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- Özlü, Neslihan, 1980-
(författare)
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The Heterogeneity of Behavior in Operations Processes : Empirical Evidence
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Behavioral science research has established that observed human behavior may deviate considerably from model suggestions. In addition to the realization that there is no such thing as standardized human behavior, there are also substantial differences in how people deviate from the model: Different individuals make dissimilar decisions in the same situation when using the same information. Recently, behavioral operations management has seen an increased focus on understanding the role of humans in the decisions and processes these models aim to capture. However, still little is known about the factors that determine the observed behavioral heterogeneity.Advancements in technology have made it possible to collect and analyze data at granular levels. The availability of such detailed data has increased the ability of the behavioral sciences to examine behavior with techniques from data science and empirical analysis. Therein lies the possibility of capturing the human role in processes and improving them according to the results.The overarching purpose of this dissertation is to enhance the understanding of what drives heterogeneity of behavior in operations processes. To fulfill this purpose, this dissertation presents four studies; each targeting drivers of the heterogeneity in different operations processes. The four studies focus on decision making, determining choices, and forecasting in different empirical settings.The first study analyzes the ordering behavior of purchasing agents when placing orders involving uncertain lead times with suppliers. Purchasers seem to rely on their prior encounters with suppliers and add safety times depending on the type and recency of their experiences with them. This behavior may lead to early ordering to avoid late deliveries. Our results inform behavioral operations by examining the mechanism behind experiential learning in an industrial setting. The second study explores e-commerce customers’ choices of fulfillment methods. It disentangles the importance of a customer’s perceived delivery convenience from factors such as order fulfillment speed and price of delivery.The third study investigates industrial purchasing again, this time analyzing how purchasers time their orders depending on previous experiences with specific suppliers and the experiences of peers. Our findings contribute to the literature on supply chain disruptions and the effect of rare events on an individual’s ordering decisions, and highlight how individuals learn from their own as well as from peers' experiences. The fourth study focuses on the effect of lifetime experiences on professionals who are tasked with forecasting inflation over an extended period. We provide evidence that systematic differences in forecasts of inflation across generations of economic forecasters are due to varied lifetime experiences. This outcome adds to the literature on experiential effects by emphasizing the importance of individual judgment versus available information in forecasting tasks.All studies use field data collected from several companies to explore drivers of behavioral heterogeneity in operations processes. Their findings permit a better understanding of what drives variations of behavior in different operations settings, which through further elaboration and research can help businesses set more targeted policies and management priorities.
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| 2. |
- Baldauf, Christoph, 1990-
(författare)
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Empirical Essays on Retail Logistics and Customer Behavior
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Retail logistics is responsible for making products available to end customers. Traditionally, this was facilitated through a network of brick and mortar stores that customers visit to buy items. However, the advent of online and omnichannel retail started to challenge this established system. Modern-day customers increasingly buy a product not solely for its features but also for how, and how fast, the item is delivered and/or returned. Such customer behavior shows that retail logistics often is the decisive factor when searching for, purchasing, and using goods and services. Consequently, logistics performance has become increasingly important for marketing performance. This is in stark contrast to logistics (often) outdated characterization as a back-office operation focusing on cost-effectiveness and lead-time reductions. To overcome the prevailing cost focus, more knowledge is needed that is based on the incorporation of customer behavior into operations management models to demonstrate to retailers the elevated role of logistics in retail today. Therefore, the overall purpose of this dissertation is to contribute to a better understanding of the relevance of logistics in online and omnichannel retail, and its impact on customer behavior.This purpose is explored in four research articles that examine the relevance of logistics from various angles. Article I is a systematic literature review synthesizing empirical literature on the impact of logistics on customer purchase, repurchase, and return behavior in online and omnichannel retail. Article II analyzes how carrying a stock-keeping unit in inventory at the warehouse affects its sales in online retail. Article III investigates how an online retailer’s change in return policy to free returns increases purchases from customers, but also the volume of items being returned to the retailer. Finally, Article IV examines how a buy-online-return-to-store policy in omnichannel retail impacts store performance.The theoretical contributions to the literature are as follows. First, the dissertation illustrates the relevance of logistics in retail today by showing how logistics does not only impact the cost side of a business but also customer behavior and, hence, the demand side. Second, the research highlights the integrative approach that retailers need to adopt between the marketing and operations functions to operate successfully, as action taken by one business function increasingly impacts the other. Third, the dissertation accentuates the role of people in operations management research. It emphasizes how customer behavior is impacted by retail logistics, and thus adds to a better understanding of how people affect real-life operational processes.
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| 3. |
- Martí Bigorra, Anna, 1990-
(författare)
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Customer Data in the Design Process with Focus on Customer Neds and Way of using the Product
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Owing to continuous advances in information technology, access to information via the Internet and the steady decline of cost related to data creation, big amounts of customer data now reside in many companies. This data is said to hold a large amount of valuable knowledge that could be used to design customer-focused products, a key factor for maintaining market-share. Information overload hinders the search for knowledge and, therefore, it is a challenge for companies to identify what is relevant to analyse. Different approaches based on data mining tools of web-based customer data have been shown to be useful for gaining customer insight. However, this information is not properly factored into the target setting process. Many improvements in modelling the relation between product performance and customer satisfaction during the target setting have been presented. However, these still rely on customer information obtained from traditional gathering techniques such as questionnaires, which do not provide enough valuable and deep customer information; therefore, designers are forced to make assumptions. While some studies highlight the potential of customer data as an aid to designing future product generations, they do not provide enough details on how such information should be processed to generate valuable information for the designers. By taking advantage of the generated customer data, this work aims to increase the reliability of the design decisions on product specifications by reducing the existing gap between the customer and the designer world. To do so, customer information from different sources such as surveys and usage data have been combined to model customer satisfaction as a function of design requirements. In this process, customer needs are defined at a detailed level to be able to link customer satisfaction with a clear interface to the design requirements. By means of usage data, customer-product interaction in the customer environment is investigated, and differences between designer assumptions and customer picture are calculated towards the target fulfilment indicator. Results show that the work presented helps designers to set targets towards a higher customer focus, since customer needs and way of using the product become visible in the process. This allows the design team not only to identify differences among customers but also the possibility to detect changes in customer needs. The target fulfilment indicator acts as a feedback channel for continuous product improvement, allowing designers to validate their decisions. Since the voice of the customer drives the process, the presented approaches guide the design team towards the most relevant customer data, thus streamlining the design process in a situation where the amount of information rapidly increases.
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| 4. |
- Marti Bigorra, Anna, 1990-
(författare)
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Customer-focused data-driven target setting
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To develop products through a customer-centric strategy, early stages of product development such as target setting play an important role. In the target setting stage Customer Needs (CN) are gathered and translated into Design Requirements (DR) in order to subsequently set product targets that fit cost constraints and at the same time result in high Customer Satisfaction (CS). Continuous advances in information technology create new opportunities for companies to gather information about the customer, for example, for marketing purposes, or to assess customer reactions after the launch of new products. In addition, products are becoming complex systems that are successively equipped with more software and sensors offering opportunities for collecting data on how they are used. Knowing how customers use the product enhances a company’s ability to segment customers and customize products.Despite customer information availability from different sources (sensors, social media, etc.), surveys and focus groups are considered today as the main data source to derive the set of CN statements during target setting. Further, the team’s interpretation of CNs, which are often described in abstract language, must be translated into DRs, which are described in a more technical language. Hence, the translation process of CNs into DRs is said to be subjective. To set product targets, CS sensitivity to changes in DR levels is also considered. Surveys and benchmarking data containing customer perceptions on competitors’ performance are often the main customer data input into the process. While insightful information may be obtained, surveys are costly and time consuming and only encompass a small part of the market population.The research presented in this doctoral thesis explores how customer information obtained from sensors (e.g. product usage data) and text data (e.g. from websites, open-survey questionnaires) can be factored in the target setting process before concept generation to enhance customer focus without compromising product development time. The aim is to increase designers’ awareness of target population and in turn increase the quality of the design decisions on product targets. For this purpose, a customer-focused data-driven target setting methodology is proposed. The presented methodology changes the actual target setting methodology by means of indicators and autonomous activities on those parts of the process where marketing or design decisions are needed. The proposed methodology gives the incentive for a more integrated product development where marketing and designers need to work closely. This further allows a sustainable customer information gathering strategy that strives for missing customer information that is required for setting product targets. The indicators act as feedback channels for continuous product improvement. The use of such indicators and autonomous activities highlights the potential of a more efficient, less subjective and higher-quality target setting process.
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