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The Duane reliability growth model has a number of inherent limitations that make it unsuitable for monitoring reliability improvement progress. These limitations are explored and a model based on variance‐stabilizing transformation theory is explained. This model retains the ease of use while also avoiding the disadvantages of the Duane model. It represents a more useful graphical model for portraying reliability improvement at development team meetings. Computer simulations have shown that the new model provides a better fit to the data over the range of Duane slopes normally observed during a reliability growth program. The instantaneous mean time between failures (MTBF) equation for the new model is developed. Computer simulations show that its use results in higher values of instantaneous MTBF than that achieved by the Duane model. The new model also reduces the total test time for achieving a particular specified instantaneous MTBF. Finally, software failure data from an actual project illustrates the calculations and benefits of the new model.

Business models can be considered as cognitive models that managers or analysts can use to describe, understand, or test business activities. However, the emergence of a new business model requires not only cognitive operations but also concrete modifications to the realities of a company’s operations and structures. In this paper, we adopt a sociomaterial view of organizational change based on actor-network theory, and underline the role of artifacts in the emergence of new business models. We base our discussion on a case study of a French leader in kitchen electric appliances. Despite the fact that the building of its new business model is still in progress, this empirical study provides important suggestions concerning the role of artifacts.

Remanufacturing is the only end-of-life (EOL) treatment process that results in as-new functional and aesthetic quality and warranty. However, applying mental model theory, the purpose of this paper is to argue that the conception of remanufacturing as an EOL process activates an operational mental model (OMM) that connects to resource reuse, environmental concern and cost savings and is thus opposed to a strategic mental model (SMM) that associates remanufacturing with quality improvements and potential price increases.

The authors support the argument by empirically assessing consumers’ multi-attribute decision process for cars with remanufactured or new engines among 202 car buyers in China. The authors conduct a conjoint analysis and use the results as input to simulate market shares for various markets on which these cars compete.

The results suggest that consumers on average attribute reduced utility to remanufactured engines, thus in line with the OMM. However, the authors identify a segment accounting for about 30 per cent of the market with preference for remanufactured engines. The fact that this segment has reduced environmental concern supports the SMM idea that remanufactured products can be bought for their quality.

A single-country (China) single-brand (Volkswagen) study is used to support the conceptualised mental models. While this strengthens the internal validity of the results, future research could improve the external validity by using more representative sampling in a wider array of empirical contexts. Moreover, future work could test the theory more explicitly.

By selling cars with remanufactured engines to customers with a SMM that values the at least equal performance of remanufactured products, firms can enhance their profit from remanufactured products. In addition, promoting SMM enables sustainable business models for the sharing economy.

As a community, the authors need to more effectively reflect on shaping mental models that disconnect remanufacturing from analogies that convey inferior quality and performance associations. Firms can overcome reduced utility perceptions not only by providing discounts, i.e. sharing the economic benefits of remanufacturing, but even more by increasing the warranty, thus sharing remanufacturing’s performance benefit and reducing consumers’ risk, a mechanism widely acknowledged in product diffusion but neglected in remanufacturing so far.

Extreme value model is one of the most important models that are applicable in air pollution data. This paper aims at introducing a new model of extreme value that is more suitable in environmental studies.

The parameters of the new model have been estimated by method of maximum likelihood. In order to relate to air pollution impacts, the new extreme value model was used, applied to carbon monoxide (CO) in parts per million (ppm) at several places in Malaysia. The objective of this analysis is to fit the extreme values with a new model and to examine its performance. Comparison of the new model with others is shown to illustrate the applicability of this new model.

The results show that the new model is the best fit using the method of maximum likelihood. The new model gives a significant impact of CO data, which gives the smallest standard error and p‐values. The new extreme value model is able to identify significantly problems of air pollution. The results presented by the new extreme value model can be used as an air quality management tool by providing the decision makers means to determine the required reduction of source.

The new extreme value model has mostly been applied in environmental studies for the statistical treatment of air pollution. The results of the numerical and simulated CO data indicate that the new model both is easy to use and can achieve even higher accuracy compared with other models.

The purpose of this paper is to introduce the new class ratio dispersion, the new smooth degree sequence and the comparison criterion of the new smooth degree and to propose the new prior check of grey modeling in order to meet the modeling demand of the optimized grey models which have the white exponential law of coincidence.

This paper introduces the corresponding new concepts and new comparison criterion which can reflect the approach degree of the raw data and the normal geometric progression by analogy with the traditional class ratio dispersion, smooth degree sequence and comparison criterion.

To the optimized grey models, the new concepts and the new comparison criterion can be regarded as the prior check of grey modeling.

First, the new concepts and the new comparison criterion can reflect the approach degree of the raw data and the normal geometric progression, and this paper proposes the prior check of grey modeling to the optimized grey models. Second, this paper proposes the quantificational valuation criterion – the concept of the smooth degree which can reflect the approach degree of a single sequence and the normal geometric progression, and ends the status quo that there is only the comparison criterion of the smooth degree between two sequences but not the quantificational valuation criterion of a single sequence.

The constructs of re-deployment and co-deployment have been central to discussions of scope economies in diversified firms. We argue however that these constructs are also significant in the context of single-business firms. Increasingly, changes in technology and demand preferences have provided opportunities for entrants to attack incumbents with a different business model, one that may neutralize the incumbent’s advantage for at least some set of customers (e.g., Netflix vs. Blockbuster). In such a context incumbents often respond by modifying their business model. We note that several of the business model-altering responses of the incumbent can be characterized in terms of co-deployment and re-deployment benefits and costs, where co-deployment benefits/cost apply to the scope economies/diseconomies in running multiple business models within the same firm and re-deployment benefits/costs apply to the implications of moving assets from one business model to another. We then examine the set of strategic choices faced by the incumbent in competing with an entrant with a different business model. We identify five set of factors that are likely to influence the decision to choose between these alternatives – uncertainty spawned by the new business model, market segment targeted by the new model, the within-business-across-business-model co-deployment and re-deployment benefits and costs, the across-business co-deployment and re-deployment benefits and costs, and the incumbent’s prior performance history. Although some of these choices have seen some work, most remain relatively underexplored in the strategy literature. We highlight the potential for research in this area with a set of propositions that identify key conditions that should hold true for a particular strategic choice to be picked by an incumbent.

3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

The purpose of this paper is to develop a new conceptual framework that reflects network dynamics in Internet of Things (IoT)-enabled service innovation processes.

Based on literature on service innovations, business networks and IoT, dynamic concepts are selected. Aided by information about an evolving case “The connected vehicle”, propositions about interaction between the variables in the framework are formulated.

A conceptual framework consisting of four interacting variables: overlapping, intermediating, objectification of actors and business modelling is developed, linking several streams of research. Propositions are motivated and issues for further research questions formulated.

The framework may stimulate further research on IoT-enabled service innovations.

Understanding network dynamics for developing and implementing business models for service innovations.

The conceptual framework provides an original contribution to understanding IoT-enabled service innovations.

This paper aims to explore the effects of holistic cognition frame on novelty-centered business model design and efficiency-centered business model design. Moreover, the authors consider how these effects differ in new ventures vs established firms.

The authors use survey data to testify the hypotheses based on a database of 204 firms in China. Then, regression analysis is used to examine the relationship between holistic cognition frame and business model design. They also explore the contingency effects of new ventures and established firms on the relationships.

The authors find that the holistic cognition frame has a positive effect on efficiency-centered business model design, whereas it has an inverse U-shaped effect on novelty-centered business model design. Furthermore, they find that the effects of holistic cognition frame on efficiency-centered business model design and novelty-centered business model design are different in established firms and new ventures.

This work offers new insights into the effects of holistic cognition frame on business model design and provides useful suggestions for firms to promote business model design.