Explaining organizational susceptibility to coercive pressure: results from a field experiment on e-invoicing IOIS adoption

Information Systems and e-Business Management - The establishment of the interoperability of accounting and other IS with those of business partners is regarded as a primary source of efficiency...     

Analysing factors impacting BPMS performance: a case of a challenged technology adoption

Business Process Management Suites (BPMSs) have been adopted in organisations to model, improve and automate business processes as they aim to increase the quality, efficiency and agility of their business processes. Yet, many organisations struggle to achieve the benefits they expected from a BPMS. This interpretive case study in a large South African financial services organisation explains factors found to negatively impact successful BPMS adoption. The paper describes how an IT team struggled to increase process agility with a BPMS in a large legacy application landscape. The dominant factors causing the struggle were the difficulty of integrating with other applications and a lack of governance around BPM. Interesting findings on the difficulties in resourcing BPM IT teams are presented. The impact of BPM strategy, culture and governance on BPM methods, resourcing, data and technology is explained. The BPM literature lacks empirical qualitative case studies and theoretical models. This paper aimed to contribute to both needs. The theoretical contribution of this paper is two models. The first inductively derived explanatory contextual model should be useful for practitioners wanting to adopt a BPMS. Using this study’s findings and models from the literature, a second, more generic explanatory model of information system performance is derived for a BPMS.

     

Enabling agile adoption practices through network organizations

As distributed organizations increasingly rely on technological innovations to enhance organizational efficiency and competitiveness, interest in agile practices that enable adoption of information technology (IT) based innovations has grown. This study examines the influence of a network organization environment on the ability to develop agile adoption practices. An exploratory case study design was used to investigate the interactions between network structure, social information processing, organizational similarity (homophily), and absorptive capacity during the adoption of a large-scale IT system in two network organization environments within New York State. The data suggest that network organization characteristics and communication processes that reinforced social influence and supported knowledge transfer positively influenced adoption agility. We propose a model of agile adoption practices and discuss implications for the development of theory about network organization characteristics and capabilities to adopt IT-based innovations.     

Implementing enterprise content management: from evolution through strategy to contradictions out-of-the-box

This study examines a strategic development and implementation process of enterprise content management (ECM) in a large oil company. In view of the framework of four motors of development and change in organizations, this study represents a revelatory case of a hybrid development approach to ECM that involves the teleological, life-cycle, and dialectical motors of development. This is in contrast to the evolutionary development motor, which has prevailed in the hitherto reported content management research. The case study also complements process-based research on enterprise system implementations in general. We suggest that research and practice on large-scale ECM implementations should acknowledge all the four motors of change.     

Everyday power struggles: living in an IOIS project

The broad aim of this interpretive study was to investigate the lived experiences of inter-organisational information system (IOIS) project members who worked in a 3-year-long IOIS project. The study presents an original longitudinal study of project member experiences. In this paper, we have described and analysed one core category, project power, which was derived from a grounded theory (GT) study. This study extends existing theories about power in organisational contexts, in particular the everyday use of power in projects. The paper shows how GT can be used to gain significant insights into a case study, and also generate new concepts. This paper gives detailed insights into the power issues as they played out in a complex IOIS project. This IOIS project spanned four user organisations, two suppliers, one national organisation, a research organisation and a government Ministry. One key finding is that, given the complexity of the project management structures in an IOIS project such as this, project members often resorted to formal authority as a means of getting things done, in the absence of informal links between organisations. The project history also had major ramifications on the use of power in this project.     

Analysing roughness of surface through fractal dimension: A review

In last three decades, fractal geometry (FG) has been the focus of attention by several researchers owing to it exhibiting excellent properties and robust application with respect to current research scenario. Fractal Dimension (FD) plays a vital role in order to analyse complex objects that are found in nature which was failed to be analysed by Euclidian geometry. FD is an imperative aspect of FG to provide indicative application in different areas of research including image processing, pattern recognition, computer graphics and many more. Analysis of an image is an important technique of image processing to describe image features like texture, roughness, smoothness etc., and is only possible through FG. Due to this reason many more technique were evolved to estimate the fractal dimension. The main aim of this article is to give a comprehensive review, which summarizes recent research progress on analysis of surface roughness and an overview of different concepts, and the way they work and their benefits and their limitations, and also we deliver how the different concepts taken into consideration to estimate FD depend upon different algorithms. This article also discusses several factors affecting FD estimation; types of similarity property, spatial resolution, sampling process, region of interest, spectral band and box-height criteria are discussed. Furthermore, we have tried to present the application area oriented versus core area of FG. There are several contradictory results found in many kinds of literature on the influence of different parameters while conducting FD analysis. Mainly it has been observed that the FD estimation will be affected by texture property, gray scale range, color property, color distance and the other parameters which are already mentioned. Hence this article will be beneficial for researchers in order to select precise FD estimation. However different algorithms lead to different results even with the use of the same kind of database images, so selection of appropriate technique is a major challenge for accurate estimation. Therefore an in-depth and proper understanding is required in order to choose the appropriate algorithm and also a robust algorithm for analysing roughness in better and precise way needs to be developed.     

Analysing the localisation sites of proteins through neural networks ensembles

Scientists involved in the area of proteomics are currently seeking integrated, customised and validated research solutions to better expedite their work in proteomics analyses and drug discoveries. Some drugs and most of their cell targets are proteins, because proteins dictate biological phenotype. In this context, the automated analysis of protein localisation is more complex than the automated analysis of DNA sequences; nevertheless the benefits to be derived are of same or greater importance. In order to accomplish this target, the right choice of the kind of the methods for these applications, especially when the data set is drastically imbalanced, is very important and crucial. In this paper we investigate the performance of some commonly used classifiers, such as the K nearest neighbours and feed-forward neural networks with and without cross-validation, in a class of imbalanced problems from the bioinformatics domain. Furthermore, we construct ensemble-based schemes using the notion of diversity, and we empirically test their performance on the same problems. The experimental results favour the generation of neural network ensembles as these are able to produce good generalisation ability and significant improvement compared to other single classifier methods.     

A web of contradictions

We describe our use of contradictions, a concept central to a popular formulation of activity theory, to derive requirements of a new technical system to support an administrative system. Contradictions are the underlying causes of disturbances in the free operation of workplace activities. We argue and demonstrate that the resolution of such contradictions can be used as the basis for the (user-centred) design of a new system. We conclude that contradictions are both conceptually valuable in understanding the design of systems and are of considerable practical use.     

Analysing complex behaviour of hydrological systems through a system dynamics approach

The interaction among various water cycle components consists of complex, non-linear, and bidirectional (interdependent) biophysical processes which can be interpreted using feedback loops in a system dynamics (SD) environment. This paper demonstrates application of an SD approach with two case studies using a specialised software tool, Vensim. The first case study simulates water balance in a rice field system on a daily basis under aerobic conditions with provision of supplemental irrigation on demand. A physically based conceptual water balance model was developed and then implemented using Vensim to simulate the processes that occur in the field water balance system including percolation, surface runoff, actual evapotranspiration, and capillary rise. The second case study simulates surface–groundwater dynamic interactions in an irrigation area where river water and groundwater are two key sources of irrigation. The modelled system encompasses dynamically linked processes including seepage from the river, evaporation from a shallow watertable, groundwater storage, and lateral flow from upland to lowland areas. The model can be applied to simulate responses of different irrigation management scenarios, to develop strategies to improve water use efficiency and control watertable, to prevent salinization in upland, and to reduce the cost of groundwater abstraction in lowland areas. The discussed applications of the SD approach conclude that it helps to conceptualize and simulate complex and dynamic water system processes deterministically which are otherwise partly simulated by conventional hydrologic and stochastic modelling approaches. It is recognised that conceptualization and implementation phases of this approach are challenging, however, the latter is greatly assisted by modern computer softwares.     

Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling

Together with the development of information systems research, there has also been increased interest in non-linear relationships between focal constructs. This article presents six Partial Least Squares-based approaches for estimating formative constructs’ quadratic effects. In addition, these approaches’ performance is tested by means of a complex Monte Carlo experiment. The experiment reveals significant and substantial differences between the approaches. In general, the performance of the hybrid approach as suggested by Wold (1982) is most convincing in terms of point estimate accuracy, statistical power, and prediction accuracy. The two-stage approach suggested by Chin et al (1996) showed almost the same performance; differences between it and the hybrid approach – although statistically significant – were unsubstantial. Based on these results, the article provides guidelines for the analysis of non-linear effects by means of variance-based structural equation modelling.     

An empirical evaluation of existing IS change theories for the case of IOIS evolution

The phenomenon of inter-organizational information systems (IOIS) evolution has not yet been adequately researched and understood. We present and analyse empirical data from a case in which electronic ordering in the Australian pharmaceutical industry evolved over a 30-year period from closed to quasi-open systems. We analyse this revelatory case using a practice-theoretical framework to make visible the phenomenon of IOIS evolution. An essential characteristic of this framework is the distinction between and symmetrical treatment of material, normative and ideational structures within the practices that constitute the IOIS. Against the findings of this case study, we then evaluate two promising models of long-term IS change, namely Porra's (1999) Colonial Systems model and Lyytinen and Newman's (2008) Punctuated Socio-technical IS Change model. These models are selected as highly elaborated IS exemplars of two classes of theories of organizational change, namely evolutionary and dialectical theories. We find that these two models can only partially explain our findings. Finally, we make suggestions for developing more comprehensive theoretical models within these two classes of IS change theories. In practical terms, our paper shows that the transformation from closed to open IOIS may require adoption of longer time frames than are usually assumed and closer attention to norms and rationales usually neglected in IS projects.     

Policy making for broadband adoption and usage in Chile through machine learning

For developing countries, such as Chile, we study the influential factors for adoption and usage of broadband services. In particular, subsidies on the broadband price are analyzed to see if this initiative has a significant effect in the broadband penetration. To carry out this study, machine learning techniques are used to identify different household profiles using the data obtained from a survey on access, use, and users of broadband Internet from Chile. Different policies are proposed for each group found, which were then evaluated empirically through Bayesian networks. Results show that an unconditional subsidy for the Internet price does not seem to be very appropriate for everyone since it is only significant for some households groups. The evaluation using Bayesian networks showed that other polices should be considered as well such as the incorporation of computers, Internet applications development, and digital literacy training.     

Novice-based data collection methods for the study of IOIS: practice probes and learning communities

In response to the increasing influence of practice theory perspectives for studying organisational and inter-organisational information systems, we demonstrate that an important dilemma from this perspective for data collection methods is between authentic access to practices and the ability to thematize knowledge of practices. We propose a promising new approach to this dilemma that uses the learning experiences of novice practitioners to collect data as they are progressively enrolled in the practice, and describe two instantiations of this new approach, practice probes and learning communities.     

Managing strategic contradictions in hybrid teams

It has become a common practice to organize work teams to include members in multiple geographic locations. In contrast to co-located teams and purely virtual teams, these ‘hybrid’ teams combine face-to-face communication with computer-mediated interaction. In this paper, we report a qualitative study of management practices in three hybrid teams in one organization. We adopt the theoretical approach of strategic contradiction, in which apparently contradictory pairs of elements can be managed by attending to their possible synergies or clarifying their distinctions so as to make balanced trade-offs over time. Our data reveal four sets of paradoxical frames in hybrid teams: remoteness–closeness, cultural uniformity–cultural diversity, rationality–emotionality, and control–empowerment. In referencing these paradoxical frames, teams engaged in three cognitive processes: (a) integrating to produce synergies between opposing tensions, (b) differentiating to clarify distinctions between opposing tensions and to balance trade-offs over time, and (c) polarizing to remove tensions between opposing elements by using one element to reduce the effects of another. Both integrating and differentiating processes were found to be instrumental to sustaining contradictions as interdependent dualities, whereas polarizing processes were found to preserve contradictions as dualisms. Our findings advance the understanding of managing strategic contradictions by showing how managers influence cognitive processes that paradoxically emphasize remoteness and closeness, cultural uniformity and cultural diversity, rationality and emotionality, and control and empowerment.     

Analysing stable feature selection through an augmented marine predator algorithm based on opposition-based learning

Retrieving the relevant information from the high-dimensional dataset enhances the classification accuracy of a predictive model. This research critique has devised an improved marine predator algorithm based on opposition learning for stable feature selection to overcome the problem of high-dimensionality. Marine predator algorithm is a population-based meta-heuristics optimization algorithm that works on the ‘survival-of-the-fittest’ theory. Classical marine predator algorithm explores the search space merely in one direction, affecting its converging capacity while being responsible for stagnation at local minima. The proposed opposition-based learning nuances enhance the exploration capacity of marine predator algorithm and productively converges the model to global optima. The proposed OBL-based marine predator algorithm selects stable, substantial elements from six different high-dimensional microarray datasets. The performance of the proposed method is investigated using five predominantly used classifiers. From the result, it is understood that the proposed approach outperforms other conventional feature selection techniques in terms of converging capability, classification accuracy, and stable feature selection.     

Advancing building engineering through structural and topology optimization

Structural and Multidisciplinary Optimization - Traditional building design is often done in a (pseudo-) sequential manner: the architect defines the form, the structural engineer defines the...