Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0260 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0260 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

While there is a rich body of literature on information system (IS) innovations, there is a limited understanding of the role IS leaders’ individual factors and their appraisals of technological factors play in organizations’ adoption of IS innovations. We address these gaps in the IS literature by focusing on an IS process innovation – namely, computer-aided software engineering (CASE) – which is targeted at the core activities of systems development/maintenance in IS departments. We specifically examine how organizations’ CASE adoption decision is impacted by (1) two individual factors of IS leaders (i.e., leaders’ hierarchical position and job tenure) and (2) their perceptions of technological factors (i.e., relative advantage and technological complexity of CASE). Data were gathered from IS leaders at 350 organizations in the United States using a national cross-sectional survey. The findings suggest that IS leaders’ hierarchical position and their job tenure significantly differentiate CASE adopters from non-adopters. IS leaders at lower levels of the organizational hierarchy and with shorter job tenure made the adoption decisions in adopter organizations, while IS leaders at higher levels of the organizational hierarchy and with longer job tenure made the adoption decisions in non-adopter organizations. The findings also reveal that relative advantage has two dimensions – namely, perceived efficacy advantage and perceived efficiency advantage – and IS leaders’ evaluation of the perceived efficacy advantage of CASE differentiates adopters from non-adopters. The study has important implications for our theoretical and practical understanding of the factors related to IS leaders that are influential in the organizational adoption of IS innovations.

Improving the management of information technology (IT) projects is of prime concern to both IS researchers and practitioners, as IT projects are notorious for poor process performance, frequently running over budget and behind schedule. Over the years, at least two separate streams of research have emerged with the aim of contributing to our understanding of IT project management. One of these focuses on the exercise of formal and informal controls, while another focuses on identifying and managing key risks such as those associated with requirements and users. Proponents of the control stream would argue that the exercise of formal and informal controls can improve process performance and there is some evidence that this is so. An obvious question that emerges, however, is how effective these controls are in the presence of particular risks. In this study, we seek to answer this question by developing and testing a research model that integrates these two streams of research. On the basis of data collected from 63 completed IT projects in China, we examine the moderating effects of requirements risk and user risk on the relationship between control (both formal and informal) and the process performance of IT projects. We contribute to the current state of knowledge by clearly demonstrating that both types of risk moderate the effects of formal and informal controls on performance. Specifically, both requirements risk and user risk were found to reduce the positive influence of controls on process performance, implying that implementing solid controls is a necessary, but not sufficient, condition to ensure good process performance.

Vendors of IT-enabled services must address equivocal and changing requirements from diverse customers while simultaneously making a profit. However, our knowledge of how these organizations can achieve the necessary scalability is limited. Against this backdrop, we leverage organizational sensemaking to investigate how a large vendor attempted to create a scalable service infrastructure through three sequential strategies. This in-depth case study reveals key factors that challenged the efficacy of each strategy. First, addressing equivocality through structural separation exacerbated the organization’s challenges because of misaligned collective identities between business units. Second, reducing equivocality through market segmentation proved to be inadequate because individual-level cognitive constraints shaped pre-packaged solutions that lacked functionality. Third, responding to equivocality through service modularization was challenged due to lack of social interaction about standardization of component interfaces, system and process redundancies, and inflexible process architectures. We offer a detailed analysis of these strategies and discuss implications in relation to theory and practice.

OBJECTIVE: This work seeks to complement and extend prior work by using a multidisciplinary approach to explain electronic medical records (EMR) system use and consequent performance (here, patient satisfaction) among physicians during early stages of the implementation of an EMR. DESIGN: This was a quantitative study, with data obtained from three distinct sources: individual-level and social-network data from employees; use data from EMR system logs; and patient satisfaction data from patients and/or authorized decision-makers. Responses were obtained from 151 physicians and 8440 patient satisfaction surveys over the course of a 1-year period at the shakedown phase of an EMR system implementation. RESULTS: Physicians who were better connected, both directly and indirectly, to their peers-that is, other physicians-for advice on their work, used the system less than those who were less connected. In addition to such social network ties, demographic characteristics (gender and age), three personality characteristics (openness to experience, agreeableness and extroversion) and a key technology perception (perceived usefulness) predicted EMR system use. CONCLUSIONS: For hospital administrators and other stakeholders, understanding the contributors to, and the relative importance of, various factors in explaining EMR system use, and its impact on patient satisfaction is of great importance. The factors identified in this work that influence a physician's use of EMR systems can be used to develop interventions and applications that can increase physician buy-in and use of EMR systems.

The focus of this paper is on flexibility in a “query-answering” system which includes the user who generates the queries as well as other components of the system responsible for interpreting and fulfilling the query. Furthermore, the emphasis is expanded from pure “queries” which can be satisfied by information alone to generalized queries which may be satisfied by some combination of information, services, and physical goods not initially known in detail by the user. In order to carry out these comparisons and contrasts in a manner that uncovers potentially useful implications for internet infrastructure providers, consumer oriented enterprises, and business-to-business enterprises, we will use an analytic approach based on the post-structural theories of Michel Foucault. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.