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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).

It is an open question whether or not the sharing economy model—utilizing unused for-profit private resources by sharing them for public use through a digital intermediary—could be adopted for public utility and service delivery in the developing world. This article addresses the question using the electric power sector in India as a case study. Six major components of the sharing economy are taken up for analysis: suppliers/service providers; infrastructure facilities; customers; public policy/regulations; platform; and pricing structure. This article discusses the status, challenges, and possible future directions.

The paper showcases the possible application of social media analytics in new product development (NPD). It compares users’ emotions before and after the launch of three new products in the market—a pizza, a car and a smart phone—for possible inputs for NPD. The user-generated content offers an alternative to conventional survey data and is cross-cultural in nature, relatively inexpensive and provides real-time information about user behaviour. A total of 302,632 tweets that mentioned the three new products before and after the launch were collected and analysed. Sentiment analysis of the tweets from two time periods was conducted and compared. The users’ responses to the pre- and post-launch of three products vary. The dissatisfaction with the new products represented by negative emotions aligns with the market performance. In the pre-launch period, trust and joy were more common for pizza, joy was more common for the car, and trust was more common for the phone. In the post-launch period, anger and disgust were more common for pizza, joy and trust were more common for the car, and joy was more common for only one aspect of the phone. Further analysis showed that for the car and the phone, firms need to focus on user attitudes towards product attributes, whereas for pizza, firms should concentrate on physiological changes, i.e., changes in product attributes, service and promotional sides. By using the proposed alternative approach, businesses can obtain real-time feedback about the expectations and experiences of the new products. The NPD process can be adjusted accordingly.

Twitter is one of the most comprehensive sources of public conversations over the world. As a platform, it provides a medium to a large group of audience to express their views and opinions. Owing to the snowballing of the cheaper smartphone market coupled with cheaper data services, the platform has experienced an enormous increase in its use and has become a good medium of influence. Hence, Twitter data i.e. around 1.1 lakh tweets over a period of three months (January to March, 2019) are used to extract the tweets surrounding the discussion of future of work which is the result of the emerging technologies vis-a-vis automation, IoT and Industry 4.0. to understand the perceptions of people, organizations and businesses regarding the same. Furthermore, themes surrounding the discussions are identified and technology enablers that are perceived to bring about a change in the future of work. The social media discussions can strongly influence the opinions and perceptions of people, businesses or organizations regarding Future of Work. The results of sentiment analysis exhibit that there is not much negative sentiment regarding the change in nature of work. Artificial Intelligence and Robotics are identified as the main technology enablers.

Governance of misinformation is a serious concern in social media platforms. Based on experiences gathered from different case studies, we offer insights for the policymakers on managing misinformation in social media. These platforms are widely used for not just communication but also content consumption. Managing misinformation is thus a challenge for policymakers and the platforms. This article explores the factors of rapid propagation of misinformation based on our experiences in the domain. An average of about 1.5 million tweets were analysed in each of the three different cases surrounding misinformation. The findings indicate that the tweet emotion and polarity plays a significant role in determining whether the shared content is authentic or not. A deeper exploration highlights that a higher element of surprise combined with other emotions is present in such tweets. Further, the tweets that show case-neutral content often lack the possibilities of virality when it comes to misinformation. The second case explores whether the misinformation is being propagated intentionally by means of the identified fake profiles or it is done by authentic users, which can also be either intentional, for gaining attention, or unintentional, under the assumption that the information is correct. Last, network attributes, including topological analysis, community, and centrality analysis, also catalyze the propagation of misinformation. Policymakers can utilize these findings in this experience study for the governance of misinformation. Tracking and disruption in any one of the identified drivers could act as a control mechanism to manage misinformation propagation.

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.

Social media had been extensively used for communication and networking purposes among corporates. Literature indicates social media platforms had also been used by the firms for building relationship with different stakeholders (i.e. customers, employees, investors and neighbouring communities). But there exists a gap in literature, whether social capital present on social media can be used for building corporate reputation (CR) in the society. Literature indicates corporate social responsibility (CSR) impacts CR. Therefore this study explores how CSR messages on social media impacts CR. For this study assumption was made that all the concerns and issues raised by Sustainable Development Goals (SDGs) are the social responsibilities of the CEOs. The study tries to explore this gap by analysing the tweets posted by two group of CEOs, i.e. CEOs in top 200 fortune companies and top 100 social influencer CEOs. Top 100 social influencer CEOs on social media were identified from the Hootsuite.com. Social influencer CEOs are having tremendous amount of influence on Twitter. The statistically test performed on the two group of the CEOs depicts there is significant difference in the number of CSR messages posted by fortune CEOs and social influencer CEOs. Results reveals social influencer CEOs had posted 5.97 times more CSR messages on Twitter as compared to fortune CEOs, which in turn may had led to better CR, in terms of shares and likes by social capital present on Twitter. The study reveals may be social influencer CEOs through CSR messages are trying to engage stakeholders strategically on Twitter. This is an open question at present, therefore future researchers can explore this in more details. The managerial implication of the study for CEOs and firms had been highlighted in the study.

Based on an ethnographic study of Afghan Sikh refugees in New Delhi, India, the present paper explores the role of information and communication technologies (ICTs), especially social media, in their lives and practices to survive after the forced migration. The unique identity of being Afghan Sikhs in India is largely unknown, as the notion of Afghans is attached to the Muslims and Sikhs to the Indians. The extant knowledge on literature on their narratives of forced migration and struggles of re-settlement is inadequate. The present paper employs the concept of ‘capital’ by Bourdieu to argue that ICTs enable refugees to gain different forms of capital in their journey of survival in a new country post forced migration. The paper highlights the importance of ICTs being equal to the physical infrastructure for the refugees. The digital practices provide them with a collectively owned capital in the form of relevant information and experiences of being refugees. The visibility enabled by the ICTs has contributed to their social, economic and symbolic capitals.

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.

Part 5: Big Data and Machine Learning; International audience; Researchers have been experimenting with various drivers of the diffusion rate like sentiment analysis which only considers the presence of certain words in a tweet. We theorize that the diffusion of particular content on Twitter can be driven by a sequence of nouns, adjectives, adverbs forming a sentence. We exhibit that the proposed approach is coherent with the intrinsic disposition of tweets to a common choice of words while constructing a sentence to express an opinion or sentiment. Through this paper, we propose a Custom Weighted Word Embedding (CWWE) to study the degree of diffusion of content (retweet on Twitter). Our framework first extracts the words, create a matrix of these words using the sequences in the tweet text. To this sequence matrix we further multiply custom weights basis the presence index in a sentence wherein higher weights are given if the impactful class of tokens/words like nouns, adjectives are used at the beginning of the sentence than at last. We then try to predict the possibility of diffusion of information using Long-Short Term Memory Deep Neural Network architecture, which in turn is further optimized on the accuracy and training execution time by a Convolutional Neural Network architecture. The results of the proposed CWWE are compared to a pre-trained glove word embedding. For experimentation, we created a corpus of size 230,000 tweets posted by more than 45,000 users in 6 months. Research experimentations reveal that using the proposed framework of Custom Weighted Word Embedding (CWWE) from the tweet there is a significant improvement in the overall accuracy of Deep Learning framework model in predicting information diffusion through tweets.

The dependence on information technology has evidently increased over the past decade. With the increased internet use and the need for accessing information on the go, the use of wireless internet has also been on the higher side. This need for always staying connected to the web has opened avenues for the usage of public internet services. The current study thus investigates the adoption of these public WiFi by the consumers. The study adopts the conceptual model of UTAUT2 which uses various variables to explain the acceptance of technology. The analysis uses the data procured from 257 respondents through a structured questionnaire which collects data surrounding model variables along with the demographics of the users. Variables including Facilitating Condition, Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Trust, Individual Characteristics, Business Intention and Usage are considered to be influencing the acceptance of public WiFi technology. The statistical analysis of the responses has been done by conducting tests on reliability and validity. The regression analysis and path analysis lead to the findings which may be useful by various stakeholders for decision making surrounding the promotion of their services in the sector.

Customer engagement is drastically improved through Web 2.0 technologies, especially social media platforms like Twitter. These platforms are often used by organizations for marketing, of which creation of numerous spam profiles for content promotion is common. The present paper proposes a hybrid approach for identifying the spam profiles by combining social media analytics and bio inspired computing. It adopts a modified K-Means integrated Levy flight Firefly Algorithm (LFA) with chaotic maps as an extension to Firefly Algorithm (FA) for spam detection in Twitter marketing. A total of 18,44,701 tweets have been analyzed from 14,235 Twitter profiles on 13 statistically significant factors derived from social media analytics. A Fuzzy C-Means Clustering approach is further used to identify the overlapping users in two clusters of spammers and non-spammers. Six variants of K-Means integrated FA including chaotic maps and levy flights are tested. The findings indicate that FA with chaos for tuning attractiveness coefficient using Gauss Map converges to a working solution the fastest. Further, LFA with chaos for tuning the absorption coefficient using sinusoidal map outperforms the rest of the approaches in terms of accuracy.

There is criticism that information and communication technology and development research community contributes less to policy-making. The present article shares an attempt to fill this gap. Systematic review is a structured way to collate, review, summarize, and use the published research for policy-making. A systematic review on the impact of information and communication technologies on the growth of urban micro, small, and medium enterprises in the low- and middle-income countries indicates a tiny impact and a lack of rigorous causal studies in this focus area. Quantitative meta-analysis did not find statistical significance for the impact. From an initial result of 24,000 plus hits, a set of 10 research studies were finalized for data extraction and analysis. The article suggests areas for future research and calls for rigorous research and reporting.

User generated content in the social media platforms are being considered as an important source for information about consumers and other emerging trends by the businesses. Using Twitter analytics, the paper presents insights on trends and discussions about the Internet of Things (IoT). Using relevant hashtags, 40,387 tweets were collected in early 2016. The analysis had followed three major approaches: descriptive analysis, content analysis and network analysis. The tools R and NodeXL were used for the analysis. The findings showed major themes like business concerns, scope of applications, security, emerging smart technologies and manufacturing. The sentiments of emotions and polarity differed across these themes. The top individual and industrial influencers were identified. The analysis also detected the highly-associated words and hashtags, and different user communities and how they are connected. Business implications of the findings and limitations are also elaborated.

Despite the popularity of mobile applications, the research regarding their usage and non-usage is scarce. With increasing competition, the Application Life Cycle (ALC) is becoming smaller for a number of applications. The purpose of this paper is to study and identify the factors that lead to uninstallation of an application or smaller ALCs. User preference of mobile phone application depends on Telecom infrastructure, Smartphone hardware specifications, the User interface of applications, Data privacy and security aspect of mobile application, and usage of the application based on trends and advertisements. A questionnaire was prepared to identify the factors related to ALC, further hypotheses were tested to reach a conclusion. The study assisted in finding the factors for the decline of mobile application life cycle as well as its non-usage and thus helps to understand the user behavior.

© 2019 Elsevier Ltd The present paper explores the relationship between nature of Facebook usage, non-directional comparisons and depressive syndromes. The extant research on linkage between social media usage and mental health is inconclusive. The paper uses data collected through an online survey of 399 Facebook users in the UK. A Facebook frequency rating scale was developed and validated as a part of the study. The Iowa-Netherlands Comparison Orientation Measure was modified and used to measure social comparison. The depressive syndromes were captured by the modified Center for Epidemiological Studies Depression Scale. The Rank Theory of Depression was used a guiding framework. The data collection had focused on the 20–29 year olds, as justified by the literature. The study found a negative relationship between active Facebook use and non-directional social comparisons. The relationship was reversed in the case of passive usage. There is small but significant causal linkage between increased non-directional social comparisons and depressive symptoms among the users.

Part 10: Security in Digital Environments; International audience; Despite high quality information systems security in place, organizations are vulnerable to cyber-attacks due to lapses in the human behavior. The present paper explores the importance of human factors in cyber security using an online survey data. It uses the work of Parson, Calic, Pattenson, Butavicius, McCormac and Zwaans [23] in measuring the human aspects of cyber security (leaving printouts, links from known source, website access, information in website, password complexity, links from known source, plugging USB in public places) and their linkages with the demographic factors (age, work experience, academic discipline, qualification, and place). ANOVA was used on a sample size of 165. It was found that demographic profile of employees and students significantly differ in their perception towards the cyber security. The paper has suggestions for information security awareness training programmes to handle the inadequacies.

User generated content in the social media platforms are being considered as an important source for information about consumers and other emerging trends by the businesses. Using Twitter analytics, the paper presents insights on trends and discussions about the Internet of Things (IoT). Using relevant hashtags, 40,387 tweets were collected in early 2016. The analysis had followed three major approaches: descriptive analysis, content analysis and network analysis. The tools R and NodeXL were used for the analysis. The findings showed major themes like business concerns, scope of applications, security, emerging smart technologies and manufacturing. The sentiments of emotions and polarity differed across these themes. The top individual and industrial influencers were identified. The analysis also detected the highly-associated words and hashtags, and different user communities and how they are connected. Business implications of the findings and limitations are also elaborated.

Using telephonic survey data collected from 11,707 unemployed females in India, the present paper examines the linkage between the digital literacy program and its impact (perceived ICT competence, seeking education and employment opportunities) and role played by the perceived value of the training.

Although blockchain has attracted a great deal of attention from academia and industry there is a lack of studies on acceptance drivers. This study explores blockchain acceptance by mining the collective intelligence of users on Twitter. It maps blockchain user acceptance drivers to technology acceptance constructs. The analysis shows that users are attracted by security, privacy, transparency, trust and traceability aspects provided by blockchain. On Twitter more discussions on blockchain benefits than on drawbacks. Initial coin offering (ICO) is extensively discussed. The study provides guidelines for managers and concludes by presenting the limitations of the study along with future research directions.

With the rise in cooling demand and the permeation of decentralised renewable energy resources in electricity networks, electricity demand-side management (DSM) has become a major tool for electricity planning and decarbonisation in the Global South. In India, the commercial application of DSM is not new, yet utility-driven residential-scale demand response (DR) remains an unexplored area. This paper contributes on two fronts – to explicate householders and practitioner's perceptions of DR: disjunctions between these perceptions and its implications for the acceptance of residential DR. Using a co-production approach, this paper draws insights from two sets of stakeholders in India - 25 DR policy and utility experts and 24 household consumers. Our results show that technological saviourism pervasively underscores practitioners understanding of DR and householder agency, a crucial factor in the adoption of DR at the residential scale remains a missing piece. The paper concludes that without considering householder agency, delivering a decarbonised future based on demand response will be challenging and consumers may remain locked into-existing socio-cultural practices that negate the adoption of DR.

Part 3: Analytics for Smart Governance; International audience; The paper argues for use of Twitter analytics in the study of public policy. Twitter as a social media platform is widely used by many users to express their emotions on various issues. Governments are now realizing the need for social media for administrative purposes and information proliferation to the general public. However, the extent of knowledge among policy makers, about gathering insights from citizen conversations on various social media platforms needs improvement. This research discusses Twitter analytics for two different use cases namely ‘Demonetization’ and ‘GST’, where citizen engagement and conversations are monitored, to gain insights. These use cases vary in terms of focus, volume, velocity, veracity, time frame, location, and networked-ness. Possible inferences are presented to gain insights and to help in the policy making process.

The paper uses a large scale national quantitate dataset to provide a fresh perspective on linkages between the digital divide and social inequalities in India. Commencing from two classical social theoretical perspectives – the Marxian conflict perspective and the Weberian cultural perspective – as starting points, we employed MANCOVA analysis on data of more than forty thousand households. The Marxian perspective reveals how information and communication technologies (ICT) assets come to be owned, and how they are used with the help of specific digital skills by the working class to influence labour markets and intergenerationally transfer class advantage. The Weberian perspective focuses on how ownership and use of ICT assets define an elite lifestyle which excludes others. The analysis is deepened by employing network effects approach. Results from data analysis indicate that the less educated, lower-income group, and lower caste strata are further marginalised as they neither have ICT assets nor the skills to use them. Households whose primary source of income is organised business own and use their ICT assets more than those that depend on agricultural or non-agricultural wage labour. Also, the highest adult education in a household, caste, and the primary source of income of the household differentiate ICT ownership and use. Overall, ICT ownership and usage are significantly different for different socio-economic groups in India. This linkage demonstrates how inequalities are aggravated in the manner of a Matthew Effect. The paper suggests that ICTs may end up amplifying the social inequalities in India.

Digital marketing is often detrimental if it is done by unskilled service providers.The hyped services of search engine marketing are not as successful as they seem to be.SEM services provided by small organizations and freelancers are generally detrimental.SEM services provided by established players are relatively more beneficial and reliable. The study highlights how digital marketing is often detrimental, when it is done by unskilled service providers. It highlights how the hyped services of search engine marketing (SEM) are not as successful as they seem to be and sometimes affect firms negatively. This study uses social media analytics to derive insights from Twitter using descriptive, content and network analytics. Methods like hashtag analysis, polarity and emotion analysis, word analysis, topic modeling and other relevant approaches have been used to mine user generated content. A qualitative case study on an e-market is used for validation of findings. SEM services provided by small organizations and freelancers are not as beneficial as the ones by established players. The services provided by these firms proved detrimental for the customers based on user experiences surrounding these services in the social media and forum specific discussions. This study highlights how SEM often not only fails to provide benefits but also destructs value if not done properly. Transaction costs like agency problems, coordination costs, loss of non-contractible value and cost of fit are also identified with potential fallouts which affect the long-term benefits. Inputs will be beneficial to practice in planning SEM and outsourcing.

In the current era of digitization and the rise of e-commerce sector over the past decade, it becomes essential to identify and address issues surrounding e-commerce logistics. The current study addresses the problem of location based inventory routing for delivery from warehouse to various destinations. The study uses a dynamic meta-heuristic approach, Ant Colony Optimization (ACO), to solve this problem in e-commerce. A sensitivity analysis is done to identify the coefficient values to be fixed for the ACO Engine. The longitude and latitude coordinates from Google Maps are mapped to distance metrics between the warehouse and the delivery destinations for orders to be delivered on a particular day. The analysis is conducted for a total of 346 locations and the total distance to be travelled came out to be 690.29 km using the proposed approach. Findings of the study can be applicable in multiple domains of e-commerce logistics and other graph based routing problems.

The paper presents a comprehensive conceptual framework for assessing the performance of e-government projects on the basis of review of literature. The review shows that the researches and studies of performance evaluation of e-government are mostly focusing on citizens as stakeholders of e-government. The other stakeholders' context is not much part of the available e-government research literature. The review identified a total of 126 constructs from 64 studies, which were further classified into five categories, namely, people, technology, policy, economic, and institution. Validation of the relevance of these constructs were done through two qualitative case studies in India. Empirical validation of these constructs in under progress.

This paper shares the experiences of creating a toolkit for women microentrepreneurs in India. The toolkit is designed as a self-learning resource that introduces mobile applications for use in micro-businesses owned by women. Porter's value chain framework was used to select the mobile apps. Each application was then explained in a form of a story that is derived from the context of potential users. The color and text of the content was selected to appeal to the women with low literacy rates. This final toolkit is an open source resource that can be used freely by anyone for non-commercial usage. The toolkit has potential to be adopted in training programmes for the women microentrepreneurs.

Part 4: Social Media and Web 3.0 for Smartness; International audience; Elections are the most critical events for any nation and paves the path for future growth and prosperity of the economy. Due to its high impact, a lot of discussions take place among all stakeholders in social media. In this study, we attempt to examine the discussions surrounding USA Election, 2016 in Twitter. Further we highlight some of the domains influencing the voter behaviour by applying the outcome of Twitter analytics to Newman and Sheth’s model of Voter Choice. Through the analysis of 784,153 tweets from 287,838 users over 18 weeks, we present interesting findings on what may have affected the polarization of USA elections.

Part 3: Analytics for Smart Governance; International audience; The ever-increasing need and interest of the consumers in online purchases has been a major driver in shaping the persistent and prominent role of e-commerce industry. Business – Consumer trust is a very important factor which should be harnessed for the well-being of this e-commerce community. Such a relationship can be easily hampered owing to the fraudulent behavior of either the consumer or the seller. From a consumer point of view, placement of fake orders can be considered as a breach of trust relationship which may attribute to the losses incurred to the e-commerce industry. Fake orders are those orders which are intentionally cancelled post the packaging and shipment stage. Motivated from this idea, this paper proposes an efficient mechanism to mitigate the fake orders, thereby, reducing the loss of packaging and shipment cost and improving the Business – Consumer relationship. The necessity to reduce the number of fake orders has been discussed eloquently in this paper. A qualitative analysis has been performed to elucidate the efficiency of the proposed mechanism. This mechanism, if implemented, has the potential to mitigate the fake orders which will inherently increase the profit of a company.

Part 5: Smart Solutions for the Future; International audience; The introduction and wide-spread adaptation of e-mobility influences emergency and roadway breakdown service firms. Service complexity and service productivity are important constructs to measure and monitor where the industry is headed and where firms need to quickly adapt.The purpose of this paper is to investigate effects on service complexity and service productivity of emergency and roadway breakdown services on the back of a growing market penetration of electric vehicles in industrialized countries.The study is grounded on expert interviews and a quantitative analysis of 325 questionnaires that have been distributed among roadside assistant service providers in Germany. It reveals the increasing complexity of an important service industry that had only been studied scarcely in the past. With its large-scale quantitative design, it adds significantly to the body of knowledge available and can be the basis for further advanced research in other geographical settings. The research supports practitioners in the field of roadside assistant services in rethinking their service offering and the handling of increasingly heterogeneous propulsion technologies such as electric drives. In order to keep the service productivity high and the complexity low, assistance systems and targeted qualification and training concepts for the roadway personnel have to be developed and rolled out.The research setting is focused on the German market, but links are provided to apply the study design to other countries and further investigate respective market singularities.

Driven by the increasing popularity of social commerce sites, this study seeks to examine the information sources and formats that influence consumer intentions to purchase. Specifically, we build on uses and gratifications theory and dual-process theory to determine how user-generated content and marketer-generated content are consumed by users when making a purchase decision. Using an eye-tracking approach on a popular social commerce site with a sample of 23 consumers, we find significant differences in the types of information used for product purchase compared to those omitted. Our study demonstrates that the format and source of information that consumers utilize, as well as the gaze transitions they make between different types of content when browsing, follow different patterns depending on if a product is bought or rejected. We conclude the paper summarizing the findings and drawing theoretical and practical implications that arise. This is a post-peer-review, pre-copyedit version of an article published in [Conference on e-Business, e-Services and e-Society] Locked until 4.10.2018 due to copyright restrictions. The final authenticated version is available online at: https://link.springer.com/chapter/10.1007%2F978-3-319-68557-1_34

Part 1: Adoption of Smart Services; International audience; Although e-commerce has been adopted and developed rapidly in rural areas in China during the past two years, serious issues have been encountered as well. Practitioners and scientists proposed lists of barriers. However, such lists differ with each other for different regions and at different time. Fixed lists may not help much practically. Present research proposes a framework named N3F4 to structure and formalize such barriers. Based on the framework, researchers could make a list of barriers for a given region, perform surveys among interested people, prioritize the barriers, analyze reasons and propose solutions. In this paper, we introduce the N3F4 framework, and present a case study of applying the framework in one small county in China. The result shows that the N3F4 framework helps structure barriers before performing the survey, and it also helps analyze the result and come up with solutions afterwards, both effectively and efficiently.

Part 4: Social Media and Web 3.0 for Smartness; International audience; Owing to the inclination towards e-Commerce, the importance of consumer reviews has evolved significantly. Potential consumers exhibit sincere intents in seeking opinions of other consumers who have already had a usage experience of the products they are intending to make a purchase decision on. The underlying businesses also deem it fit to ascertain common public opinions regarding the quality of their products as well as services. However, the consumer reviews have bulked over time to such an extent that it has become a highly challenging task to read all the reviews, even if limiting to only the top ones, to reach an informed purchase decision or have an insight regarding how satisfied or not the consumers of a particular product are. Since most of the reviews are either unstructuctured or semi-structured, information classification is employed to derive knowledge from the reviews. However, most classification methods based on sentiment orientation of reviews do not detect which features of a product were specifically liked or disliked by a reviewer. This constitutes itself into another problem since most consumers are on the lookout for certain prerequisite features while viewing the products. The paper proposes a Consumer Satisfaction Rating System (CSRS) based on sentiment analysis of consumer reviews in context of the features of a product. The system aims at providing a summary that represents the extent to which the consumers were satisfied or unsatisfied with the specific features of a product.

Part 2: Assessment of ICT enabled Smart Initiatives; International audience; Internet of Things – Aerospace (IoTA): IoT in Aerospace sector? Is it really happening? This is what industry experts think while talking about Internet of Things in Aerospace! IoTA is the Ninth letter of Greek alphabet, which means ‘extremely small amount’. Will it impact the nine letter ‘AEROSPACE’ industry? In the Aerospace & Defense industry, split-second decisions can mean a difference between success and failure as there is longer product life cycle.This exploratory research is targeted towards identifying the various IoT characteristics and its maturity level. A methodology was developed using House of Quality (HoQ) to identify the potential system which will be impacted by IoT in future. HoQ helped in identifying the positive and negative correlation between the IoT characteristics and its linkage with various aerospace systems. This investigative research can be used by aerospace system suppliers to make qualitative decisions before investing in IoT for their system/product enhancements.

Part 1: Adoption of Smart Services; International audience; Digital payments (mainly enabled by mobile devices) have huge potential to change lives of millions of people in developing countries by offering financial services to the unbanked masses. Despite its potential digital payment methods have not been widely and successfully adopted in the developing countries. In order to ascertain the various drivers and inhibitors behind digital payment adoption, this study did a review of research on digital and mobile payment adoption and use. Results of this literature analysis revealed performance expectancy/perceived usefulness as most significant determinant of consumer’s behavioral intention to use mobile payments followed by perceived ease of use (PEOU). Perceived risk was found as major inhibitor to the adoption of mobile payments. Also majority of studies employed TAM and its extension to understand consumer adoption to mobile payment followed by UTAUT.

Part 2: Assessment of ICT enabled Smart Initiatives; International audience; This paper profiles the research activities that have been published on e-government adoption in the context of South Asian Association for Regional Cooperation (SAARC) countries. Our analysis includes variables such as the years of publication, journals most often publishing papers on e-government adoption, countries in which the research activity was conducted, the authors most active in the subject area, keywords analysis, methodological analysis, technology and respondents contexts, analysis of theories or models used and analysis of limitations and future research directions extracted from 48 research papers that were extracted from Scopus database using some key terminologies related to e-government, adoption and eight SAARC countries. This is the first study that has comprehensively examined the analysis of e-government adoption literature in these eight countries’ context. The results of this research provide some promising lines of inquiry that have been largely neglected along with those that have received a much larger attention.

Prompted by the popularity of social commerce in the past few years, this study seeks to examine how online reviews influence consumer’s tendency to engage in word-of-mouth (WOM). We investigate how different aspects pertinent to online reviews affect consumers trust, and how that in turn induces WOM passing and WOM giving. The moderating influence of network ties is studied in the trust to WOM relationship. Building on survey-based study design with a sample of 385 social commerce consumers, we that specific aspects induce a sense of trust towards vendors. In turn, our study demonstrates that trust positively influences WOM passing and WOM giving and this relationship is amplified in conditions of strong network ties. We conclude the paper summarizing the findings and drawing theoretical and practical implications that arise. This is a post-peer-review, pre-copyedit version of an article published in [Conference on e-Business, e-Services and e-Society] Locked until 4.10.2018 due to copyright restrictions. The final authenticated version is available online at: https://link.springer.com/chapter/10.1007%2F978-3-319-68557-1_31