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The implementation of enterprise content management (ECM) software applications has been a subject of extensive discussion. Although a number of ECM scholars have provided guidance on ECM implementation, there is a gap in how to assess benefits accruing from the implementation. One of the approaches of assessment is the use of maturity models. This paper aims to examine the utility of other ECM maturity model (ECM3) as an assessment tool.

The study was undertaken based on two related research questions, the first explored ECM3 assessment within a South African context and the second explored the utility of other maturity models for ECM implementation and lessons learnt to improve ECM3.

The results show that all the South African institutions assessed using ECM3 had a low level of maturity and there are a number of maturity models comparable to ECM3, and the global survey provides the closest parallel to the survey conducted in South Africa.

The study offers a unique discussion on the possible utility of ECM3 as a maturity model for assessing ECM implementation. This was done by comparing it with maturity models developed or used by records professionals and through assessing the results of two surveys, one conducted amongst South African institutions and another conducted by the Real Story Group.

The purpose of this paper is to gain a deeper comprehension of the nature of enterprise content management systems (ECMS) success by exploring factors that are important in the context of ECMS success, i.e. how these factors can be measured, and how they are interrelated.

The paper develops a success model specific to the enterprise content management (ECM) domain that builds on the DeLone and McLean information systems (IS) success model. The model is empirically tested by means of structural equation modeling applying the partial least squares approach and using data collected in an online survey.

The results show that ECMS positively affects organizational content management in terms of efficiency, collaboration and compliance. It also provides evidence that the use of the ECMS alone does not provide impact to the organization, but needs to be moderated either by the impact of the ECMS on the user or the users’ satisfaction of the ECMS.

For practitioners, the model identifies the factors that influence the success of ECMS. Practitioners can monitor these factors as performance indicators to improve users’ satisfaction with the ECMS and, thus, the success of their ECMS. Furthermore, the results can support practitioners in understanding the multiple facets of ECMS success to improve how they plan and prepare for ECM investments.

The study’s results contribute to theory by extending and empirically testing the D&M IS success model in a new domain and system context. The presented research is the first to empirically validate a comprehensive ECMS success model that extends knowledge related to ECM by examining the relationship between the quality dimensions and the success measures.

This paper aims at developing an interpretive structural model of drivers for environmentally conscious manufacturing (ECM). It will demonstrate how interpretive structural modeling (ISM) supports policy makers in the government and industry in identifying and understanding interdependencies among drivers for ECM. Interdependencies among drivers will be derived and structured into a hierarchy to derive subsystems of interdependent elements with corresponding driving power and dependency.

ISM has been used to identify hierarchy and inter-relationships among drivers for ECM adoption and to classify the drivers according to their driving and dependence power using MICMAC analysis. The drivers for ECM adoption are identified through the review of literature followed by developing a model of drivers using ISM.

The main findings of the paper include the development of an ISM model of drivers for ECM adoption. The developed model divided the identified drivers into five levels of hierarchies showing their inter-relationship and depicting the driving-dependence relationship. These five levels have been classified into four categories – awareness, external, organizational and benefits.

The developed ISM model is expected to provide a direction to the policy makers in the government and industry and the top management of the organizations to leverage their resources in a timely manner to adopt ECM successfully.

Sustainable success of group-buying web sites implies that consumers not only accept them initially but also use them continuously. Most group-buying web sites, however, are unable to achieve such sustainable success. Drawing on expectation-confirmation model (ECM), the purpose of this paper is to examine the factors that influence group-buying web sites continuance.

In total, 605 valid responses were collected via a survey of a leading group-buying web site in China. Structural equation modeling was used to test the research model. This study also compared the three competing models of continued use behavior, namely, ECM, the research model and the integrated model.

The results show that perceived web site quality, perceived price advantage and confirmation are important determinants of consumer satisfaction, while perceived web site quality, perceived reputation, satisfaction and perceived critical mass significantly influence consumers’ continuance intention. The research model accounts for more variance in satisfaction and continuance intention than ECM and is the best-fitting model among the competing models.

Drawing on ECM, this research proposes a model by incorporating three online group-buying characteristics (perceived price advantage, perceived reputation and perceived web site quality) and two social influence factors (subjective norm and perceived critical mass). Specially, this study uses these three online group-buying characteristics to represent perceived usefulness in ECM. These constructs are found to influence consumer satisfaction and continuance intention. This study extends ECM and provides a better explanation of consumers’ post-purchase behavior in the online group-buying context.

Manufacturing firms consume energy and natural resources in highly unsustainable manner and release huge amounts of green house gases leading to many economic, environmental and social problems; from local waste disposal to climate change. Consciousness about these issues has lead to a new manufacturing paradigm of environmentally conscious manufacturing (ECM). There exist many social, legislative, policy, economic, internal, and environmental factors which can motivate and/or force industry to adopt ECM. The purpose of this paper is to identify the drivers for ECM, developing a model of these drivers using statistical analysis and testing the model using structural equation modeling (SEM) technique.

The basic steps of methodology are ECM driver development, survey instrument development, data collection, model proposition, and model validation. The main data analysis approaches are exploratory factor analysis, confirmatory factor analysis, and SEM to develop a model of drivers and validating the same based on the data collected from the manufacturing industry.

The reliable, valid, and tested model has three types of drivers – internal, policy, and economic. It has been found through hypothesis testing that internal drivers for the implementation of ECM are positively related to policy and economic drivers; and policy drivers are positively related to economy drivers. This research is expected to help government and industry in developing policies and strategies for the successful implementation of ECM.

The novelty of this study is that it provides the relationship among the drivers which can be leveraged by the managers to focus on the root drivers for smooth and effective implementation of ECM.

This paper provides new theoretical insight into the factors motivating the industry to implement ECM systems in the industry with special focus on manufacturing sector of emerging economies.

The purpose of this paper is to investigate the interactions between new product development (NPD) and engineering change management (ECM) processes in terms of their impact on organizational performance.

A system dynamics model of the NPD and ECM processes within an organization has been built and simulated for a range of parameter values to investigate the interactions between the two processes.

The effect of various parameters on the lead‐time of the NPD process varies with different process environments. No single process management policy is advantageous for most if not all process operating conditions, thus it is important to change the critical parameters of the process every time.

The accuracy of the estimated effect of parameters on the lead‐time depends on the accuracy of estimated parameter values.

The insights developed from the results would be useful for managers in planning their process management policies under various circumstances.

The study of interactions between the ECM and NPD processes has been scarce. This research would be very helpful to managers who plan the process managing strategies given various circumstances such as limited resources.

Blast‐induced traumatic brain injury (TBI) is a signature injury of the current military conflicts. Among the different types of TBI, diffuse axonal injury (DAI) plays an important role since it can lead to devastating effects in the inflicted military personnel. To better understand the potential causes associated with DAI, this paper aims to investigate a transient non‐linear dynamics finite element simulation of the response of the brain white matter to shock loading.

Brain white matter is considered to be a heterogeneous material consisting of fiber‐like axons and a structure‐less extracellular matrix (ECM). The brain white matter microstructure in the investigated corpus callosum region of the brain is idealized using a regular hexagonal arrangement of aligned equal‐size axons. Deviatoric stress response of the axon and the ECM is modeled using a linear isotropic viscoelastic formulation while the hydrostatic stress response is modeled using a shock‐type equation of state. To account for the stochastic character of the brain white matter microstructure and shock loading, a parametric study is carried out involving a factorial variation of the key microstructural and waveform parameters.

The results obtained show that the extent of axon undulations and the strength of axon/ECM bonding profoundly affect the spatial distribution and magnitude of the axonal axial normal and shear stresses (the stresses which can cause diffuse axonal injury).

The present approach enables a more accurate determination of the mechanical behavior of brain white matter when subjected to a shock.

This study's purpose is to propose an integrated post-adoption model based on expectation-confirmation model (ECM) and cognitive absorption (CA) theory to examine whether network factors, gamification factor, and quality factors as antecedents to end-users' beliefs can affect their continuance intention of the robo-advisor.

A total of 600 questionnaires were distributed in three sample banks in Taiwan, and sample data for this study were collected from these three banks' customers who had experience in using these banks' own robo-advisor to make their investment decisions. Consequently, 381 useable questionnaires were analyzed using structural equation modeling in this study, with a useable response rate of 63.5%.

This study proposes a solid research model that based on ECM and CA theory, three types of factors, network factors, gamification factor, and quality factors, as antecedents to end-users’ continuance intention of the robo-advisor have been examined, and this study's results strongly support the research model with all hypothesized links being significant.

This study contributes to end-users' continuance intention of the robo-advisor based on ECM, CA theory, theory of network externalities, gamification, and updated DeLone and McLean IS success model, and reveals deep insights into the evaluation of determinants in the field of end-users' continuance intention of the robo-advisor. Hence, it is especially worth mentioning that three types of determinants (i.e. network factors, gamification factor, and quality factors) are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into account in this study's research model development of end-users' continuance intention of the robo-advisor to acquire a more all-round and robust analysis.

A literature review revealed that none of the few longitudinal studies of enterprise content management (ECM) implementations focus on small and medium‐sized enterprises (SMEs). To contribute to this area, the purpose of this paper is to generate insights in relation to how SMEs can successfully promote implementation of ECM solutions.

The paper presents and analyses a longitudinal study of an ECM project.

The findings of the paper include a definition of a process model for ECM implementation in SMEs, identification of ten success factors related to ECM system implementation, and a definition of a new pattern for ECM technology development, compared to existing case studies.

Since it appears to be the first detailed study of ECM system implementation in SMEs, this paper provides a point of departure for future research in the use of ECM technology in SMEs.

Practitioners in SMEs preparing to engage in ECM projects may utilize the findings of the paper in relation to managing the implementation process and understanding various benefits that ECM systems can produce.

The paper represents a contribution to the sparse literature on ECM implementation. In fact, the case seems to be the first longitudinal study of ECM implementation in SMEs.

The purpose of this paper is to explore the key antecedents of organizational users’ continuance intention of cloud enterprise resource planning (ERP) by the integration of constructs from the expectation–confirmation model (ECM), technology acceptance model (TAM), theory of planned behavior (TPB), task-technology fit and TAM2.

Sample data for this study were collected from end users of cloud ERP working in companies in Taiwan. A total of 560 questionnaires were distributed in the 56 sample companies, and 395 (70.5 percent) usable questionnaires were analyzed using structural equation modeling in this study.

Findings show that while compatibility and output quality are key antecedents of confirmation of expectations toward cloud ERP, compatibility still reveals overwhelming and more indirect impacts on users’ continuance intention of cloud ERP than output quality because output quality has significant but evidently lesser effects on perceived usefulness and perceived ease of use than compatibility.

First, this study contributes to the identification of compatibility and output quality that may reveal deep insights to the task-related factors in understanding users’ cloud ERP continuance greatly driven by their confirmation of expectations toward cloud ERP. Next, the empirical evidence on capturing the ECM, TAM and TPB for completely explaining the antecedents of users’ continued cloud ERP usage intention is well documented. Hence, this study’s findings have significantly shed light on the possible formulation of a richer post-adoption model.