal attribute weights. In summary, the PCRA is especially applicable to products where consumers consider more than one attribute and in markets where the product assortments change constantly.

Zeitschriftenartikel

Michael Scholz
V. Dorner
G. Schryen
A. Benlian

A configuration-based recommender system for supporting e-commerce decisions.

In: European Journal of Operational Research (vol. 259) , pg. 205-215

(2017)

DOI: 10.1016/j.ejor.2016.09.057

Abstract anzeigen

Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers’ decision processes in e-commerce shopping tasks.

Zeitschriftenartikel

Michael Scholz

Estimating demand function parameters of mobile applications.

In: Economics of Innovation and New Technology (vol. 26) , pg. 621-633

(2017)

DOI: 10.1080/10438599.2017.1263444

Abstract anzeigen

The vibrant market for mobile applications has raised awareness of several professional and also voluntary software developers. The key question especially for professional developers is how to improve the profit gained with a developed app. Recent research provided evidence on the factors that determine the demand of a mobile app. This paper presents a procedure to estimate demand function parameters that are required for developing pricing, advertising and also product update strategies. More specifically, the procedure estimates an app’s maximal willingness to pay, demand elasticity on price and network value. The procedure is based on the Fulfilled Expectations Cournot Model and requires knowledge about the apps being considered as substitutes to each other. It is applied to a data set consisting of download rank data of Apple iPhone apps.

Zeitschriftenartikel

O. Ivanova
Michael Scholz

How can online marketplaces reduce rating manipulation? A new approach on dynamic aggregation of online ratings.

In: Decision Support Systems (vol. 104) , pg. 64-78

(2017)

DOI: 10.1016/j.dss.2017.10.003

Abstract anzeigen

Retailers' incentives to manipulate online ratings can undermine consumers' trust in online marketplaces. Finding ways to avoid fake ratings has become a fundamental problem. Most marketplaces update product ratings immediately, i.e., display new ratings as soon as they are submitted. Some platforms have proposed to reduce the frequency of rating updates, as hiding ratings for a certain amount of time allows identifying and eliminating bursts of suspicious ratings. Reducing the update frequency also allows aggregating ratings and displaying only a summary statistic (e.g., mean of ratings). Although such aggregation helps to reduce the amount of fake ratings, as multiple fake ratings get represented by only one value, it might also distort legitimate ratings from real customers and hence have negative impact on honest retailers. In the present study, we propose and evaluate a novel method that instead of displaying every new rating immediately, aggregates a sequence of most recent ratings to k-values, with k determined dynamically based on the distribution of the recent ratings. In a simulation, we demonstrate that our proposed method outperforms state-of-the-art aggregation methods – it effectively reduces the impact of fake ratings on sales, and at the same time only marginally affects sales of honest retailers. Our proposed method can be easily integrated in online rating systems and can be especially used for designing fraud-resistant ranking algorithms.

Zeitschriftenartikel

Michael Scholz
M. Franz
O. Hinz

Effects of decision space information on MAUT-based systems that support purchase decision processes.

In: Decision Support Systems (vol. 97) , pg. 43-57

(2017)

DOI: 10.1016/j.dss.2017.03.004

Abstract anzeigen

This paper shows that decision makers often have a misconception of the decision space. The decision space is constituted by the relations among the attributes describing the alternatives available in a decision situation. The paper demonstrates that these misconceptions negatively affect the usage and perceptions of MAUT-based decision support systems. To overcome these negative effects, this paper proposes to use a visualization method based on singular value decomposition to give decision makers insights into the attribute relations. In a laboratory experiment in cooperation with Germany's largest Internet real estate website, this paper moreover evaluates the proposed solution and shows that our solution improves decision makers' usage and perceptions of MAUT-based decision support systems. We further show that information about the decision space ultimately affects variables relevant for the economic success of decision support system providers such as reuse intention and the probability to act as a promoter for the systems.

Zeitschriftenartikel

Michael Scholz
J. Schnurbus
H. Haupt
V. Dorner
A. Landherr
F. Probst

Dynamic effects of user- and marketer-generated content on consumer purchase behavior: Modeling the hierarchical structure of social media websites.

In: Decision Support Systems (vol. 113) , pg. 43-55

(2018)

DOI: 10.1016/j.dss.2018.07.001

Abstract anzeigen

User- and marketer-generated content items on social media platforms are supposed to have an impact on economic target variables, such as variables measuring consumers' purchase behavior. The position of each content item – and thus the impact on economic variables – changes with newly appearing items. We propose a hierarchy score to capture the dynamics of the content items on social media platforms. In order to mimic the reduced visibility of earlier content items, our hierarchy score computes the position of content items based on the number of text line equivalents of content items above a particular item. Employing the proposed hierarchy score in a dynamic regression framework for data of a large online store yields improved estimates and predictions compared to a variety of other models.

Beitrag in Sammelwerk/Tagungsband

T. Wimmer
Michael Scholz

Online Product Descriptions - Boost for your Sales?.

In:
T. Ludwig
V. Pipek

Siegen pg. 498-512

(2019)

Zeitschriftenartikel

Michael Scholz
C. Brenner
O. Hinz

AKEGIS: automatic keyword generation for sponsored search advertising in online retailing.

In: Decision Support Systems (vol. 119) , pg. 96-106

(2019)

DOI: 10.1016/j.dss.2019.02.001

Abstract anzeigen

Sponsored search advertisers face several complex decisions when planning and implementing a new sponsored search advertising campaign. These decisions include the selection of keywords, the definition of landing pages, and the formulation of bidding strategies. Relatively low attention has been paid on supporting the selection of keywords in recent research and most studies on sponsored search advertising focus on the formulation of bidding strategies and strategies for budget planning. We present a novel approach for automatically generating sponsored search keywords that relies on the theory of consumer search behavior. Our approach uses an online store's internal search log to extract keywords used by consumers within their search process, because recent research has shown that especially consumers with a high conversion probability that exhibit goal-directed instead of exploratory search patterns use an online store's internal search engine. We empirically test our approach based on a store's internal search engine and identify the effects of this approach by comparing it to a state-of-the-art approach. Our analysis reveals that our approach substantially increased the number of profitable keywords, improved the store's conversion rate by approximately 41%, and decreased the average cost per click by more than 70%.

Beitrag in Sammelwerk/Tagungsband

A. Keller
Michael Scholz

Trading on Cryptocurrency Markets: Analyzing the Behavior of Bitcoin Investors.

In:
J. Fedorowicz
W. Boh
H. Krcmar
S. Wattal
J. Leimeister

(2019)

Beitrag in Sammelwerk/Tagungsband

Bernhard Daffner
Michael Scholz
Jörg Bauer
Diane Ahrens

Kapazitäts- und zeitrestringierte Tourenplanung am Beispiel eines mittelständischen Großhändlers.

In:
G. Reiner
S. Stein
F. Starkl
M. Prandtstetter
U. Brunner
T. Walkolbinger

Linz, Österreich: Trauner Verlag pg. 207-218

(2021)

Zeitschriftenartikel

Michael Scholz
T. Wimmer

A comparison of classification methods across different data complexity scenarios and datasets.

In: Expert Systems with Applications (vol. 168) , pg. 114217

(2021)

DOI: 10.1016/j.eswa.2020.114217

Abstract anzeigen

Recent research assessed the performance of classification methods mainly on concrete datasets whose statistical characteristics are unknown or unreported. The performance furthermore is often determined by only one performance measure, such as the area under the receiver operating characteristic curve. The performance of several classification methods in four different complexity scenarios and on datasets described by five data characteristics is compared in this paper. Synthetical datasets are used to control their statistical characteristics and real datasets are used to verify our findings. The performance of each classification method is determined by six measures. The investigation reveals that heterogeneous classifiers perform best on average, bagged CART is especially recommendable for datasets with low dimensionality and high sample size, kernel-based classification methods perform very well especially with a polynomial kernel, but require a rather long time for training and a nearest shrunken neighbor classifier is recommendable in case of unbalanced datasets. These findings help researchers and practitioners finding an appropriate method for their binary classification problems.

Vortrag

Michael Scholz
Melanie Dietmeier

Packing and stacking rings into rectangular bins.

Hagenberg im Mühlkreis, Österreich 17.-19.11.2021.

(2021)

Zeitschriftenartikel

Leon Binder
Michael Scholz
Roman-David Kulko

A Comparison of Convolutional Neural Networks and Feature-Based Machine Learning Methods for the Ripeness Classification of Strawberries.

In: Bavarian Journal of Applied Sciences , pg. 124-137

(2022)

DOI: 10.25929/bjas202285

Abstract anzeigen

A variety of machine learning methods are often used for ripeness detection of fruits and vegetables using image data. Existing research in this area often focuses only on training feature-based classifiers or on using raw images with convolutional neural networks. The purpose of this paper is to compare both approaches in terms of their classification accuracy. To answer our research question, we analyze the performance of convolutional neural networks and different feature-based classifiers on a balanced dataset consisting of three strawberry ripeness classes: unripe, ripe, and overripe. Our investigation shows that convolutional neural networks outperform almost all feature-based classifier. However, the penalized multinomial regression achieves the best accuracy of 86.27 % without any hyper-parameter tuning. Another insight is that different methods lead to the best sensitivity for different ripeness classes. Convolutional neural networks most accurately classify unripe strawberries, while ripe strawberries are best classified by penalized discriminant analysis and overripe berries are best classified by penalized multinomial regression.

Zeitschriftenartikel

Michael Scholz
Melanie Dietmeier

Packing and stacking rings into rectangular bins.

In: Procedia Computer Science (vol. 200) , pg. 768-777

(2022)

DOI: 10.1016/j.procs.2022.01.275

Abstract anzeigen

In this paper, we propose a new variant of the circular packing problem whose objective is to pack rings with unequal outer and inner radii into larger rings and finally into rectangular bins of unequal sizes. This problem arises in industrial contexts where rings are cut from rectangular raw material. We divide this problem into three partial problems, formulate a model for these partial problems and provide heuristic algorithms. Our algorithms are based on some simplifications that make it possible to identify eco-efficient packing layouts also for large numbers of ring sizes and moderate numbers of bin sizes. In a computational evaluation of our algorithms, we demonstrate that the packing problem can be solved in approximately 70 seconds on average when 7,500 rings with 50 different outer circle sizes are packed into bins of seven different sizes.

Zeitschriftenartikel

Benedikt Elser
Michael Scholz

Price Optimisation of Perishable Goods Using a Genetic Algorithm.

In: International Journal of Revenue Management (vol. 1) , pg. 1

(2022)

DOI: 10.1504/IJRM.2022.10044440

Abstract anzeigen

Multi-product profit optimisation problems have been studied under nested logit models of consumer behaviour. Although attractive through to the relaxation of strong assumptions of multinomial logit models, nested logit models as well as multinomial logit models require costly discrete choice experiments in order to collect data for estimating model parameters. We propose a novel formulation of multi-product profit optimisation that is especially useful for perishable goods that are of the same type and different only in their quality level. Our model relies on willingness to pay data that can be elicited directly, derived from market data or measured indirectly in auctions or through transactions. We furthermore present a genetic algorithm for solving the formulated multi-product profit optimisation and show that our proposed genetic algorithm finds nearby optimal solutions within a very short time span.

Zeitschriftenartikel

Michael Scholz
Roman-David Kulko

Dynamic pricing of perishable food as a sustainable business model.

In: British Food Journal (BFJ) (vol. 124) , pg. 1609-1621

(2022)

DOI: 10.1108/BFJ-03-2021-0294

Abstract anzeigen

Purpose The purpose of this paper is to (1) investigate the effect of freshness on consumers' willingness to pay, (2) derive static and dynamic pricing strategies and (3) compare the effect of these pricing strategies on a retailer's revenue and food waste. This investigation helps to reveal the potentials of dynamic pricing strategies for building more sustainable business models. Design/methodology/approach The authors conduct an online experiment to measure consumers' willingness to pay for fresh and three-days’ old strawberries. The impact of freshness on willingness to pay is analysed using univariate tests and regression analysis. Pricing strategies are compared using a Monte Carlo simulation. Findings The results of this study show that freshness largely determines consumers' willingness to pay and price sensitivity. This renders dynamic pricing a promising strategy from an economic point of view. The results of the simulation study show that food waste can be reduced by up to 53.6% with a dynamic pricing instead of a static pricing strategy in the case that there are as many consumers as strawberry packages in the inventory. Revenue can be increased by up to 10% compared to a static pricing strategy based on fresh strawberries. Practical implications This study suggests that food retailers can improve their revenue when switching from static to dynamic pricing. Furthermore, in most cases, food retailers can reduce food waste with a dynamic instead of a static-pricing strategy, which might help to improve their image through a more sustainable business model and attract additional consumers. Originality/value This study is the first to analyse the possibility of using food freshness to design a dynamic pricing strategy and to analyse the impact of such a pricing strategy on both, a retailer's revenue and a retailer's food waste.

Zeitschriftenartikel

Leon Binder
Simon Rackl
Michael Scholz
Mathias Hartmann

Linking Thermal Images with 3D Models for FFF Printing.

In: Procedia Computer Science (vol. 217) , pg. 1168-1177

(2023)

DOI: 10.1016/j.procs.2022.12.315

Abstract anzeigen

The thermal profile plays a major role in additive manufacturing. Thermal cameras are thus increasingly used for quality monitoring. So far, either full thermal images or metrics extracted from them are used to monitor the manufacturing quality or detect defects. To additionally allow the detection of local anomalies, it is necessary to link the thermal image to the 3D object geometry. We propose a framework that includes steps for filtering object points that are visible from the camera perspective, projecting 3D points onto thermal images and removing pixels that represent the printhead. Our framework can be used for process monitoring and subsequent on-line defect detection which are necessary components for production automation and Industry 4.0 applications. In a validation experiment, we show that the temperature extracted from thermal images and assigned to 1mm × 1mm × 1mm voxels is highly correlated to the temperature measured with type K thermocouples.

Beitrag in Sammelwerk/Tagungsband

P. Yadav
Michael Scholz
N. Pervin

Revisiting Sleeping Beauties in Science: A Novel Coefficient to Identify Delayed Recognition using Citation Trajectories.

(2023)

Beitrag in Sammelwerk/Tagungsband

P. Yadav
N. Pervin
Michael Scholz

Ahead of the Curve: Identifying Timely Potential in IS Research-Determinants of Delayed Recognition and Insights into Reader Engagement.

pg. 155

(2024)

Beitrag in Sammelwerk/Tagungsband

Leon Binder
Johannes Kuchler
Michael Scholz

Reducing shipping cost of packages by optimizing the bin set.

(2024)

Beitrag in Sammelwerk/Tagungsband

Johannes Kuchler
Leon Binder
Michael Scholz

Selecting and packing bins - A human perspective.

(2024)

Beitrag in Sammelwerk/Tagungsband

Michael Scholz
Jörg Bauer

OTE: A Tool For Extracting Tabular Purchasing Order Information From PDF Documents.

In:
A. Lopata
J. Čeponis
D. Gudonienė
R. Butkienė
Winner Best Paper Award.

Cham: Springer Nature Switzerland AG

DOI: 10.1007/978-3-031-84263-4_14

(2025)