Organizations as Digital Enactment Systems: A Theory of Replacement of Humans by Digital Technologies in Organizational Scanning, Interpretation, and Learning

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Constantiou, Ioanna; Joshi, Mayur; Stelmaszak, Marta

Abstract: Digital transformation has become a dominant phenomenon of interest among infor ... Expand

Abstract: Digital transformation has become a dominant phenomenon of interest among information systems scholars. To account for this phenomenon, it is imperative to develop a theoretical understanding of its processes and objects. We adapt a seminal organizational theory that conceptualizes organizations as interpretation systems to a possible future of organizations. We theorize digital transformation as a progressive replacement of humans by digital technologies in performing an organization's fundamental activities underpinning the processes of scanning, interpretation, and learning that encompass an organization's interaction with its environment. As a result, organizations cease to be human interpretation systems and instead turn into digital enactment systems, where digital technologies, instead of humans, nearly autonomously create and act upon information. We illustrate this digital transformation theory using the example of high-frequency trading. This transformation redefines the relationship among organizations, information, and the environment, changing the role of humans and reshaping strategic decision-making. Thus conceived, digital transformation offers a concrete way of theorizing and accounts for deep implications on the nature of organizations and organizing in the digital age. Collapse

Topics: digital transformation IT work decision making electronic trading organization structure

Methods: theory development computational algorithm

Theories: general systems theory organizational theory

How individuals can shape AI through data - An AI literacy and morality perspective

2023 | European Conference On Information Systems | Citations: 0

Authors: Schneider, Johannes; Meske, Christian; Bikic, Antonio

Abstract: ... Expand

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Topics: artificial intelligence data quality IT productivity privacy Python

Methods: case study descriptive statistic artificial neural network supervised learning logistic regression

Theories: ethical theory

Perceived Warmth and Intelligence of AI: Impact on Employee Performance

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Savoli, Azadeh; Bhatt, Mamta

Abstract: Artificial Intelligence (AI) can extend human capabilities. Thus, when AI techn ... Expand

Abstract: Artificial Intelligence (AI) can extend human capabilities. Thus, when AI technologies are integrated into businesses, they can assist employees in their tasks by augmenting their analytical skills or automating important but manual and time-consuming tasks, freeing up employees to focus on higher-value work. Extant literature has explored the effect of AI on employee performance, with some studies highlighting its benefits (e.g., increased efficiency and effectiveness). However, other studies suggest that AI may also have a detrimental effect on employee performance (e.g., by promoting AI anxiety and threat). Despite their growing use, little is known about how employees interact with and perceive AI and how this might impact their job performance. The current study aims to address this gap in knowledge by adopting an identification perspective and focusing on the perceived warmth and intelligence of AI as two key dimensions of social perception. Our research shows that the perceived warmth and intelligence of AI have a significant impact on how employees identify with AI-based technologies and eventually on their job performance. The data for this investigation were collected from 319 customer service representatives who interacted with chatbots in their daily customer service tasks. Collapse

Topics: artificial intelligence customer service task productivity computer software individual productivity

Methods: chatbot survey structural equation modeling SEM tool data transformation

A Probabilistic Perspective of Human-Machine Interaction

2022 | HICSS | Citations: 0

Authors: Canan, Mustafa; Demir, Mustafa; Kovacic, Samual

Abstract: Human-machine interaction (HMI) has become an essential part of the daily routin ... Expand

Abstract: Human-machine interaction (HMI) has become an essential part of the daily routine in organizations. Although the machines are designed with state-of-theart Artificial Intelligence applications, they are limited in their ability to mimic human behavior. The humanhuman interaction occurs between two or more humans; when a machine replaces a human, the interaction dynamics are not the same. The results indicate that a machine that interacts with a human can increase the mental uncertainty that a human experiences. Developments in decision sciences indicate that using quantum probability theory (QPT) improves the understanding of human decisionmaking than merely using classical probability theory (CPT). In this paper, we examine the HMI from a QPT perspective. Applying QPT to studying HMI for decision-making shows improvement in understanding the decision process when interacting with machines because it provides insights into the mental uncertainty of a human that is not apparent in CPT. Collapse

Topics: web page decision making search engine human computer interaction usability

Methods: case study experiment

Theories: probability theory decision theory

When Constructs Become Obsolete: A Systematic Approach to Evaluating and Updating Constructs for Information Systems Research

2022 | Management Information Systems Quarterly | Citations: 10

Authors: Compeau, Deborah; Correia, John; Thatcher, Jason

Abstract: In this paper, we confront a paradox in the IS literature that even though our f ... Expand

Abstract: In this paper, we confront a paradox in the IS literature that even though our field focuses on the rapid pace of technological change and the dramatic scale of technology-enabled organizational and societal changes, we sometimes find ourselves studying these changes using—largely without question—constructs that were developed in a vastly different IT, user, and organizational environment. We provide guidelines to help assess whether an existing construct warrants updating and to structure the updating task if it is undertaken. Our three-step process provides for a theoretically grounded and comprehensive method that ensures we balance the need for construct updating against the need to sustain our cumulative tradition. We illustrate our guidelines using computer self-efficacy (CSE) as a case study. We document each of the steps involved in analyzing, reconceptualizing, and testing the revised construct information technology self-efficacy (ITSE). Our analyses show that the new construct better explains both traditional and contemporary constructs with a traditional (postal survey) and contemporary (online panel) sample. We discuss the implications of our work both for research on self-efficacy and more broadly for future updating of other important constructs. Collapse

Topics: self efficacy information system use anonymity information privacy concern technological change

Methods: survey case study survey design theory development descriptive statistic

Theories: identity theory measurement theory social cognitive theory

Soft but Strong: Software-Based Innovation and Product Differentiation in the IT Hardware Industry

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Kim, Keongtae; Lee, Jeongsik “Jay”; Gopal, Anandasivam

Abstract: It has been argued in recent years that software is a significant value-creating ... Expand

Abstract: It has been argued in recent years that software is a significant value-creating factor within the manufacturing sector, leading to an increased interest in understanding the mechanisms by which firms may benefit from software-based innovation. Empirical work in evaluating these mechanisms, however, remains underdeveloped. In this paper, we examine one such process through which software-based innovation may provide value for information technology hardware firms in the U.S. Specifically, we examine the influence of software patents on the extent to which firms are able to benefit from product differentiation in their product markets. Using data on over 380,000 patent grants for innovations filed by 730 public IT hardware firms over the time period 1996-2015, we find that greater levels of software-based innovation within the firm are associated with higher levels of product differentiation in product markets: increases in the intensity of software patents within the firm’s patent stock, which we refer to as software intensity, are associated with lower total similarity of product offerings, relative to those offered by rivals, as well as fewer effective competitors in the market. Furthermore, using a different dataset consisting of 23,000 new IT hardware product announcements, we show that firms with greater software intensity subsequently launch a higher number of new products. Moreover, these firms are more likely to launch products that are not only differentiated from those of rivals but also distinct from their own product lines. Our research contributes to the emerging literature on software-based innovation by substantiating an important mechanism through which software helps transform industry sectors, specifically through product differentiation. Collapse

Topics: innovation management computer hardware product differentiation software patent digital innovation

Methods: topic model term frequency–inverse document frequency longitudinal research robustness check difference in differences

Career Path Clustering via Sequential Job Embedding and Mixture Markov Models

2022 | International Conference on Information Systems | Citations: 0

Authors: Zhong, Hao; Liu, Chuanren

Abstract: Extracting typical career paths from largescale and unstructured talent profile ... Expand

Abstract: Extracting typical career paths from largescale and unstructured talent profiles has re cently attracted increasing research attention. However, various challenges arise in effec tively analyzing selfreported career records. Inspired by recent advances in neural net works and embedding models, we develop a novel career path clustering approach with two major components. First, we formulate an embedded Markov framework to learn job embeddings from longitudinal career records and further use them to compute dynamic embeddings of career paths. Second, to cope with heterogeneous career path clusters, we estimate a mixture of Markov models to optimize clusterwise job embeddings with a prior embedded space shared by multiple clusters. We conduct extensive experiments with our framework to investigate its algorithmic performance and extract meaningful patterns of career paths in the information technology (IT) industry. The results show that our ap proach can naturally discover distinct career path clusters and reveal valuable insights. Collapse

Topics: IT career cloud computing IT workforce computer hardware implicit knowledge

Methods: cluster analysis Markov model experiment computational algorithm data transformation

Firm Profiling and Competition Assessment via Heterogeneous Occupation Network

2022 | International Conference on Information Systems | Citations: 0

Authors: Zhong, Hao; Liu, Chuanren

Abstract: Extensive efforts have been made by both academics and practitioners to underst ... Expand

Abstract: Extensive efforts have been made by both academics and practitioners to understand inter firm competitive relationship. However, it has never been an easy task to fully character ize firms and assess their competitive relationship owing to the challenge of information heterogeneity. In this regard, we propose a novel IT artifact for firm profiling and inter firm competition assessment guided by Information System Design Theory (ISDT). We start by constructing a Heterogeneous Occupation Network (HON) using employees' oc cupation details and education attainments. Then we adopt a Methpath2Vecbased het erogeneous network embedding model to learn firms' latent profiles (embeddings). Using firm embeddings as input, we train multiple classifiers to assess the competitive relation ship among the firms. We demonstrate the utility of our IT artifact with extensive experi mental study and indepth discussions. Our study also reveals that employees' occupation and education information significantly contribute to the identification of the focal firm's potential competitors. Collapse

Topics: human resource management LinkedIn database system computer software information retrieval

Methods: design artifact machine learning experiment kernel theory design science

Blessing or Curse: Impact of Algorithmic Trading Bots Invasion of the Cryptocurrency Market

2022 | International Conference on Information Systems | Citations: 0

Authors: cho, Kanghyun; Nam, Kihwan; Jung, JaeHwuen

Abstract: In this paper, we investigate the impact of the absence of trading bots on huma ... Expand

Abstract: In this paper, we investigate the impact of the absence of trading bots on human traders' investment returns. Using comprehensive data set obtained from a large cryptocurrency exchange platform, we find that trading bots play a market-making role, and they boost human traders' investment returns. We use the natural experiment setting that transforms a heterogenous market co-created with trading bots and human traders into a human-only financial market for empirical design. This paper extends the traditional investment decision under uncertainty by considering human attitudes toward algorithms while providing significant contributions to policymakers and regulators by providing empirical evidence on trading bots. Collapse

Topics: application programming interface cryptocurrency electronic trading individual characteristic computer software

Methods: experimental group natural experiment propensity score matching robustness check computational algorithm

Theories: transaction cost economics

Digital valuation of personality using personal tokens

2022 | Electronic Markets | Citations: 1

Authors: Marchewka-Bartkowiak, Kamilla; Nowak, Karolina Anna; Litwiński, Michał

Abstract: Attempts to valuate personality and personal brand in the digital economy face s ... Expand

Abstract: Attempts to valuate personality and personal brand in the digital economy face significant problems when it comes to the tools of such an activity. The main aim of this paper is to consider a personal token as the new means of personality valuation and to propose tools of its financial and network valuation. We applied financial analysis, content analysis, and statistical analysis, using data from the platform dedicated to personal token issuers. Financial valuation of personal tokens was performed using a modified Network Value to Transaction ratio (NVT). The personality types of the tokenised persons were determined based on Bartle’s Four Player Types model and the Big Five Personality Traits model. The issuers of personal tokens were grouped with a use of clustering analysis and the differences in score on personality traits were tested accordingly. Our results uncovered the novel means of personality and personal brand valuation which is a personal token and allowed us to differentiate two groups of personal token issuers based on their personality traits and activity profile. Collapse

Topics: personality network effect Twitter cryptoasset strategic management

Methods: cluster analysis qualitative content analysis narrative analysis hierarchical clustering complete linkage hierarchical clustering

Theories: big five model