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PurposeBuilding on the arguments of expectancy theory and social exchange theory, the present study provides insights into the process by which pay-for-performance (PFP) impacts employee job performance.Design/methodology/approachBased on a sample size of 226 employees working in a technology company in India, the study examines the relationships between PFP, procedural justice, organizational citizenship behavior (OCB) and employee job performance. Data on perceptions of PFP and procedural justice were collected from the employees, data on OCB were collected from the supervisors and the data on employee job performance were collected from organizational appraisal records.FindingsThe study found support for the positive relationship between PFP and job performance and for the sequential mediation of the relationship between PFP and job performance via procedural justice and OCB. Further, procedural justice was found to mediate the relationship between PFP and OCB.Research limitations/implicationsThe study was cross-sectional, so inferences about causality are limited.Practical implicationsThe study tests the relationship between PFP and employee job performance in the Indian work context. The study shows that the existence of PFP is positively related to procedural justice which, in turn, is positively related to OCB. The study found support for the sequential mediation of PFP-job performance relationship via procedural justice and OCB.Originality/valueThe study provides an insight into the underlying process through which PFP is related to employee job performance. To the best of our knowledge, such a study is the first of its kind undertaken in an organizational context.

PurposeContainer throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.Design/methodology/approachA novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.FindingsThe result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”Originality/valueA novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).

Purpose The purpose of this paper is to summarize the literature of electronic government frameworks and models to identify various constructs and their relationship to measure the performance of e-government projects. Design/methodology/approach In total, 77 publications were identified from Scopus database after using exclusion and inclusion criteria. A total of 136 constructs were mapped across five categories. Further using network science, communities of usage of these constructs across different studies were identified. Findings Dominant constructs used across studies were ease of use, usefulness, user satisfaction, infrastructure, website maturity, security, user trust, transparency, empowerment, operational efficiency, service quality and information quality. This review offers directions for future research in terms of potential for constructs, which have been explored lesser in the existing literature. Research limitations/implications The study provides direction for the usage of theoretical lenses, constructs and association among usage for the evaluation of e-government projects, which have been used less in existing literature, and thus, has higher needs for greater exploration. Search scope is limited to Scopus database, which is one of the largest citation database. Practical implications It gives information to the policymakers about the importance of the dominant constructs such as user satisfaction, usefulness, ease of use, efficiency and quality, which have been used across the spectrum of studies of e-government performance assessment frameworks and models. Practitioners need to accommodate the relevance of these factors while designing processes and key performance indicators. Originality/value This study analyzes the e-government assessment frameworks and gives direction to theory building for future studies.

Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

PurposeThe purpose of this paper is to provide a systematic literature review on the technological aspects of smart cities and to give insights about current trends, sources of research, contributing authors and countries. It is required to understand technical concepts like information technology, big data analytics, Internet of Things and blockchain needed to implement smart city models successfully.Design/methodology/approachThe data were collected from the Scopus database, and analysis techniques like bibliometric analysis, network analysis and content analysis were used to obtain research trends, publications growth, top contributing authors and nations in the domain of smart cities. Also, these analytical techniques identified various fields within the literature on smart cities and supported to design a conceptual framework for Industry 4.0 adoption in a smart city.FindingsThe bibliometric analysis shows that research publications have increased significantly over the last couple of years. It has found that developing countries like China is leading the research on smart cities. The network analytics and article classification identified six domains within the literature on smart cities. A conceptual framework for the smart city has proposed for the successful implementation of Industry 4.0 technologies.Originality/valueThis paper explores the role of Industry 4.0 technologies in smart cities. The bibliometric data on publications from the year 2013 to 2018 were used and investigated by using advanced analytical techniques. The paper reviewS key technical concepts for the successful execution of a smart city model. It also gives an idea about various technical considerations required for the implementation of the smart city model through a conceptual framework.

Purpose – The purpose of this paper is to conceptualise and discuss the possible insights that can be generated for product development by analysing the user-generated content available from various social media platforms. Design/methodology/approach – The paper reviews the role of user generated content in developing products and its features (e.g. appearance and shape). It delineates the directions in which the relationship between social media content and customer oriented concepts evolve while developing successful new products. Findings – The review and arguments presented in this paper suggest that the social media approach adds more value than the traditional approaches for obtaining insights about the products. Availability of users’ opinions and information about existing products provide insights for the improvement in the product design process. Co-creation and self-construal are important components that are based on customer engagement and customer behaviour, respectively, in the product design and development. Practical implications – As social media creates new ways of communication with users, businesses can include users into the product development process to improve and refine their products or for making the next generation of products. Originality/value – This paper suggests a new approach in getting useful insights about the products from user-generated contents. This way of using social media helps businesses to move forward from the traditional product development paradigms.

Purpose– The purpose of the present paper is to attempt to examine the determinants of citizens’ electronic participation with respect to the communication aspects. To accomplish this objective, using the extant literature, the paper delineated factors that determine and the theories that can explain citizens’ e-participation. An analysis of citizens’ democratic communication through multiple e-participation forums is carried out, and the determinants of electronic participation are described in the paper.Design/methodology/approach– In light of the literature, e-participation services were classified on the basis of characteristics of democratic communications. The factors that determine citizens’ online democratic participation were also identified and validated. Indian citizens who often e-participate were surveyed through online and offline questionnaires. A regression analysis of the 407 responses was carried out to predict the influence of individual, governance and technology components on various e-participation initiatives.Findings– Citizens’ participation efficacy, value system and participation freedom were found to determine different e-participation initiatives. Further, e-participation is also found to be varyingly determined by the governance and technology components.Research limitations/implications– The theoretical contribution of this study includes the classification of determining factors and the illustrative labeling (I, G and T) for an e-participation framework. The delineation of e-participation from democratic communication aspects also contributes to the e-participation literature. However, this research had considered only one set of e-participation services and had incorporated only select forms of e-participation that are in coherence with the services selected.Originality/value– Past studies often consider separate e-participation forums and infrequently report a simultaneous analysis of multiple e-participation forums. The factors that determine citizens’ e-participation from a democratic communication aspect are also inadequately discussed. The significant contribution of this study includes policy recommendations to improve e-participation in different information and communication technologies initiatives.

PurposeThe purpose of this study is to investigate the influence of electronization in improving the effectiveness of citizens' democratic participation in the context of e‐petitioning. With this aim, the current study worked to ascertain what influences citizens' offline and online petitioning and the extent to which electronization empowers citizens for effective e‐petitioning.Design/methodology/approachThe paper uses a case study from Sutharyakeralam meaning “Transparent Keralam” to determine the extent to which e‐petitioning worked for protecting a public irrigation canal in Kerala (India). Data were obtained through in‐depth interviews with relevant government officials, journalists and petitioners who reside near the canal. Secondary data used for the case analysis consist of petitioners' documents. Using a content analysis, this paper assesses citizens' ability to participate and influence decision making.FindingsFindings illustrate adequate citizen participation before and after the electronization of the grievance redress mechanism. Results also show if there are adequate publicizing facilities, e‐petitions can empower citizens to engage effectively in efforts to fight for their human rights.Research limitations/implicationsThe scope of the study is limited to exploring the determining parameters that may improve democratic participation in an issue of environmental pollution. Results imply that adequate policies to ensure the involvement of participants are essential to enable e‐government initiatives to deliver on the ideals of e‐democracy for equity and justice.Originality/valueEarlier studies on e‐participation were less adequate in explaining the influence of electronization on citizens' capability for effective e‐petitioning. The current study attempts to explore the enablers of effective e‐petitioning. Drawing on the canal case study, arguments are presented that explain the possible success and failure of e‐petitioning initiatives in India.

Despite extensive investigation of the Indian software industry, knowledge about small software firms is inadequate. This knowledge is important as many developing countries are contemplating the software industry as a means of national growth along the lines that India has taken. This paper provides a descriptive analysis of small software firms in India. It shows that small software firms that are located in software clusters; quality certified; low product oriented; and slightly larger tend to be more productive than others. Small software firms are defined as firms that have fewer software employees than the national median size. The paper used firm level data available in the Indian IT Software and Services Directory 2003, whose members contribute 95% of the industry revenue.