This research examines when and why lean maturity can generate either synergy or tension in the management of automation adoption. It decodes how firms can effectively manage and align the interaction between lean (or its absence) and automation. This research relies on a qualitative multiple-case study. Drawing on a sensemaking perspective, we analysed eight cases, split between lean and non-lean firms, each with annual revenues exceeding €100 million and implementing comparable robotics automation projects. Using six-stage template analysis, we identified patterns across three phases of sensemaking. The research reveals the differences between lean and non-lean firms in their automation journey by developing a process model in scanning (pre-automation), in interpreting (automation), and in responding (post-automation) stages of sensemaking. Depending on the objective targeted by the firm, lean maturity can serve as synergy or tension towards it. The research provides a framework for depicting the strategic fit between managerial approaches and automation objectives.

The nuanced understanding of process that firms in the B2B setting undergo for VCC and the role different types of technologies play in that process is missing. We adopted a research design that comprised of four sequential phases to address this gap. In first phase, we conducted a systematic review of literature. In second phase, we deductively developed an integrated 3 x 3 framework illustrating the usage of different types of technologies across the three stages of VCC. In third phase, we conducted semi-structured interviews with 25 practitioners to validate the framework. Finally, in fourth phase, we developed a process model to explain how digital technology can be leveraged in each step of VCC. The process model in the backdrop of integrated framework is novel as it codifies the individual activities of VCC and brings out the nuances involved at the interaction of the activities and stages. The findings can catalyse B2B VCC at a higher speed by establishing faster feedback loops through the integration of digital technologies in each of the process steps. The gaps in research and practice on how businesses co-creating value can iterate to get a testable product with a measurable outcome while leveraging digital technologies are discussed.

Customer churn is a commonly found problem in most businesses. Yet, it is not well studied in sharing economy businesses, due largely to difficulty in observing customer attrition across different customer segments. To better address customer churn—and so the enhancement of sustainable urban mobility under diverse user behavior and service engagement patterns—six data-driven approaches, with and without data balancing techniques (Synthetic Minority Oversampling Technique, SMOTE), have been herein adopted and applied to a dataset from a car-sharing operator in Thailand. Our results indicate that, within specific user groups, certain algorithms excel without the need for a data balancing technique. In particular, the Transformer model without SMOTE performs best in predicting churn for one-time user groups, whereas the Artificial Neural Network (ANN) model without SMOTE and the Extreme Gradient Boosting (XGBoost) model exhibit the highest prediction performance for frequent and infrequent users, respectively. We also find that important features influencing churn tend to vary greatly across different customer segments, underscoring the necessity for churn retention strategies tailored to specific segments. In this regard, financial and service engagements are highly correlated with churn, implying that customers with better engagement are less likely to churn, which is expected in a sharing economy business.

Purpose Anchored in the Natural Resource-Based View (NRBV) and Dynamic Capabilities theory, this study examines blockchain’s role in facilitating firms' transition to a circular economy (CE), aiming to provide a robust framework for understanding the interplay between blockchain, CE and sustainability. Design/methodology/approach The study uses an interpretivistic approach and semi-structured interviews to explore how blockchain can drive the transition to a CE. Findings The study shows blockchain can expedite the shift to a CE through pollution prevention, product stewardship and sustainable development, by leveraging dynamic capabilities (DC). It emphasizes blockchain as a micro foundation of DCs, with these capabilities enabling NRBV strategic capabilities. Originality/value This study investigates the intersection of blockchain and CE, offering empirical validation for a robust conceptual framework and revealing the societal impact of the CE transition.

Purpose Smart farming (SF) holds immense potential in making farming viable and improving farmers' livelihoods. However, its adoption is still in the early stages, and the resulting impacts are underexplored. This study investigates the information systems/policy and implementation barriers to the adoption of SF and its implications for various socioeconomic aspects. Design/methodology/approach This survey research used a structured questionnaire to collect data on farm details, farmer characteristics and usage of SF technologies from a sample of 197 farmers based in the State of Karnataka, India. Exploratory factor analysis with principal component extraction is used to validate the proposed questionnaire constructs. Results are analyzed using one-way and two-way ANOVAs and interaction plots. Findings Our study throws light on the benefits and barriers of SF technologies. Advanced SF technologies displayed a significant positive effect on socioeconomic variables compared to startup SF technologies, which displayed no significant effect. Overcoming information systems/policy barriers for adopting startup and advanced SF technologies displayed significant positive effects on farmers' health and net income, whereas overcoming implementation barriers for adopting advanced SF technologies led to improvement in the farmers' gross income. Originality/value Although the technical feasibility of SF has been explored in the literature, its adoption barriers and socioeconomic impacts have been underexplored. To the best of our knowledge, the interaction between different types of barriers and the level of SF adoption has not yet been investigated in the literature. Our study addresses those research gaps using data from a previously underexplored context, viz. developing nations.

Agriculture financing in developing countries is dominated by informal lending. One challenge in the expansion of institutional (formal) credit is the lack of reliable data on the historical performance of farmers. Due to the absence of data, financial institutions face uncertainties that obstruct the decision-making process, leading to sub-optimal credit disbursal. Based on the theoretical lens of uncertainty reduction, this study focuses on achieving two key research objectives: identifying uncertainties in institutional crop credit management processes and examining how a data-driven digital transformation for social innovation based on satellite imagery analytics could alleviate these hindrances. We longitudinally study a satellite imagery analytics firm and complement the case data with stakeholder interviews. The results capture state space, option, and ethical uncertainties institutional lenders face in expanding crop credit and explain how data-driven digital transformation can reduce these uncertainties. Adopting such a data-driven digital transformation promises to make different stakeholder groups interact and collaborate to achieve the common objective of financial inclusion of small-scale economic actors. Further, we show that satellite imagery in crop credit management can significantly reduce the uncertainties caused by the lack of independent data sources.

The diversion of manufacturers’ branded products outside the authorized channel leads to the emergence of gray markets (GMs), which is legal under the doctrine of first sale. Product diversion to GMs can negatively affect manufacturers’ brand reputation. In this study, we analyze a manufacturer’s strategic channel choice decision and its implications for social welfare in the presence of GMs and strategic consumers. We present scenarios in which the presence of GMs can either have a positive or negative impact on the manufacturer and the supply chain. Our findings indicate that the manufacturer and the supply chain become worse off due to product diversion to GMs under lower channel differentiation and high penalty cost for the loss of brand reputation due to product diversion. This effect is more severe for a decentralized supply chain, where product diversion does not directly affect the retailer’s profitability due to the erosion of brand reputation. In such scenarios, we suggest that manufacturers should follow a brand-protection strategy to curb product diversion to GMs. However, manufacturers selling product categories with low brand equity can follow a market-expansion strategy. In such cases, the presence of GMs provides an alternative channel to expand sales by capturing untapped consumer segments without incurring additional market search or advertising costs. This strategy also facilitates indirect price discrimination among consumers. We find that social welfare is higher when channel differentiation is low under a decentralized supply chain.

In this research, we demonstrate how a qualitative assessment based on benchmarking approach can be carried out to evaluate the effectiveness of lean implementation in a hospital. A single case study approach is used for primary data collection from an Indian hospital. Secondary data are gathered on two best-in-class benchmarking partners, identified for their reputation in lean implementation. Lean practices and performance measures adopted in the case hospital are compared with the benchmarking partners using Xerox's benchmarking model. Our results show that most of the lean practices implemented in the case hospital are low-hanging, and the scope exists for improving its lean maturity by learning from the benchmarking partners. The performance measures are highly contextualised to individual hospitals, although the waste to be reduced is standard across the healthcare sector. The demonstrated assessment approach can guide practitioners in understanding how well their hospital has implemented lean compared to benchmarking partners and where they have scope for attaining further lean maturity.

The article seeks to develop a process model to guide research and practice in the effective integration of robotics for pick-and-place activities in manufacturing firms, where lean management tools and practices are being embraced. Utilizing a multiple case study analysis, the researchers conducted on-site visits to production facilities and analyzed 16 diverse projects, 11 coming from manufacturing companies and 5 projects provided by System Integrators, to gain heterogeneous and wide-spanning insights. The unit of analysis is the single robotic implementation, spanning across various sectors to extract patterns independently from the specific industry. These projects yielded a substantial volume of information, knowledge, and best practices related to the adoption of Robotics. Following a meticulous examination of the case studies, a process model was formulated to guide companies through decision-making, implementation, monitoring, and sustain stages in robotics introduction projects. This research disentangles the influence of lean management in ensuring the optimization of benefits derived from such projects. The process model seeks to offer practical guidance for companies approaching the complexities of robotics integration within manufacturing processes, successfully filling in the pre-existing research gap.

Healthcare 3D printing (3DP) is in nascent stage and some service providers are facilitating its adoption across hospitals in the world. There is limited research on healthcare 3DP services, the business models of such service providers, the value they provide to the surgeons and the hospitals and the co-creation process needed to deliver such service innovation. The objective of this research is to identify the value proposition, value creation, value capture and value provided to users by the healthcare 3DP service providers and to identify the resources and capabilities needed by the healthcare 3DP service providers and the clinical team to co-create value. Interviews are conducted with seven healthcare 3DP service providers, three surgeons, and a healthcare 3DP design expert along with secondary data collected from the service providers to answer the research questions. The results show that healthcare 3DP service providers utilise their exploitative capabilities while the surgeons used both explorative and exploitative capabilities to engage in the co-creation process and to create perceived value for patients. The findings also show that integrated healthcare 3DP service providers by providing knowledge, expertise, insights and training to the surgical teams are better suited to improve the absorptive capacity of hospitals to adopt 3D Printing than specialised implant developers.

Health care is a complex system that demands critical decision making, especially in the diagnosis of various conditions in patients. To minimize possible errors in diagnosis, an emerging technology, machine learning (ML), is being effectively used. ML classifiers can be used to proactively diagnose the medical conditions, which are identified based on the presence or absence of specific characteristics of the diseases. Therefore, in this article, we demonstrate how ML can be used to determine Parkinson's disease (PD) and thereby, provide early diagnosis using nonclinical data of the patients. Novel ensembles are developed in this article to improve the diagnostic capability and the experimental results show that the improved versions of artificial neural network (ANN) could yield 13.4% more accurate results compared with the traditional ANN classifier. PD is considered a challenging medical condition, owing to its global relevance and complexity in diagnosis. Moreover, the early detection of PD is instrumental for patient recovery, and any lapses in diagnosis can lead to an immeasurable loss to patients. Also, the study has developed an effective diagnostic tool for PD and detects the disease at an early stage using the voice data of individuals, and this will aid in making better clinical decisions related to PD, thus rendering better health services.

Over half of India's employment is attached to the agriculture sector and their survival is dependent on the performance of farms. The uncertainty in the performance of farms due to weather fluctuations and other risks is tackled by providing insurance cover. However, policymakers’ choice of administrative measures for estimating crop loss has resulted in inaccurate data collection, opened vulnerability to the politicization of the process, and created bottlenecks to operate at scale. These problems have led to skewed timelines for data collation, lack of confidence in the data produced by the agri-insurance providers, and caused long-drawn delays in settling claims made by farmers. In this article, we present a case study on the assessment of using satellite big data as a technology deployed in Northern India to solve the aforementioned problems between the stakeholders in the agri-insurance claim settlement process. Satellite big data based analytics provides an independent data source and decision-making platform for the agri-insurers to conduct an assessment for calculating the indemnity payments. The results showcase how transparency brought in by the satellite big data analytics curbs the plausible exploitation of the claim settlement process and leads to increased efficiency and efficacy in settling farmer claims.

In this article, we examine how collective creative self-efficacy (CCSE) of a team can act as a competency indicator for team creativity output (TCO) in knowledge-intensive SMEs. As a team’s creative efficacy shape the collective mental model about its social context, team climate of creativity is considered as a mediator in the relationship between CCSE and TCO. Through faultline-strength analysis, we investigate how team members’ compositional attributes (age and job tenure) moderate the relationship between CCSE and team climate. A High sub-group separation (age and job tenure attributes) of team members is beneficial in a high CCSE team, whereas a homogeneity in age and tenure is desirable when a team’s CCSE is low. Our results show group faultline-strength can significantly strengthen or dampen the existing team climate and team creativity output within SMEs, thus creating a strong basis for firm owners or managers to align teams for improved team output. Moreover, HRs in such firms can design interventions to measure and enhance teams’collective creative self-efficacy of a team that serve two purposes—a) act as a competency indicator that guides a team to become self-directed, and, b) strengthen the team creativity climate for producing creative deliverables.

Healthcare institutions have been working to improve the efficiency and effectiveness of the service delivered. The literature has argued that their capabilities have a direct effect on service outcomes. Research has explained how their capabilities can be enhanced by implementing high-performance work practices (HPWP) bundles and how these bundles can impact performance through relational coordination. However, this previous research has focused primarily on single-specialty healthcare institutions in a developed country. Inherent characteristics of multispecialty healthcare institutions (e.g., inability to standardize) and emerging economy context (e.g., absence of case manager role) motivate further investigation in this setting. Therefore, in our research, we study the impact of HPWP on the overall performance, efficiency, and effectiveness of healthcare service delivered and how this linkage is moderated by relational coordination. We analyzed 605 valid responses from different healthcare institutions located in the southern Tamil Nadu state of India using structural equation modeling. In alignment with past research, our results show that HPWP improves the overall performance and effectiveness and this linkage is moderated by relational coordination. However, HPWP's impact on efficiency and its moderation by relational coordination is insignificant. We explain the results by anchoring them to the characteristics of the multispecialty and emergingeconomy context.

PurposeThe purpose of this research is to investigate the contingent adoption of Additive Manufacturing (AM) and propose a typology to evaluate its adoption viability within a firm's supply chain.Design/methodology/approachBy conducting semi-structured interviews of practitioners with deep knowledge of AM and supply chains from diverse industries, this research explores the contingent factors influencing AM adoption and their interaction.FindingsWhile the AM literature is growing, there is a lack of research investigating how contingent factors influence AM adoption. By reviewing the extant literature on the benefits and barriers of AM, we explain the underlying contingencies that enact them. Further, we use an exploratory approach to validate and uncover underexplored contingent factors that influence AM adoption and group them into technological, organizational and strategic factors. By anchoring to a selected set of contingent factors, a typological framework is developed to explain when and how AM is a viable option.Research limitations/implicationsThis study focuses on specific industries such as automotive, machine manufacturing, aerospace and defense. Scholars are encouraged to explore the contextual factors affecting AM adoption in particular industries to expand our findings. The authors also acknowledge that the robustness of their framework can be enhanced by integrating the remaining contingent factors.Practical implicationsThe developed typological framework provides a pathway for practitioners to see how and when AM can be useful in their supply chains.Originality/valueThis is the first paper in the supply chain management literature to synthesize contingent factors and identify some overlooked factors for AM adoption. The research is also unique in explaining the interaction among selected factors to provide a typological framework for AM adoption. This research provides novel insights for managers to understand when and where to adopt AM and the key contingent factors involved in AM adoption.

Information and communication technologies (ICTs) are known for supporting healthcare services in dealing with adverse situations. However, little is known on the contribution of ICTs in a prolonged crisis involving a new disease, such as the COVID-19 pandemic. In this study, we carry out an exploratory investigation of which ICTs contribute the most to the emergency care of patients diagnosed with COVID-19 according to healthcare technology experts and how physicians perceive these contributions. Initially, we applied an online survey to 109 healthcare technology experts. Then, we conducted 16 in-depth follow-up interviews with emergency medicine professionals from 10 countries to identify the ICTs contributing the most to treat COVID-19 patients. Results from the survey indicated four ICTs as the most useful to support the treatment of COVID-19 patients; they are remote consultations, digital platforms for data sharing, digital non-invasive care, and interconnected medical decision support. The interviews provided insight into the applicability of those ICTs for the studied context. The four main ICTs were also found to be logically compatible with the complexity of the pandemic, reducing undesirable complexity attributes (e.g. physical proximity between caregivers and infected patients) and amplifying desirable ones (e.g. interactions that support collaborative work and knowledge sharing).

The ongoing competition between traditional vehicle manufacturers and technology companies for quickly developing autonomous vehicles (AVs) and gaining early traction in the market is well known. However, some issues need to be cleared regarding the antecedents of the behavioral intention to use AVs. In this context, we conducted a meta-analysis using the TIS (Technological, Individual, and Security) framework, to understand the convergence and divergence of the factors influencing the behavioral intention to use AV technology. This meta-analysis tested the hypotheses using a database of 65 studies obtained from 58 articles with the cumulative sample size of 37,076. The study identified perceived usefulness, attitude, trust, safety, hedonic motivation, and social influence as the critical antecedents of AV adoption. Several of the relationships investigated in the study were moderated by factors such as level of automation, vehicle ownership and culture. The results revealed fewer incentives for the public to accept AVs. Theoretical contributions and recommendations to practitioners and policymakers have also been discussed.

Regardless of increased attention in electric vehicles (EV) market expansion, the actual pene-tration of EVs remains low globally. Almost all major OEMs have announced investment plans to ensure that EVs constitute a major, if not complete, chunk of their product portfolios. On their part, governments worldwide (e.g., China, Poland, India, USA, etc.) have used various policy measures to facilitate EV adoption. In this paper, we study how incentives offered in terms of subsidy and differential taxation schemes could increase the market penetration of EVs. We analyze different models under uniform and differential taxation policies with and without subsidy, using a non-cooperative game-theoretic approach. Our analysis reveals that the gov-ernment can follow any of the three tax-subsidy mixes that could maximize social welfare, i.e., differential taxation with and without subsidy, and identical tax with a subsidy. Surprisingly, the manufacturer's profit, the government's income, and consumer surplus for these three models are also the same and are better than the other two models depending on the consumer's green sensitivity, i.e., for higher green sensitivity, these three models can provide a win-win outcome. From an environmental perspective, levying tax on gasoline vehicles (GV) without subsidy to the manufacturer minimizes the overall environmental impact. In contrast, levying the same tax for both types of vehicles without subsidy to the manufacturer generates the maximum overall environmental impact. Furthermore, an increase in the unit environmental impact of vehicles attracts higher taxes. We portray that the increase in the cost-difference between EV and GV increases GV demand and is detrimental for EV acceptance. In addition, multifaceted insights are drawn for manufacturers and policymakers to envisage electric mobility. We extend our models and show that our main results hold under the implementation of mandate on EV manufacturers under subsidy and non-subsidy model, and inclusion of hassle cost for consumers due to lack of infrastructure in terms of charging facilities and maintenance.

In this study, we identify bundles of technologies and associated implementation barriers that could be viewed as part of Healthcare 4.0 (H4.0) and test their impact on performance improvement in a sample of hospitals. For that, we carried out a cross-sectional study with 181 leaders from hospitals in different countries that have already started H4.0 implementation. The collected data were analyzed using multivariate statistical techniques. Results indicate that H4.0 technologies could be organized into two different bundles according to their role within the hospital. Common barriers to H4.0 implementation were also empirically organized in two groups, following the sociotechnical systems theory. Bundles of H4.0 technologies presented a positive and significant effect on hospitals' performance. As their interaction with H4.0 barriers displayed a significant effect on performance improvement, it is important to concurrently consider H4.0 technologies and barriers. Our results allow hospital managers to anticipate potential issues in H4.0 implementation, enabling more assertive efforts to improve performance and deliver high-quality and low-cost care in the fourth industrial revolution era.

Senior executives must make strategic decisions on (re)configuring global value chains (GVCs) in a post-COVID19 world, with digital technologies playing a decisive role in enabling decision-making on both the reconfigurations and their implementation. Against this backdrop, the paper explores in depth how executives can leverage and combine big and small data analytics into their GVC (re-)configuration decisions. We draw on a longitudinal single-case study of an analytics firm supporting decision makers in agri-food GVCs, enriched through multiple interviews with experts from the industry. Our analysis reveals an interesting shift towards hybrid data strategies combining big and small data to arrive at new forms of decision-making processes after the outbreak of COVID-19. Through this hybridization, executives aim to improve their understanding of GVCs as well as their own agility and flexibility in decision-making to ensure GVC resilience and efficiency.