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This paper aims to examine the process for estimation of efforts for software development and suggests a framework for estimating software development costs and ensuring quality of code in e-Government projects from the Indian state of Andhra Pradesh. With no established processes for estimation of efforts, the Government relied on open bids from the market to develop these e-Government applications.

The paper adopts an exploratory case study approach to analyze the e-Government applications in Andhra Pradesh. Using data from the information technology department of the Government of Andhra Pradesh, the paper evolves a framework to compute costs of software development, based on the software development life cycle.

The framework helps in arriving at a hurdle price before the tender process. The study has shown that an e-Government application in AP state would cost Rs. 224,000, or US$2,969.25, for a simple application, and Rs. 33,60,000, or US$44,538.71, for a complex application over a five-year period, depending on the complexity and size of the application. This information would be useful to the Government decision-makers for expanding e-Government.

Further research may assess the utility of this framework for e-Government support activities like automation of data centers, video conferencing facilities and ushering in financial technologies for encouraging cashless payments.

The paper provides information that could be of value at a national level (for India) and at the same time providing a guide for other countries that would like to adopt this framework.

This research aims to create a methodology that integrates optimization techniques into preliminary cost estimates and predicts the impacts of design alternatives of steel pedestrian bridges (SPBs). The cost estimation process uses two main parameters, but the main goal is to create a cost estimation model.

This study explores a flexible model design that uses computing capabilities for decision-making. Using cost optimization techniques, the model can select an optimal pedestrian bridge system based on multiple criteria that may change independently. This research focuses on four types of SPB systems prevalent in Egypt and worldwide. The study also suggests developing a computerized cost and weight optimization model that enables decision-makers to select the optimal system for SPBs in keeping up with the criteria established for that system.

In this paper, the authors developed an optimization model for cost estimates of SPBs. The model considers two main parameters: weight and cost. The main contribution of this study based on a parametric study is to propose an approach that enables structural engineers and designers to select the optimum system for SPBs.

The implications of this research from a practical perspective are that the study outlines a feasible approach to develop a computerized model that utilizes the capabilities of computing for quick cost optimization that enables decision-makers to select the optimal system for four common SPBs based on multiple criteria that may change independently and in concert with cost optimization during the preliminary design stage.

The model can choose an optimal system for SPBs based on multiple criteria that may change independently and in concert with cost optimization. The resulting optimization model can forecast the optimum cost of the SPBs for different structural spans and road spans based on local unit costs of materials cost of steel structures, fabrication, erection and painting works.

The authors developed a computerized model that uses spreadsheet software's capabilities for cost optimization, enabling decision-makers to select the optimal system for SPBs meeting the criteria established for such a system. Based on structural characteristics and material unit costs, this study shows that using the optimization model for estimating the total direct cost of SPB systems, the project cost can be accurately predicted based on the conceptual design status, and positive prediction outcomes are achieved.

Cost-benefit (C/B) analysis helps to determine the economic feasibility of business software investments. Research literature and published practices do not recognize substantial software maintenance costs in C/B analysis. Current analyses emphasize the benefits of an initial investment but do not consider the recurring benefits of each enhancement during the software lifecycle. Such analyses could lead to incorrect investment decisions and lost business opportunities. This article aims to review current research on software lifecycle costs and develop a theoretically sound C/B analysis.

This article reviews current C/B analyses and discusses their shortcomings in treating the significant recurring maintenance costs. It analyzes the findings of various studies on software maintenance and synthesizes these findings to identify the nature of various maintenance costs and their benefits. Based on the synthesis, it theorizes various cost and benefit elements for inclusion in a revised C/B analysis.

This article identifies each recurring maintenance cost relevant to C/B analysis. It also identifies recurring benefits from each enhancement that hitherto have been omitted. Finally, this article discusses how these costs and benefits should be treated in the revised C/Bs analysis.

This is a conceptual paper proposing a new C/B analysis and requires an empirical validation.

This article provides a revision of the C/B analysis that is long overdue. It will help to justify a software investment correctly, rank software projects that compete for limited funds and help create a sound software project portfolio. Since 20% of software products may incur 80% of software investment, this analysis will help to make correct software investments and avoid lost business opportunities. This article also describes a practical method to use the revised C/B analysis.

This article provides a revision of the C/B analysis that is long overdue. It will help to justify a software investment correctly, rank software projects that compete for limited funds and help create a sound software project portfolio. Since 20% of software products may incur 80% of software investment, this analysis will help to make correct software investments and avoid lost business opportunities. This article also describes a practical method to use the revised C/B analysis.

Managing project completion within the stipulated time is significant to all firms' sustainability. Especially for software start-up firms, it is of utmost importance. For any schedule variation, these firms must spend 25 to 40 percent of the development cost reworking quality defects. Significantly, the existing literature does not support defect rework opportunities under quality aspects among Indian IT start-ups. The present study aims to fill this niche by proposing a unique mathematical model of the defect rework aligned with the Six Sigma quality approach.

An optimization model was formulated, comprising the two objectives: rework “time” and rework “cost.” A case study was developed in relevance, and for the model solution, we used MATLAB and an elitist, Nondominated Sorting Genetic Algorithm (NSGA-II).

The output of the proposed approach reduced the “time” by 31 percent at a minimum “cost”. The derived “Pareto Optimal” front can be used to estimate the “cost” for a pre-determined rework “time” and vice versa, thus adding value to the existing literature.

This work has deployed a decision tree for defect prediction, but it is often criticized for overfitting. This is one of the limitations of this paper. Apart from this, comparing the predicted defect count with other prediction models hasn’t been attempted. NSGA-II has been applied to solve the optimization problem; however, the optimal results obtained have yet to be compared with other algorithms. Further study is envisaged.

The Pareto front provides an effective visual aid for managers to compare multiple strategies to decide the best possible rework “cost” and “time” for their projects. It is beneficial for cost-sensitive start-ups to estimate the rework “cost” and “time” to negotiate with their customers effectively.

This paper proposes a novel quality management framework under the Six Sigma approach, which integrates optimization of critical metrics. As part of this study, a unique mathematical model of the software defect rework process was developed (combined with the proposed framework) to obtain the optimal solution for the perennial problem of schedule slippage in the rework process of software development.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.

Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.

The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.

This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.

Poor indoor air quality (IAQ) contributing to occupants’ health symptoms is a universal, typically ventilation-related, problem in schools. In cold climates, low-cost strategies to improve IAQ in a naturally ventilated school are rare since conventional methods, such as window opening, are often inappropriate. This paper aims to present an investigation of strategies to relieve health symptoms among school occupants in naturally ventilated school in Finland.

A case study approach is adopted to thoroughly investigate the process of generating the alternatives of ventilation redesign in a naturally ventilated school where there have been complaints of health symptoms. First, the potential sources of the occupants’ symptoms are identified. Then, the strategies aiming to reduce the symptoms are compared and evaluated.

In a naturally ventilated school, health symptoms that are significantly caused by insufficient ventilation can be potentially reduced by implementing a supply and exhaust ventilation system. Alternatively, it is possible to retain the natural ventilation with reduced number of occupants. The selected strategy would depend considerably on the desired number of users, the budget and the possibilities to combine the redesign of ventilation with other refurbishment actions. Furthermore, the risk of poorer indoor air caused by the refurbishment actions must also be addressed and considered.

This study may assist municipal authorities and school directors in decisions concerning improvement of classroom IAQ and elimination of building-related symptoms. This research provides economic aspects of alternative strategies and points out the risks related to major refurbishment actions.

Since this study presents a set of features related to indoor air that contribute to occupants’ health as well as matters to be considered when aiming to decrease occupants’ symptoms, it may be of assistance to municipal authorities and practitioners in providing a healthier indoor environment for pupils and teachers.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.

The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.

A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO₂) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.

The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO₂ emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

This study delves into the dynamics between Agile Response to Change (AR), Adaptive Scoping (AS), Stakeholder Engagement (SE) and Project Performance (PP), with a special focus on the moderating influence of Project Complexity (PC). The research, grounded in a thorough literature review, identifies critical gaps in these areas and examines the extent to which PC moderates the effects of AR, AS and SE on PP, offering new perspectives for managing complex projects.

The research develops a conceptual model based on a critical analysis of existing literature. A comprehensive questionnaire was designed, incorporating 28 items to measure AR, AS, SE, PP and PC. Data was collected from 136 project managers across various industries, and the responses were analysed using structural equation modelling (SEM) to explore the complex interplay between these variables.

The analysis revealed that internal AR (ARint) significantly enhances both quantitative (PPqt) and qualitative aspects of PP (PPql). AS and SE were found to positively influence PPql, but their impact on PPqt was not significant. Interestingly, PC was observed to negatively moderate the relationship between ARint and PPql, while its moderating effect on the relationship between ARint and PPql was not significant. The study also notes that PC does not significantly alter the positive correlations between AS, SE and PPql.

This research contributes to the existing body of knowledge by elucidating the relationships between ARint, AS, SE and different dimensions of PP. It uniquely explores the role of PC as a moderating variable in these relationships, offering valuable insights for practitioners and researchers in the field of complex project management.