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The purpose of this paper is to show how stages of growth models have been applied to a number of organizational phenomena in management research. One class of models consists of maturity models for IT outsourcing relationships. There is a need for an improved theoretical foundation for stage models.

Based on a literature review of theory research as well as stage research, a stage of growth theory is proposed in this paper. Also, a procedure for stages of growth modeling is suggested.

A theoretical foundation for stages of growth models enables trust worthier modeling.

Future research modeling organizational phenomena can follow the suggested modeling procedure. This paper can be used in teaching by discussing industrial management phenomena along stages of growth by applying dominant problems and benchmark variables.

In strategy work, this paper helps in providing a framework for assessing current stage as well as determining future strategic direction for an industrial organization.

Theory development in this paper includes four important propositions on which this theory is based.

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.

First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.

The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.

Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

Inter-organisational collaboration (IOC) across healthcare settings has been put forward as a solution to mounting financial and sustainability challenges. Whilst ingredients for successful IOC have been explored, there remains limited understanding of the development of IOCs over time.

The authors systematically reviewed the literature to identify models applied to IOCs in healthcare across databases such as Healthcare Management Information Consortium (HMIC) and MEDLINE, identifying 2,763 titles and abstracts with 26 final papers included. The authors then used a “best fit” framework synthesis methodology to synthesise fourteen models of IOC in healthcare and the wider public sector to formulate an applied composite model describing the process through which collaborations change over time. This synthesis comprised extracting stages and behaviours from included models, selecting an a priori framework upon which to code these stages and behaviours and then re-coding them to construct a new composite model.

Existing models often did not consider that organisations may undergo many IOCs in the organisations' lifetime nor included “contemplation” stages or those analogous to “dissolution”, which might negatively impact papers using such models. The formulated' composite model utilises a life-cycle design comprising five non-linear phases, namely Contemplating, Connecting, Planning, Implementation and Maintenance or Dissolution and incorporates dynamic elements from Complex Adaptive Systems thinking to reflect the dynamic nature of collaborations.

This is the first purpose-built model of the lifecycles of IOCs in healthcare. The model is intended to inform implementers, evaluators and researchers of IOCs alike.

The existing three-stage network Data Envelopment Analysis (DEA) models with shared input are self-assessment model that are prone to extreme efficiency scores in pursuit of decision-making units (DMUs) efficiency maximization. This study aims to solve the sorting failure problem of the three-stage network DEA model with shared input and applies the proposed model to evaluate innovation resource allocation efficiency of Chinese industrial enterprises.

A three-stage network cross-DEA model considering shared input is proposed by incorporating the cross-efficiency model into the three-stage network DEA model. An application of the proposed model in the innovation resource allocation of industrial enterprise is implemented in 30 provinces of China during 2015–2019.

The efficiency of DMU would be overestimated if the decision-maker preference is overlooked. Moreover, the innovation resource allocation performance of Chinese industrial enterprises had a different spatial distribution, with high in eastern and central China and low in western China. Eastern China was good at knowledge production and technology development but not good at commercial transformation. Northeast China performed well in technology development and commercial conversion but not in knowledge production. The central China did not perform well in terms of technology development.

A three-stage network DEA model with shared input is proposed for the first time, which makes up for the problem of sorting failure of the general three-stage network model.

Defines end user computing (EUC). Suggests a dichotomy between information systems (IS) managers and end users, both of whom wish control computing resources. Reviews literature concerning EUC and the stage growth model. Suggests alternative visions of the role of IS and user managements in the growth of EUC.

The purpose of this paper is to apply data envelopment analysis (DEA) techniques to the collective decision-making environment to appraise two-stage production process under different decision preferences.

The authors propose a novel multi-criteria group decision-making approach that uses consensus-strategic data envelopment analysis (CSDEA) to appraise two-stage production process under two different decision strategies, which are efficiency- and fairness-based group decision preferences.

The authors find that the proposed CSDEA model evaluates the performance of the decision-making units (DMUs) not by diminishing other competitors but rather based on group interests of the entire decision set.

The authors extend Li’s two-stage model to cases that consider both intermediate inputs and outputs. The authors address the issue of incorporating collective managerial strategy into multi-criteria group decision-making and propose a novel CSDEA model that considers not only the individual-level performance of a DMU but also the group-level or collective decision strategies.

The purpose of this paper is to timely control of a construction collapse accident effectively during its development process by constructing a stage model and then aligning IT with each stage to help provide the information for decision making.

Through comprehensive literature review, this paper first identifies the various IT applications in on-site construction monitoring and analyzes the existed disaster/crisis stage models, also the stage models are compared with the causation models to illustrate the strengths. Then, a three-step methodology was conducted to develop and apply the conceptual framework, including the construction of the four-stage model; the establishment of the conceptual framework of information technology (IT) support for management of construction accidents (ITSMCA); and a building collapse accident used to illustrate the proposed framework.

The accident is divided into four stages, which are incubation stage, outbreak stage, spreading stage and final stage. The real-time staged information to support decision making, such as the contributing factors of on-site workers, materials, equipment and workplace, can be provided by emerging IT. Therefore, IT is aligned with the variations of contributing factors’ attributes in the four stages and ITSMCA is constructed to help accidents management.

The focus of the framework presented in this paper is that the stage model is effective for it catches the variations of the attributes whose values can be provided by IT rather than research on the practical application of the IT system. The construction and application of the IT system will be the research focus in the future.

This paper presents a stage model of a building collapse accident and gives a comprehensive conceptual framework of ITSMCA, which align the IT with different stages of the collapse accident. The ITSMCA proposes a feasible ideology and practical method for real-time management of the collapse accident during the process.

This study aims to expand previous work on website stage models by proposing a new model including a dimension encompassing digital integration and the context of mobile, digital marketing and new technologies. Also, this study aims to classify Brazilian wineries using the stage model proposed and verify if the designated stages influence the wineries’ presence in digital and mobile media.

Observational research and quantitative content analysis were adopted to examine a sample of winery websites (N = 150) located in different states and regions of Brazil and create a new website stage model. After the new model’s development, the authors conducted a statistic analysis by running a one-factor analysis of variance to assess the influence of the company’s stage in the use of digital media and mobile strategies, as well as its impacts on market development.

The results suggested a quite mature market regarding website stage position, even though there is still room for growth and technological innovation. Also, the research findings show that there is a positive association between the company’s stage in the use of digital and mobile media strategies. In other words, the more advanced the winery stage, the greater its digital media and mobile presence.

While previous studies attempted to identify wineries’ stages through models, this study presents a new approach by updating the past studies’ stages as well as providing and testing a digital media and mobile dimension. This research contributes to consolidating the Brazilian wine industry position regarding its presence in digital and mobile media, bringing reflections to organizations, society and theory development.

The purpose of this paper is to explore the functionality of multistage programming approach on network supply chain structure.

The general supply chain structure is considered and the supply chain planning model is developed using a two stage programming approach. The same model is extended to cover the applicability and advantages of a multi‐stage programming approach.

A multi‐period supply chain model for new product launches under uncertain demand for supply chain network structure has been developed. The model allows simultaneous determination of optimum procurement quantity, production quantity across the different plants, transportation routes and the outsourcing cost in case of shortages. The proposed multi‐stage model is compared with the standard two‐stage model by examining the difference between the objective values of two solutions. The research clearly shows the importance of the multi‐stage model as compared to the two‐stage programming model.

The models developed here are limited to covering demand uncertainty, whereas real supply chain exhibits different uncertainties like capacity, processing time, etc. This can be the future direction for extending the work.

The model is very useful in designing and planning the supply chain in an uncertain environment. The model allows the adjustment of the production plan as time progresses and uncertainties become resolved.

The model uses a scenario approach to address the supply chain planning problem for a supply chain network structure under an uncertain environment and compares the two‐solution approach for a set of problems. Generally supply chain costs are in millions of dollars and the saving using multi‐stage programming can be significant.

The recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.

This paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.

Among various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.

This study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.