7 research outputs found

    Procurement Auctions and Negotiations: An Empirical Comparison

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    This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Organizational Computing and Electronic Commerce on Aug. 2, 2017, available online: http://www.tandfonline.com/10.1080/10919392.2017.1363576.Real world procurement transactions often involve multiple attributes and multiple vendors. Successful procurement involves vendor selection through appropriate market mechanisms. The advancement of information technologies has enabled different mechanisms to be applied to similar procurement situations. Advantages and disadvantages of using such mechanisms remain unclear. The presented research compares two types of mechanisms: multi-attribute reverse auctions and multi-attribute multi-bilateral negotiations in e-procurement. Both laboratory and online experiments were carried out to examine their effects on the process, outcomes and suppliers’ assessment. The results show that in procurement, reverse auctions were more efficient than negotiations in terms of the process. Auctions also led to greater gains for the buyers than negotiations but the suppliers’ profit was lower in auctions. The buyer and the winning supplier jointly reached more efficient and balanced contracts in negotiations than in auctions. The results also show that the suppliers’ assessment was affected by their outcomes: the winning suppliers had a more positive assessment towards the process, outcomes and the system. The findings are consistent in both the laboratory and online settings. The implications of this study for practitioners and researchers are discussed

    A USER’S COGNITIVE WORKLOAD PERSPECTIVE IN NEGOTIATION SUPPORT SYSTEMS: AN EYE-TRACKING EXPERIMENT

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    Replying to several research calls, I report promising results from an initial experiment which com-pares different negotiation support system approaches concerning their potential to reduce a user’s cognitive workload. Using a novel laboratory-based non-intrusive objective measurement technique which derives the user’s cognitive workload from pupillary responses and eye-movements, I experi-mentally evaluated a standard, a chat-based, and an argumentation-based negotiation support system and found that a higher assistance level of negotiation support systems actually leads to a lower user’s cognitive workload. In more detail, I found that an argumentation-based system which fully automates the generation of the user’s arguments significantly decreases the user’s cognitive workload compared to a standard system. In addition I found that a negotiation support system implementing an additional chat function significantly causes higher cognitive workload for users compared to a standard system

    Agent-level determinants of price expectation formation in online double-sided auctions

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    For an auctioneer, it is of utmost importance to design an auction mechanism that gives robust price signals which in turn increases auction performance. Information architecture and forward trading platforms are the two main information sources that could generate these price signals. However the traditional presumption that agents form rational expectations by accurately processing all available information in the online trading environment and forming their expectations accordingly has found mixed support. We develop a research model that empirically tests the impact of agents’ attitudes on their price expectation through their trading behaviour. Using a unique data set, we tested our hypotheses on real ex ante forecasts, evaluated ex post, in an electricity day ahead auction context. This paper is one of the first to take an information-based view to study the trading behaviour of agents and their price expectations, with results that suggest a re-consideration of some of the conventional concepts

    Three Studies on Multi-attribute Market Mechanisms in E-procurement

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    Successful e-procurement depends on selecting the appropriate mechanisms that comprise rules governing and facilitating transaction process. Existing mechanisms have theoretical or practical limitations such as limited number of attributes, disclosure of buyer’s preferences and costly processes. The present research addresses these issues through three studies. Study 1 presents two feasible mechanisms for multi-attribute multi-supplier transactions. They allow buyers to control preference representation and information revelation, assuring that suppliers obtain sufficient information in making effective proposals while protecting confidential information. Following the design-science approach, the mechanisms are implemented to support multi-attribute reverse auctions and multi-bilateral negotiations. Study 2 examines the revelation of information in multi-attribute reverse auctions. Three revelation rules are formulated with admissible bids, winning bids and all bidders’ bids. Their effects on the process, outcomes and bidders’ assessment are tested in two experiments. The results show significant improvement in process efficiency when more information is revealed. The suppliers reached better outcomes with either admissible bids only or all bidders’ bids, while the buyers gained more when revealing the winning bids only. Bidders were more satisfied with the outcomes and system when more information was provided. Study 3 compares multi-attribute reverse auctions and multi-bilateral negotiations in both laboratory and online experiments. The results show that auctions are more efficient than negotiations in terms of the process. Auctions led to greater gains for the buyers, whereas more balanced contracts were reached in negotiations. Suppliers’ assessment was affected by their outcomes, and the winning suppliers were more satisfied with the process, outcomes and system. The buyer’s role was also examined. Different types of information conveyed from buyer influence suppliers’ behavior in making bids/offers and concessions, which in turn affected buyer’s gains. This research provides implications to future studies and practices in e-procurement, in particular, the formulation of a procedure of two multi-attribute mechanisms and the formulation of general guidelines for strategic use of different mechanisms in various e-procurement contexts

    Mobility in a Globalised World 2014

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    The term mobility has different meanings in the following science disciplines. In economics, mobility is the ability of an individual or a group to improve their economic status in relation to income and wealth within their lifetime or between generations. In information systems and computer science, mobility is used for the concept of mobile computing, in which a computer is transported by a person during normal use. Logistics creates by the design of logistics networks the infrastructure for the mobility of people and goods. Electric mobility is one of today’s solutions from engineering perspective to reduce the need of energy resources and environmental impact. Moreover, for urban planning, mobility is the crunch question about how to optimise the different needs for mobility and how to link different transportation systems. In this publication we collected the ideas of practitioners, researchers, and government officials regarding the different modes of mobility in a globalised world, focusing on both domestic and international issues

    Organizational Decision-making in the Age of Big Data and Artificial Intelligence

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    This dissertation examines organizational decision-making in the context of big data and artificial intelligence (machine learning) technologies. There are three studies. All three focus on collaborative decision-making in organizations, with study 1 examining it in the context of big data, while study 2 and 3 in the context of artificial intelligence. Study 1: This study examines the impact of different manners of presenting information on collaborative decision-making performance. Using controlled economic experiments, I assign participants with a resource allocation decision-making task (adapted from the game theoretic public goods provision problem) and examine the collaborative outcomes of groups when exposed to different levels of information aggregation and visualization formats. Interestingly, the results show that in certain cases, the more effective means of presenting information for individuals (i.e., graphs or tables compared to raw data) do not bode as well for groups. This study contributes to the information visualization literature, which has mostly looked at individual decision-making, by examining the collaborative task context and by combining perspectives from experimental game theory, cognitive fit and information processing theories. Methodologically, this study also contributes to the information visualization literature by accounting for the dynamics of collaborative decision-making over time. Study 2: Organizations increasingly deploy artificial intelligence (AI) systems to automate specific tasks and assist human experts in organizational decision-making. In this study, I focus on complex task settings wherein human decision-makers work with AI systems. Using credit authorization for consumer loans as our specific context, I conduct an economic experiment with a repeated round design to investigate how organizations can create business value from the new human–machine collaborative decision-making paradigm. This study contributes to extant literatures on algorithmic decision-making and automation by moving beyond only examining individual decision-makers’ attributes to examine the intertwined roles of organizational factors and AI’s characteristics. The results show that when firms implement complementary organizational practices in parallel with AI investments, they achieve higher levels of algorithm appreciation, leading to better decisions, made with stronger confidence, in turn increasing organizational profits. I also show that human decision-makers and machines develop increasingly more effective work relationships over time and outperform AI machines in stand-alone settings. Finally, I show evidence that keeping humans in the loop could enable AI-powered firms to achieve the most productive outcomes. Study 3: Extant research on algorithmic bias has mostly approached the subject from a technical perspective, with few studies investigating the decision bias of human-machine collaborative decision-making, wherein human experts have the final say after working with the algorithms. In this study, I conduct a controlled economic experiment with a repeated-round design. I assign participants with a task that models a complex organizational decision-making process wherein human decision-makers (DMs) work with an AI repeatedly over 10 decision periods to evaluate consumer loan applications. I use loan data from a large-scale, historic dataset and manipulate the AI predictions to create two experimental conditions: (1) Prediction Bias, where DMs work with AI predictions that discriminate against one group of loan applicants and favor another, and (2) No Bias, where DMs work with AI predictions that treat the two loan applicant groups equally. This study contributes to current research on algorithmic bias mitigation and bias in human-machine collaboration by showing that human DMs can over time learn to adapt to a biased algorithm, implicitly detect the bias in the AI, adjust their behavior to significantly improve their performance, and importantly, outperform the biased AI working alone, both in terms of reducing decision bias and increasing organizational profit

    Surveillance of Complex Auction Markets: a Market Policy Analytics Approach

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    The dissertation consists of four essays that investigates the merits of big data-driven decision-making in the surveillance of complex auction markets. In the first essay, Avci and her co-researchers examine the aggregate-level bidding strategies and market efficiency in a multi-time tariff setting by using parametric and semi parametric methods. In the second essay, they address three key forecasting challenges; risk of selection of an inadequate forecasting method and transparency level of the market and market-specific multi-seasonality factors in a semi-transparent auction market. In the third essay, they demonstrate the effect of information feedback mechanisms on bidders’ price expectations in complex auction markets with the existence of forward contracts. They develop a research model that empirically tests the impact of bidders’ attitudes on their price expectation through their trading behavior and tested their hypotheses on real ex-ante forecasts, evaluated ex-post. In the fourth essay, they investigate characterization of bidding strategies in an oligopolistic multi-unit auction and then examine the interactions between different strategies and auction design parameters. This dissertation offers important implications to theory and practice of surveillance of complex auction markets. From the theoretical perspective, this is, to our best knowledge, the first research that systematically examines the interplay of different informational and strategic factors in oligopolistic multi-unit auction markets. From the policy perspective, Avci’s research shows that integration of big data analytics and domain-specific knowledge improves decision-making in surveillance of complex auction markets
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