Search results

E‐services are important in B2C e‐commerce for managing customer relations and enhancing sales. In the electronic world the customer and the merchant do not meet face‐to‐face, and the clients are more discerning with increased options and solutions available to them online. With the click of a mouse a customer can find another provider. As customers embrace e‐commerce their expectations about service, support, and how they make purchases are changing. Services to customers offered electronically to enhance their online shopping experience include search support, e‐response to customer queries, orders and transactions, e‐payment, e‐transaction record management, e‐assurance and trust, e‐help and other online support in the B2C e‐space. This paper discusses the role of e‐services in B2C e‐commerce and how they can be applied to enhance the online customer shopping experience. Findings of two research projects that shed some light on both business and customer perspectives of the role of e‐services in the B2C e‐commerce are launched in this paper.

Advanced Manufacturing Technologies (AMTs) present a major challenge for managers; that is, the successful adoption and implementation of AMT and the translating of technical benefits into business benefits in the marketplace. There are many factors that are critical to achieving this success. The authors discuss the key success factors and highlight some of the common problems experienced by firms during the planning and implementation of AMT.

This paper aims to present a case study on the public value of the e‐Sri Lanka program. Four major dimensions of public value creation through e‐government including the delivery of public services, the achievement of outcomes, the development of trust, and the effectiveness of public organizations are considered in evaluating the performance of the e‐Sri Lanka program. The progress of e‐Sri Lanka program is evaluated, and the potential areas for improvement in the development of e‐government in Sri Lanka are identified.

Existing methodologies for evaluating the public value of e‐government have been critically analyzed. The need for extending existing methodologies for better assessing the performance of e‐government is discussed. Consequently, an extended conceptual framework is developed for evaluating the public value of e‐government. Various national survey data in Sri Lanka on e‐government development are used for conducting the empirical analysis on the performance of the e‐Sri Lanka program.

The study reveals that the public value of e‐government in Sri Lanka is unsatisfactory in all the dimensions of public value generation. It shows that the proposed framework is effective in facilitating the identification of public value of e‐government in Sri Lanka. Furthermore, the study shows that the lack of e‐services, the security threat to public information in public organizations, the low adoption of information and communication technologies in government, and the low uptake of available e‐government initiatives are the key reasons for such a poor performance in e‐government.

The proposed framework can be used for evaluating the public value of e‐government. It provides individual governments with an effective means for better understanding the impact of their e‐government efforts on their citizens and societies, leading to better policies and strategies being made for the continuous development of e‐government.

The proposed framework would be the first approach in examining the public value of e‐government by considering all the dimensions of public value creation. It is the first in‐depth study of public value creation through e‐government in Sri Lanka. Such a study is significant to Sri Lanka giving that its e‐government development is at a crucial stage supported by various international aid organizations.

The purpose of this paper is to capture and understand the nature of the relationship between e-government development and the digital economy.

Drawing on the Technology Acceptance Model and Fountain’s technology enactment theory, a multidimensional research model was developed. The model was tested empirically through an international study of 67 countries using reputable archival data, primarily including the UN’s e-government survey and the Economist Intelligence Unit’s digital economy rankings.

The empirical findings indicate a strong positive reciprocal (two-way) relationship between e-government development and the digital economy. This finding provides empirical evidence to support the general notion of “co-evolution” between technology and organisations. The study also finds that along with social, economic, political, technological and demographic factors, certain national cultural characteristics have significant effects on the digital economy and e-government development.

Relying on archival global data sets, this study is constrained by the coverage and formulation of the data set indices, the sample size (67 countries), and the impossibility of detecting errors that may occur in the process of data collection. Therefore, caution should be taken when making generalisations about the findings of this study.

The paper addresses a deficit of empirical research that is supported by sound and established theories to explain short-term dynamics and the long-term impact of the digital economy on public administration. The study contributes to a more accurate and comprehensive understanding of the dynamic relationship between e-government development and the digital economy.

Today, a firm’s major concern is to know the way in which an innovation is adopted in the marketplace. The purpose of this paper is to focus on the two-stage nature of diffusion process in which the time lag between people being informed and their act of making final purchase is considered.

The paper discusses an approach based on the time lag for modeling awareness and adoption process as two separate and yet connected processes. Varying forms of time lag (constant, deterministic or random) have been considered while modeling the required framework. Furthermore, an equivalence approach has been shown between the present framework and the two well-known and established approaches of infinite queuing theory and hazard rate function.

The results are verified on sales data of two different consumer durables and it show good prediction capability of proposed models in capturing the real-life scenario. Further, the equivalence approach helps us to quantify such scenarios which were difficult to be modeled with any one particular approach. Further, the possibility of capturing different market scenarios by studying various distribution functions has been identified.

The proposed methodology is based on a two-stage adoption process. The same can be extended to a multi-stage adoption process as in today’s competitive environment. “Motivation” is one such factor that is highly important which can be considered in some later studies. In future, the authors wish to study the multi-stage adoption process considering the different forms of time lag function.

The equivalence approach discussed in the paper can help to cater the possibility of capturing different market scenarios by studying various distribution functions.

The proposed approach helps to cater the time lag between awareness and adoption process and develop different mean value functions to account for the manner in which sales are happening under different circumstances. The proposed methodical approach can also help decision makers in managing their available resources in a prudent manner.

In the current market scenario, software upgrades and updates have proved to be very handy in improving the reliability of the software in its operational phase. Software upgrades help in reinventing working software through major changes, like functionality addition, feature enhancement, structural changes, etc. In software updates, minor changes are undertaken which help in improving software performance by fixing bugs and security issues in the current version of the software. Through the current proposal, the authors wish to highlight the economic benefits of the combined use of upgrade and update service. A cost analysis model has been proposed for the same.

The article discusses a cost analysis model highlighting the distinction between launch time and time to end the testing process. The number of bugs which have to be catered in each release has been determined which also consists of the count of latent bugs of previous version. Convolution theory has been utilized to incorporate the joint role of tester and user in bug detection into the model. The cost incurred in debugging process was determined. An optimization model was designed which considers the reliability and budget constraints while minimizing the total debugging cost. This optimization was used to determine the release time and testing stop time.

The proposal is backed by real-life software bug dataset consisting of four releases. The model was able to successfully determine the ideal software release time and the testing stop time. An increased profit is generated by releasing the software earlier and continues testing long after its release.

The work contributes positively to the field by providing an effective optimization model, which was able to determine the economic benefit of the combined use of upgrade and update service. The model can be used by management to determine their timelines and cost that will be incurred depending on their product and available resources.

Mathematical modeling of innovation diffusion is a constantly evolving field within marketing science. The diffusion process explains the dispersion of an innovation among potential buyers. Prior research on innovation diffusion has been based on modeling varied aspects of real life situations in marketing. One such aspect is studying the adoption process depending on the awareness and motivation level among the customers. Awareness is having knowledge of an innovation, whereas motivation is about the perception of an individual. In line with these aspects, the purpose of this paper is to propose a unified modeling framework for the adoption process based on the awareness and motivation about the product.

When the market is well informed about the product, there are some people who are motivated and some, who have adopted the product earlier and shall now influence others in their buying behavior. It is very much similar to queuing system in which some units are waiting in a queue for the service, service for some units are being processed and some units have already been served. This analogous behavior between two approaches has motivated the use of infinite server queuing theory in modeling adoption of the product. Thereafter, the authors have proposed a unification scheme to model different market scenarios.

From analyzing the values of comparison criteria, it was not clear that which among them is performing best. Thus there was a need for an approach which can judiciously find the optimal model. For this very purpose the authors applied distance-based approach which was capable of computing the optimal model based on the distance of attribute value from the optimal. The analysis performed on two real life sales data sets depict that model in which awareness is following logistic pattern and motivation and adoption are following a constant pattern is ranked one.

The idea has been validated on product. It would be interesting to know how the methodology works on service.

The modeling framework discussed in this paper can be helpful to know from the available set of alternative, which among them is performing better in capturing the spread of the product in the market. The proposed framework offer some managerial guidance by highlighting the unusual aspects of diffusion process and also present an approach to judge the best among a set of different models.

The study aims to focus on data envelopment analysis for assessing the microfinance institutions (MFIs) efficiency over the footings of its undesirable output, i.e. non-performing loans (NPLs). The attention is not only to evaluate the efficiency but also to identify the variable wise inefficiencies incorporating the quality of the portfolio.

The paper assessed MFI efficiency using three different methods of treatment of undesirable output to portray the significant difference. It also has used an advanced methodological model, i.e. weighted Russell directional distance model (WRDDM), under the non-radial assumption that allowed us to find the variable-wise inefficiency contribution. The study also investigated the efficiency differences concerning ownership, including all sizes of MFIs.

The study findings evidence the fall in efficiency score as NPL integrated, and it is found to be statistically significant. In the context of inefficiency assessment, among all input and output variables, total employees and operating expenses, portfolio quality inefficiencies are the leading causes of MFI inefficiencies. Undesirable output inefficiency accounts for almost one-third part of the total inefficiencies and remaining due to input inefficiencies. It is significant to draw attention that there is no improvement in undesirable output inefficiency. By contrast, input inefficiencies retained gains for two years and gradually showed a decreasing trend throughout 2015–2017.

The authors have used balanced panel data of 72 Indian MFIs for five years' period from 2013–2017 whose complete data were available in the Microfinance Information Exchange.

The paper has focused on identifying the inefficiencies that are needed to be focused on to attain efficiency. It could provide vital information to the managers, policymakers in identifying the causes of inefficiencies, which is crucial to improve for long-term sustainability. It will be a roadmap for benchmarking, strategy building and policy-making processes.

The findings of the study help in finding the benchmarking information for the inefficient decision-making units to identify the target units that need particular attention to focus. These practices could give a positive outcome, not only for institutions but also for the MFI clients.

The study provides an insight in to variable-wise inefficiency measurement using advanced model WRDDM in Indian context MFIs.

The Indian power sector is dominated by coal. Environmental awareness and advances in techno-economic front have led to a slow but steady shift towards greener alternatives. The distributions of both fossil fuel resources and renewable energy potential are not uniform across the states. Paper attempts to answer how the states are performing in the sector and how the renewable energy and conventional resources are affecting the dynamics.

The authors employ a two-stage data envelopment analysis (DEA) to rank the performance of Indian states in the power sector. Multi-stage analysis opens up the DEA black-box through disaggregating power sector in two logical sub-sectors. The performance is evaluated from the point-of-view of policy formulating and implementing agencies. Further, an econometric analysis using seemingly unrelated regression equations (SURE) is conducted to estimate the determinants of total and industrial per-capita electricity consumption.

Efficiency scores obtained from the first phase of analysis happens to be a significant explanatory variable for power consumption. The growth in electricity consumption, which is necessary for economic wellbeing, is positively affected by both renewable and non-renewable sources; but conventional sources have a larger impact on per-capita consumption. Yet, the share of renewables in the energy mix has positive elasticity. Hence, the findings are encouraging, because development in storage technologies, falling costs and policy interventions are poised to give further impetus to renewable sources.

The study is one of the very few where entire spectrum of the Indian power sector is evaluated from efficiency perspective. Further, the second phase analysis gives additional relevant insights on the sector.