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Assessing and measuring the extent of organization-level policy implementation has received little scholarly attention, especially in the areas of local governments' procurement and environmental protection. To rectify the paucity of research in this area, this paper adopts Leonard-Barton's (1988) conceptualization of the misalignment between the (policy) innovation and the organization and draws on an original survey of local government finance, environment and public works departments in a representative sample of US cities with at least 25,000 residents to develop a strategy for measuring the extent of the implementation of a sustainable procurement policy (SPP) in local governments.

The authors demonstrate through the construction of a composite index that in order for a SPP to be fully implemented, standards and rules, routines and decision-making criteria need to exist to reconcile any technical, infrastructural and decision-making misalignments between the new policy and an organization's pre-existing norms and routines.

The authors empirically assess and demonstrate that the paper's proposed composite measure of policy implementation is robust to multiple specifications and measurement reliability and construct validity tests.

Whereas the existing literature from political science and policy science has tended to focus on higher levels of implementation in government through a complex hierarchical system, this paper underscores the importance of the policy implementation at the organizational level. Moreover, the authors contribute methodologically by our development of a strategy to measure the extent of the implementation of a SPP by local governments.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

This article presents a study of revenue forecasting in a Florida municipal government. Seven techniques, including the budget officers' judgmental approach, time series models, a deterministic model, and an optimized model, are employed with franchise and utility receipts in the Town of Davie. The authors found that simple time series models outperformed deterministic models and the judgmentally derived forecasts of local officials. Consistent with prior research, findings here suggest that the time series models are not only accurate, but also easy to implement and readily comprehensible by local officials.

The purpose of this paper is to suggest a model that incorporates critical factors contributing to the success in new service development (NSD) projects in electronic government (e‐government).

After a brief introduction to e‐government and NSD in respect to models for successful implementation, the authors justify and build on the existing literature that advocates the use of critical success factors (CSFs) to study the implementation of these projects. They suggest a model that incorporates the determinants of success or failure for a new service through a set of variables.

This paper proposes a framework, which is believed to help with the empirical research of CSF in NSD. The suggested framework attempts to bring experience in leadership and coordination of work theory and practice together by synthesising the existing literature with real‐life experience.

The paper contributes to the e‐government implementation literature in terms of suggesting a model that takes under consideration important CSF for implementing NSD.

The projects a number of implications for public sector scholars as well as administrators. A vast amount of taxpayer money can be saved if decision makers can promptly identify potential waste of funds in prone‐to‐failure projects.

The research aims at providing a better understanding of the underlying factors and dimensions that describe NSD in e‐government through the suggestion of a model that takes under consideration important CSF for implementing NSD. Therefore, it expands the scope of NSD research in e‐government context, stating the need for more research to be conducted regarding the NSD in e‐government using CSF. By identifying the potential success or failure of future projects a number of implications for public sector scholars as well as administrators comes to light.

Virgin Atlantic Cargo is one of the largest air freight operators in the world. As part of a wider strategic development initiative, the company has identified forecasting accuracy as of strategic importance to its operational efficiency. This is because accurate forecast enables the company to have the right resources available at the right place and time. The purpose of this paper is to undertake an evaluation of current month-to-date forecasting utilized by Virgin Atlantic Cargo. The study employed demand patterns drawn from historical data on chargeable weight over a seven-year-period covering six of the company's routes.

A case study is carried out, where a comparison between forecasting models is undertaken using error accuracy measures. Data in the form of historical chargeable weight over a seven-year-period covering six of the company's most profitable routes are employed in the study. For propriety and privacy reasons, data provided by the company have been sanitized.

Preliminary analysis of the time series shows that the air cargo chargeable weight could be difficult to forecast due to demand fluctuations which appear extremely sensitive to external market and economic factors.

The study contributes to existing literature on air cargo forecasting and is therefore of interest to scholars examining the problems of overbooking. Overbooking which is employed by air cargo operators to hedge against “no-show” bookings. However, the inability of air cargo operators to accurately predict cargo capacity unlikely to be used implies that operators are unable to establish with an aspect of certainty their revenue streams. The research methodology adopted is also predominantly discursive in that it employs a synthesis of existing forecasting literature and real-life data for accuracy analysis.

The purpose of this paper is to investigate the effects of creator credibility and backer endorsement on the levels of donation to crowdfunding campaigns in a focal muslim community and to determine the success factors of projects in developing and developed countries.

This research adopts a quantitative approach, utilizing 223 crowdfunding projects from LaunchGood to examine the expected connections. The crowdfunding projects in the distinctive data set (Ramadan) are similar, permitting exploration of the role of creator credibility and backer endorsement in a unique context of religious-based crowdfunding activity.

Creators’ credibility and backers’ endorsements are decisive factors in crowdfunding success. Results from an additional test related to location (developing and developed countries) suggest that creators’ credibility is more important than backers’ endorsements, especially in developing countries.

The projects used in the data set are homogenous and take place in a specific context. The limited number of available observations influences the testing function and capacity of the models and variables. Further, the investigation is related to a specific community, so might not be applicable to all communities.

Potential supporters from the community are vital for the success of crowdfunding projects. This study informs campaign efforts in a religious community by stressing the importance of creators’ credibility and backers’ endorsements. The study also sheds light on factors that affect crowdfunding success in developing and developed countries.

This study helps to explain successful crowdfunding practices in donation-based crowdfunding projects within a religious community context. Results improve scholars’ ability to comprehend individuals’ supporting behavior, providing empirical evidence of creators’ credibility and backers’ endorsement on project success. The study also addresses the determinants of crowdfunding success in developing and developed countries.

The paper is to explore crowdfunding success determinants from the reward menu design aspect, distinguishing from extant studies focusing on characteristics of project creators or crowdfunding projects and funding dynamics. Both the number of reward options and price differentiation of rewards are considered.

The authors use the quadratic model to identify a curvilinear relationship between the number of reward options and crowdfunding success, by running regressions on data collected from one of the most influential reward-based crowdfunding platforms in China. In addition, they explore the moderating effect of price differentiation on the curvilinear relationship.

The authors find an inverted U-shape relationship between the number of reward options and the optimal number of options is around 10. In addition, they find that the curvilinear relationship is moderated by reward price differentiation.

This paper has managerial implications for crowdfunding project creators and platform managers. To achieve better crowdfunding outcomes, a proper number of reward options with diversified reward prices should be provided.

The paper contributes to the literatures in antecedents of crowdfunding success from reward menu design aspect based on theories in investment and purchasing decision making. It is different from existing studies focusing on the characteristics of project creators and crowdfunding projects or funding dynamics. It also parallels retirement contribution plan design studies by exploring the reward menu design in the crowdfunding context.

The crowdfunding market has experienced rapid growth in recent years. However, not all projects are successfully financed because of information asymmetries between the founder and the providers of external finance. This shortfall in funding has made factors that lead to successful fundraising, a great interest to researchers. This study draws on the social capital theory, human capital theory and level of processing (LOP) theory to predict the success of crowdfunding projects.

A feature set is extracted and correlations between project success and features are utilized to order the features. The artificial neural network (ANN) is popularly applied to analyze the dependencies of the input variables to improve the accuracy of prediction. However, the problem of overfitting may exist in such neural networks. This study proposes a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance.

This study shows that the success of crowdfunding projects can be predicted by measuring and analyzing big data of social media activity, human capital of funders and online project presentation. The ensemble neural network method achieves highest accuracy. The investments rose from early projects and another platform by the funder serve as credible indicators for later investors.

The managerial implication of this study is that the project founders and investors can apply the proposed model to predict the success of crowdfunding projects. This study also identifies the most influential features that affect fundraising outcomes. The project funders can use these features to increase the successful opportunities of crowdfunding project.

This study contributes to apply a new machine learning modeling method to extract features from activity data of crowdfunding platforms and predict crowdfunding project success. In addition, it contributes to the research on the deployment of social capital, human capital and online presentation strategies in a crowdfunding context as well as offers practical implications for project funders and investors.

Creative performance relies on the capability of developing and presenting an original concept or idea, and the collaborative production of creative content which enhances feeling of connection with others and formation of strong community. The purpose of this paper is to apply the theory of work performance containing four dimensions (capacity, opportunities, willingness, and performance) to investigate how the capabilities of social network sites enhance user creative performance through collective social capital and information capital (opportunities) for and individual habit of use (willingness) of the user to engage in social learning process.

Many measurement items are adapted from the literature, except those measuring the constructs of social media capabilities (i.e. transmission velocity (TV), parallelism, symbol sets, rehearsability, and reprocessability) and user creative performance. The study uses survey method to collect data from social media network (SMN) users in Taiwan. Facebook is chosen as the source because it is the most prevalent and sophisticated social media platform that provides a home for users to interact and communicate. Structural equation modeling with partial least square is used to analyze the usable data collected from 533 Facebook users.

The results show that the constructs are significantly and positively correlated, meaning that social media capabilities enable social capital, information capital, and habit of use to improve user creative performance in SMNs. Three out of five social media capabilities (i.e. TV, parallelism, and rehearsability) are identified as the key enablers.

Because of the sampled surveyed subjects and the single research method, there are some limitations in this study. The research results may lack generalizability that should be taken into account when they are interpreted. The authors encourage researchers to test the proposed theoretical model further with additional subjects, variables, and linkages.

The findings of this research shed light for managers of SMN platforms on how to manage the platforms more effectively. A healthy SMN platform must implement at least these three media capabilities: the functions of news feed (i.e. TV), chat (i.e. parallelism), edit (i.e. rehearsability), in order to sustain its service.

This study confirmed that user creative performance can be increased in various ways through social capital, information capital, and habit of use. Company management should use SMNs (e.g. Facebook or Twitter) to enable employees to interact and exchange ideas and promote “coopetition” among employees across the company. If the organizational culture supports free expression of ideas and sharing of opinions, the development and robustness of group creativity can be enhanced, leading to higher competitive advantage for a company against its competitors.

Past studies related to individual creativity have mostly discussed it as a personality trait or talent; yet, personality trait or talent is implicit until it is shown by one’s behavior. Thus, for the collective performance of user creativity on SMNs, the authors elicit individual creativity through the creative performance manifested by user behavior. Furthermore, the authors confirm that social capital, information capital, and habit of use are the critical antecedents of user creative performance, and that the five social media capabilities are the enablers of social capital, information capital, and habit of use on SMNs.