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Business model renewal is a key consideration for organizations, and AI (artificial intelligence) has been identified as a significant potential enabler for that renewal. However, while there are examples of emerging organizations using AI as a key basis of competitive advantage as well as examples of established organizations trialing AI technologies, there are relatively few examples of established organizations fundamentally transforming their business models through the use of AI. Through case studies underpinned by interviews with named executives of ten organizations and complemented by an applicability check involving 14 executives, advisors and practice-oriented academics, this paper presents an empirically supported set of factors linked to successful AI-enabled business model transformation as well as a model of interactions between these factors. Using a horizontal contrasting approach to articulate the difference between empirical findings and a literature based model, this paper moves from the language of potentially passive top management support towards the concept of proactive leadership and introduces tech-sensitive innovation culture, AI-sensitive risk tolerance and strategic process discipline into the dynamic capability lexicon. The insights of this paper can be used by managers to assess their readiness to move beyond traditional ‘proof-points’ and successfully undertake and accelerate AI-enabled business model transformation.

Megaprojects and specifically ‘green’ construction of residential megaprojects can contain significant risks of failure. To design proper risk mitigation strategies, after identifying key risk factors, the next step is to conduct assessments that would facilitate the process of risk element prioritization. Risk assessment comprises the establishment of factor interrelation and discerning the indicators of importance. This research proposes a novel version of an integrated prioritization method and analyzes twelve all-inclusive key supply chain oriented risk factors identified in a previous study. Through a comprehensive literature review three criteria, impact, probability, and manageability are selected. Also, a fourth criterion namely influence rate is included in the model, based on the driving powers that can also be derived from the Interpretive Structural Modeling’s (ISM) assessment. Fundamentally, the calculations hinge on the Analytic Network Process (ANP) method which provides an assessment of the alternatives’ weights based on pairwise comparisons concerning the criteria specified. To enhance the accuracy of the perceptive judgments of the expert panelists, a bell-shaped fuzzy function is used to convert the verbal statements to crisp values. In addition, Row Sensitivity Analysis is administered to check the stability of the results and provide predictive scenarios. To validate the model, a case study, located in Iran, was conducted, where an expert panel consisting of four individuals made the pair-wise comparisons through an ANP questionnaire. Results indicate priority and sensitivity of the alternatives concerning criteria, for the case under study.

In this paper, we explore why users’ experiences with emerging supply chain technologies comprise inflated expectations followed by disappointment in the early stages of adoption, as per the Gartner Hype Cycle. We used “affordance theory” to study how managers perceive emerging technologies to explain their adoption experience. Affordance theory indicates that perceived benefits—and goals and constraints—depend on the interaction between technology and the users, not on the technology alone. First, we used the literature for two purposes: first, to obtain characteristics of blockchain, Internet of Things (IoT), and artificial intelligence (AI) as emerging technologies; and second, to itemize generic goals, affordances, and constraints in adopting any supply chain technology. Next, we asked 400+ supply chain managers to select those affordances, constraints, and goals that they viewed as pertinent to their organizations’ supply chains for whichever of these three technologies they were implementing. Finally, we compared the responses across technologies for individual respondents (who selected more than one technology) and within the pool of respondents. We found that respondents who selected more than one technology made distinct selections individually for the different technologies relevant to them. The pooled responses across all respondents, however, prioritized the aggregated goals, affordances, and constraints in the same way, regardless of the technology, the organization, or the network features of the supply chain. Overall, it appears that the characteristics of the technology do not inform user expectations at the early stages of adoption. This initial disconnect—between characteristics and expectation—may explain the “inflated expectations” followed by the early “trough of disappointment” with emerging technologies in the Gartner Hype Cycle, as users focus on obtaining the same benefits for the supply chain from any new emerging technology. Only subsequen

Over the years, Operations and Supply Chain Management (OSCM) researchers have continued to examine foundational topics, while also expanding and adding new ones. Given that the field has changed a great deal, it seems valuable to examine the evolution of OSCM's intellectual structure over its 70 years of academic communications. Using bibliometrics methods, we analyze 96,125 articles containing 1,047,468 references from 31 representative OSCM journals. Our study defines primary topic areas represented in the most co‐cited documents, while tracing past and current research trends. We include analyses of recent bursts in research attention and comparisons of trends in academic and practitioner literatures. The results from an inclusive analysis suggest that OSCM researchers have been responsive to methodological advances and to some of the dynamics in the operating environments of businesses. However, a product/manufacturing focus seems to persist, and inconsistencies between researchers’ and practitioners’ interests suggest that relevance of OSCM could be further improved. Current hot topics are quite different from those of the past. Trend data suggest that studies of supply chain management and data analytics issues will continue to figure largely in shaping the future research agenda. Our analysis quantifies trends and identifies subtopics that are growing in prominence. In addition, the findings provide a benchmark for evaluating the progress of the OSCM field, while highlighting opportunities for shaping its development and directions.

We propose a method to collect data on user requirements of medical equipment shared by different users and services, relate those requirements with the equipment's technical features, and rank the most important features to be considered when procuring the equipment, leading to more effective procurement decision making. Our method is structured in three phases: (i) elicit the device's technical characteristics, (ii) determine user requirements, and (iii) relate technical characteristics to user requirements. The method is applied to rank the most relevant features of a hospital recliner used by seven services and three types of users. Our results indicate the 10 most relevant factors (requirements) for an “ideal” hospital recliner with potential impact on outputs (importance scores). A final list of 32 items was useful for comparing user requirements and identifying key features that address the most relevant requirements. User‐centered approaches to requirements elicitation in medical equipment procurement promote healthcare benefits, safety, and end‐user satisfaction. Potential use of our approach goes beyond our application case study into many other categories of procurement decisions and into other industry and business applications, wherever multiple stakeholders' requirements should be considered in decisions with multiple value dimensions of value.