Single-Sourcing vs. Multisourcing: An Empirical Analysis of Large Information Technology Outsourcing Arrangements

2023 | Information Systems Research | Citations: 0

Authors: Bapna, Ravi; Gupta, Alok; Ray, Gautam; Singh, Shweta

Abstract: As the information technology (IT) services landscape matures, clients are incre ... Expand

Abstract: As the information technology (IT) services landscape matures, clients are increasingly adopting multisourcing arrangements that involve multiple vendors. Although a large body of information systems (IS) literature addresses issues of whether to outsource (to a single vendor), what types of contracts to use, and how to achieve optimal relational governance, little is known about the antecedents and consequents of the single versus multisourcing decision. Moreover, while conceptual and analytical models of single-sourcing versus multisourcing have been developed, there is no empirical IS research using a large-scale data set with rigorous econometric analysis that examines the antecedents and consequents of multisourcing in the IT context. This paper fills this void, using the transaction cost economic lens and a data set of 49,057 large IT outsourcing arrangements that spans multiple industries and dates back 25 years. We find that there is a curvilinear relationship between number of IT services in an IT outsourcing arrangement and the likelihood of multisourcing. This relationship increases as the number of IT services increases to up to five services and then decreases. For managers who plan to multisource IT outsourcing arrangements, this research provides guidance to minimize exchange hazards through a better understanding of the relationship between sourcing choice, client IT outsourcing capabilities, the competitiveness of the vendor landscape, and the number of IT services in an IT outsourcing arrangement. We provide empirical evidence that the choice between single-sourcing and multisourcing is material to the performance of outsourcing contracts as an incorrect sourcing choice is likely to result in negative contract outcomes. History: Paul A. Pavlou, Senior Editor; Ning Su, Associate Editor. Collapse

Topics: outsourcing system integration IS management service desk data center

Methods: XGBoost robustness check machine learning k-nearest neighbor statistical hypothesis test

Theories: transaction cost economics game theory

“I NEED HELP – NOW!”: THE ROLE OF TECHNICAL SUPPORT IN THE PROCESS OF IS USE COPING

2023 | European Conference On Information Systems | Citations: 0

Authors: Blass, Marlene; Hörold, Felix; Schoch, Manfred

Abstract: IS research has identified technical support as an important organizational mea ... Expand

Abstract: IS research has identified technical support as an important organizational measure that increases user satisfaction and may reduce technostress. Yet, the effectiveness of such offers is dependent on whether and how users utilize them. Research has shown that there are manifold different coping sequences that users take after discrepant IT events. However, insights on the intersection between technical support and the user's own coping sequences are missing so far. To address this gap, we conduct a qualitative interview study with 31 users of technical support in a German service organization. We develop a process model that explains the coping sequences taken by users after experiencing a discrepant IT event and identify factors that influence how and why they contact technical support. Thus, we provide insights on the effective utilization of technical support and derive measures on how to best support employees in their coping efforts. Collapse

Topics: system support technostress information system use service desk database system

Methods: qualitative interview business process modeling qualitative content analysis personal interview modeling language

Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success

2023 | Information Systems Frontiers | Citations: 1

Authors: Chen, Yu; Jensen, Scott; Albert, Leslie J.; Gupta, Sambhav; Lee, Terri

Abstract: In higher education, low teacher-student ratios can make it difficult for studen ... Expand

Abstract: In higher education, low teacher-student ratios can make it difficult for students to receive immediate and interactive help. Chatbots, increasingly used in various scenarios such as customer service, work productivity, and healthcare, might be one way of helping instructors better meet student needs. However, few empirical studies in the field of Information Systems (IS) have investigated pedagogical chatbot efficacy in higher education and fewer still discuss their potential challenges and drawbacks. In this research we address this gap in the IS literature by exploring the opportunities, challenges, efficacy, and ethical concerns of using chatbots as pedagogical tools in business education. In this two study project, we conducted a chatbot-guided interview with 215 undergraduate students to understand student attitudes regarding the potential benefits and challenges of using chatbots as intelligent student assistants. Our findings revealed the potential for chatbots to help students learn basic content in a responsive, interactive, and confidential way. Findings also provided insights into student learning needs which we then used to design and develop a new, experimental chatbot assistant to teach basic AI concepts to 195 students. Results of this second study suggest chatbots can be engaging and responsive conversational learning tools for teaching basic concepts and for providing educational resources. Herein, we provide the results of both studies and discuss possible promising opportunities and ethical implications of using chatbots to support inclusive learning. Collapse

Topics: artificial intelligence IT career anonymity user experience accounting

Methods: chatbot survey qualitative interview personal interview machine learning

Getting Your RPA Priorities Straight with Process Mining: The PLOST Framework

2023 | European Conference On Information Systems | Citations: 0

Authors: Jongeling, Hilde; Lu, Xixi; Beerepoot, Iris; Weerd, Inge van de; Reijers, Hajo

Abstract: Robotic Process Automation (RPA) has gained much attention both in industry and ... Expand

Abstract: Robotic Process Automation (RPA) has gained much attention both in industry and academia. One of the main challenges for a successful implementation of RPA is selecting which tasks should be automated. While different methods exist to identify RPA candidate tasks, they lack in providing objective evidence on why to automate that task. Such objective evidence can be gathered by applying process mining techniques to gain insights into the performance of a process and its tasks. Although this has multiple advantages, it can be time-consuming to analyze all potential processes. We conducted a literature review of existing methods to identify relevant criteria and method components, based on which we designed a framework for identifying and prioritizing suitable RPA candidate tasks: the Prioritized List of Suitable Tasks (PLOST) Framework. The framework includes both qualitative and quantitative assessment criteria and guides the analyst to focus on relevant processes before zooming in on the task level. It also takes into account a customized automation strategy. We conducted a case study to evaluate the applicability and effectiveness of the PLOST Framework and performed thinking-aloud sessions to evaluate its usability, practicality, and completeness. The results show that the framework is easy to apply and feasible. Collapse

Topics: process mining business intelligence organizational value usability digital dashboard

Methods: case study cognitive walkthrough qualitative interview design science literature study

A Conceptual Model for Labeling in Reinforcement Learning Systems: A Value Co-creation Perspective

2023 | DESRIST | Citations: 0

Authors: Reinhard, Philipp; Li, Mahei Manhai; Dickhaut, Ernestine; Reh, Cornelius; Peters, Christoph; Leimeister, Jan Marco

Abstract: Artificial intelligence (AI) possesses the potential to augment customer service ... Expand

Abstract: Artificial intelligence (AI) possesses the potential to augment customer service employees e.g. via decision support or solution recommendations. Still, its underlying data for training and testing the AI systems is provided by human annotators through human-in-the-loop configurations. However, due to the high effort for annotators and lack of incentives, AI systems face low underlying data quality. That in turn results in low prediction performance and limited acceptance by the targeted user group. Faced with the enormous volume and increasing complexity of service requests, IT service management (ITSM) especially, relies on high data quality for AI systems and incorporating domain-specific knowledge. By analyzing the existing labeling process in that specific case, we design a revised to-be process and develop a conceptual model from a value co-creation perspective. Finally, a functional prototype as an instantiation in the ITSM domain is implemented and evaluated through accuracy metrics and user evaluation. The results show that the new process increases the perceived value of both labeling quality and the perceived prediction quality. Thus, we contribute a conceptual model that supports the systematic design of efficient and interactive labeling processes in diverse applications of reinforcement learning systems. Collapse

Topics: participatory design artificial intelligence recommender system data quality usability

Methods: design methodology conceptual modelling design artifact usability testing design principle

Theories: service-dominant logic

Knowledge management mechanisms and common knowledge impacts on the value of knowledge at individual and organizational levels

2023 | International Journal of Information Management | Citations: 0

Authors: Sabherwal, Rajiv; Steelman, Zach; Becerra-Fernandez, Irma

Abstract: Knowledge management (KM) impacts have typically been examined on the value of k ... Expand

Abstract: Knowledge management (KM) impacts have typically been examined on the value of knowledge to organizations but not individuals. This paper uses survey data (186 employees) and qualitative data (300 + employees) at the National Aeronautics and Space Administration to examine the effects of four KM mechanisms (social learning, learning from others through IT, learning from IT, and IT-based knowledge substitution) and common knowledge on the individual and organizational value of knowledge. The results indicate that common knowledge and the two interpersonal mechanisms (social learning, learning from others through IT) enhance the perceived value of knowledge at both levels, but both mechanisms that rely on IT for the knowledge (learning from IT, IT-based knowledge substitution) reduce the perceived knowledge value at one level and have no significant effect at the other level. Further, common knowledge strengthens the positive effects of both interpersonal mechanisms, and weakens the negative effects of IT-based knowledge substitution, on perceived value of knowledge at both levels. Collapse

Topics: organizational value explicit knowledge IS impact knowledge sharing service desk

Methods: qualitative interview chi squared test focus group structural equation modeling design artifact

Theories: assimilation theory of learning

Who gets phished? Insights from a Contextual Clustering Analysis Across Three Continents

2022 | European Conference On Information Systems | Citations: 0

Authors: Frank, Muriel; Wagner, Niklas; Ranft, Lukas Manuel

Abstract: Phishing attacks are one of the most prevalent cybersecurity threats to modern ... Expand

Abstract: Phishing attacks are one of the most prevalent cybersecurity threats to modern organizations. As a result, researchers and practitioners alike have pooled their strengths to understand who is most at risk of falling for phishing attacks. Since recent work calls for consideration of discrete context dimensions when examining phishing susceptibility, we use cluster analysis in conjunction with a large-scale phishing experiment to identify and scrutinize highly deceivable employees across three continents based on contextual factors. The results reveal salient similarities between employee groups in Europe, Australia, and North America. Consequently, our findings underscore the importance of classifying employees based on discrete contextual characteristics impacting their phishing susceptibility. Furthermore, the identified clusters have important implications for policymakers, awareness programs, and anti-phishing interventions, as they allow to better target individuals based on contextual attributes. Collapse

Topics: phishing electronic mail service desk computer crime missing data

Methods: cluster analysis experiment Mann Whitney U test chi squared test machine learning

Improving Support Ticket Systems Using Machine Learning: A Literature Review

2022 | HICSS | Citations: 0

Authors: Fuchs, Simon; Drieschner, Clemens; Wittges, Holger

Abstract: Processing customer support requests via a support ticket system is a key-elemen ... Expand

Abstract: Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field. Collapse

Topics: database system customer satisfaction artificial intelligence system support service desk

Methods: literature study computational algorithm machine learning support vector machine literature sample

Contextual drivers of employees' phishing susceptibility: Insights from a field study

2022 | Decision Support Systems | Citations: 0

Authors: Frank, Muriel; Jaeger, Lennart; Ranft, Lukas Manuel

Abstract: Phishing attacks rate as one of the most prevalent security threats to contempor ... Expand

Abstract: Phishing attacks rate as one of the most prevalent security threats to contemporary organizations. Hence, managers strive heavily to apply security measures that keep their employees safe from these risks, thereby relying on insights from security researchers who have predominantly focused on recipient characteristics, message attributes, and interventions to explicate the phishing susceptibility of individuals. A theoretical lens yet to be explored is the discrete context in which individuals encounter phishing attacks. This paper presents a multi-dimensional model – comprising the three contextual components social, task, and physical – that explains why an employee is likely to fall for phishing emails or not. To empirically validate our model, we conducted a field study among 2302 employees of an internationally operating pharmaceutical company in the United States. By combining employees' behavioral responses to a phishing email, training data, and contextual data, like help desk reliance, job status or workspace, we find that context is key to a more thorough understanding of phishing susceptibility. Moreover, our study provides practical insights on how organizations can identify and support employees prone to phishing as well as tailor training programs to prevent their workforce from falling prey to cybercriminals. Collapse

Topics: phishing service desk email phishing IT security training social network

Methods: logistic regression multidimensional modeling company material field study ANOVA

Polite Interaction Design: Capturing the Users Attention Without Compromising their Experienced Trust

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Islind, Anna Sigríður; Willermark, Sara

Abstract: Pop-ups have been widely used to control users' attention, causing a high degre ... Expand

Abstract: Pop-ups have been widely used to control users' attention, causing a high degree of irritation and dissatisfaction. We explore so-called 'polite' pop-ups, i.e., pop-ups implemented into the interface eliminating the intrusive and surprising factors. We hypothesize that: H1) Users pay less attention to, and interact less with, polite pop-ups than traditional pop-ups, and; H2) Users perceive a higher degree of trust in applications with polite pop-ups compared to traditional pop-ups. The research approach includes: i) comparative user tests with 88 participants; ii) observations of user tests; iii) assessment questionnaire, and; iv) data-driven analysis of interaction patterns. We analyze the data through the theoretical lens of trust and show that users pay less attention to, yet perceive a higher degree of trust. Our contributions include conceptualizing 'polite' design elements and the research agenda of Polite Interaction Design that aims to capture users' attention without causing unpleasant experiences or decreased trust. Collapse

Topics: Google+ user experience usability user behavior information system use

Methods: usability testing design artifact survey cognitive walkthrough design science