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Chatbots are being used in an increasingly wide range of public services. Chatbots present several benefits for public service delivery (e.g. cost efficiency for public organizations and better service accessibility for service applicants). However, obtaining effective service outcomes from a chatbot-mediated service delivery requires designing a suitable type of chatbot for specific types of public services. We (a) classify different types of public services in terms of complexity of their delivery process; (b) classify chatbots based on their technical properties and capabilities; and (c) propose a typology that supports public organizations with deciding what type of chatbot is suitable for what type of public services. Our typology contributes to effective chatbot-mediated service delivery in the public service context.

Since 2015, several countries have shown significant interest in artificial intelligence (AI) and have released national-level AI strategic plans. These plans reflect the country’s rationale for embarking on AI. To identify what factors influence the AI approach of a country, this study employs the signaling theory to decode strategic national AI plans and understand each country’s rationale. The study adapts the typology of signals and plots AI information given in national AI plans (AI-enabled public services, research, data, algorithmic ethics, governance) in a matrix of intentionality and veracity considering socio-economic and political conditions. Our findings indicate that countries with high democracy scores are more likely than less democratic countries to prioritize ethical and governance issues of AI, however, this is more pronounced in democratic countries with a lower technology base. The results also suggest that advanced research capability and data accessibility for AI is a precondition to developing a nationwide AI system.

As the sophistication of artificial intelligence (AI) systems develop and AI becomes a key element of organizational strategy across a wide spectrum of industries, new demands are being placed on senior leaders. To understand the growing challenges leaders will face in the age of AI, we conducted interviews with 33 senior leaders in several countries across a wide range of industries. Our research highlights key capabilities and skills that leaders will require. Underlying these capabilities is a mindset oriented toward continuous learning and self-development, which will enable ongoing and rapid adaptation to change. Our findings identified the following key capabilities: digital know-how, data-driven focus, networking, ethics, and agility. To successfully navigate the coming era, senior leaders will need to focus on reskilling the workforce, recruiting and retaining highly skilled talent, building an intrapreneurial culture, and managing unprecedented changes in technologies and the nature of work.

Rationale: Ivacaftor's clinical effects in the residual function mutations 3849+10kb C →T and D1152H warrant further characterization. Objectives: Evaluate ivacaftor's effect in people with cystic fibrosis aged ≥6 years with 3849+10kb C→T or D1152H residual function mutations; explore the correlation between ivacaftor-induced organoid-based cystic fibrosis transmembrane conductance regulator function measurements and clinical response to ivacaftor. Methods: Participants were randomized (1:1) in this placebo-controlled crossover study; each treatment sequence included two 8-week treatments with an 8-week washout period. The primary endpoint was absolute change in lung clearance index2.5 from baseline through Week 8. Additional endpoints included lung function, patient-reported outcomes, and in vitro intestinal organoid-based measurements of ivacaftor-induced cystic fibrosis transmembrane conductance regulator function. Results: Of 38 participants, 37 completed the study. The primary endpoint was met; the Bayesian posterior probability of improvement in lung clearance index2.5 with ivacaftor vs placebo was >99%. Additional endpoints improved with ivacaftor. Safety findings were consistent with ivacaftor's known safety profile. Dose-dependent swelling was observed in 23/25 viable organoid cultures with ivacaftor treatment. Correlations between ivacaftor-induced organoid swelling and clinical endpoints were negligible to low. Conclusions: In people with cystic fibrosis aged ≥6 years with a 3849+10kb C →T or D1152H mutation, ivacaftor treatment improved clinical endpoints vs placebo; however, there was no correlation between organoid swelling and change in clinical endpoints. The organoid assay may assist in identification of ivacaftor-responsive mutations but in this study did not predict magnitude of clinical benefit for individual people with cystic fibrosis with these two mutations. Clinical trial registered with ClinicalTrials.gov (NCT03068312).

Cognitive Computing Systems (CCSs) are increasing in prominence in the public sector. This paper develops a framework drawing on public value and information technology service management literatur...

Records management in the public sector is integral for delivering public good. However, several institutional challenges inhibit the required implementation of innovative and information‐...

Nations have recognized the transformational potential of artificial intelligence (AI). Advances in AI will impact all facets of society. A spate of recently released national strategic AI plans provides valuable insights into how nations are considering their future trajectories. These strategic plans offer a rich source of evidence to understand national-level strategic actions, both proactive and reactive, in the face of rapid technological innovation. Based on a comprehensive content analysis of thirty-four national strategic plans, this article reports on (1) opportunities for AI to modernize the public sector and enhance industry competitiveness, (2) the role of the public sector in ensuring that the two most critical elements of AI systems, data and algorithms, are managed responsibly, (3) the role of the public sector in the governance of AI systems, and (4) how nations plan to invest in capacity development initiatives to strengthen their AI capabilities.

Introduction Mutations in the cystic fibrosis transmembrane conductance regulator gene (CFTR) affect the quantity and/or function of CFTR protein reaching the cell surface. Ivacaftor, a CFTR potentiator that enhances chloride transport, increases the channel-open probability of normal and dysfunctional CFTR. Initially approved for people with CF (pwCF) with G551D-CFTR gating mutations, ivacaftor demonstrated clinical benefit in pwCF with other gating mutations and certain residual function mutations, including R117H-CFTR, in clinical studies. We evaluated the long-term safety and efficacy of ivacaftor in pwCF aged 6 years and older with non-G551D-CFTR ivacaftor-responsive mutations. Methods Efficacy and safety data from a phase 3, multicenter, open-label, extension study for participants from Study 110 (R117H-CFTR mutations), Study 111 (non–G551D-CFTR gating mutations), and Study 113 (n-of-1 pilot study in participants with residual CFTR function) were analyzed. Following washout from the randomized parent study, participants received oral ivacaftor 150 mg once every 12 h for 104 weeks. Results Forty-one of 121 participants completed treatment through 104 weeks; 59 participants who did not complete the extension study continued treatment with commercial ivacaftor. The most common adverse events were pulmonary exacerbation (46.3%) and cough (33.9%). Most treatment-emergent adverse events were mild/moderate in severity and consistent with manifestations of CF or the ivacaftor safety profile. Rapid, durable improvement occurred across all efficacy endpoints. Conclusions Ivacaftor was generally safe and well tolerated with no new safety concerns for up to 104 weeks in pwCF with ivacaftor-responsive mutations. The pattern of improvement across efficacy endpoints was durable and generally consistent with parent-study outcomes. Trial Registration NCT01707290

As the public sector increasingly moves into the digital age and tries to take advantage of previous Information Technology (IT) innovations, a more systematic approach is needed to envision the po...

In this paper, we examine the understudied issue of the pathways to smart cities. While the extant literature on smart cities offers several insights into what smart cities are, with a few notable ...

Artificial intelligence applications in cognitive computing systems can be found in organizations across every market, including chatbots that help customers navigate websites, predictive analytics systems used for fraud detection, and augmented decision-support systems for knowledge workers. In this article, we share reflections and insights from our experience with AI projects in the public sector that can add value to any organization. We organized our findings into four thematic domains—(1) data, (2) technology, (3) organizational, and (4) environmental—and examine them relative to the phases of AI. We conclude with best practices for capturing value with cognitive computing systems.

This paper exploits novel data and empirical methods to examine parental preferences for child care. Specifically, we analyze consumer reviews of child care businesses posted on the website Yelp.com. A key advantage of Yelp is that it contains a large volume of unstructured information about a broad set of child care programs located in demographically and economically diverse communities. Thus our analysis relies on a combination of theory- and data-driven methodologies to organize and classify the characteristics of child care that are assessed by parents. We also use natural language processing techniques to examine the affect and psychological tones expressed in the reviews. Our main results are threefold. First, conditional on contributing a Yelp review, consumers overall are highly satisfied with their child care provider, although those in higher-income markets are substantially more satisfied than their counterparts in lower-income markets. Second, the program characteristics most commonly evaluated by consumers relate to safety, quality of the learning environment, and child-teacher interactions. However, those in lower- and higher-income markets evaluate different characteristics in their reviews. The former is more likely to comment on a program’s practical features, such as its pricing and accessibility, while the latter is more likely to focus on the learning environment. Finally, we find that consumers in lower-income markets are more likely to display negative psychological tones such as anxiety and anger in their reviews, especially when discussing the nature of their interactions with program managers and their child’s interactions with teachers.

Public agencies have a strong interest in artificial intelligence (AI) systems. However, many public agencies lack tools and frameworks to articulate a viable business model and evaluate public value as they consider investing in AI systems. The business model canvas used extensively in the private sector offers us a foundation for designing a public AI canvas (PAIC). Employing a design science approach, this study reports on the design and evaluation of PAIC. The PAIC comprises three distinctive layers: (1) the public value-oriented AI-enablement layer; (2) the public value logic layer; and (3) the public value-oriented social guidance layer. PAIC offers guidance on innovating the business models of public agencies to create and capture AI-enabled value. For practitioners, PAIC presents a validated tool to guide AI deployment in public agencies.

The ability to recover from disruptions is important for organizations and supply chains. Empirical data were used to investigate factors that affect supply chain recovery from disruptions, including collaboration, visibility, flexibility, analytical orientation, and supply chain risk management. A literature review was conducted to build an online questionnaire that was applied to manufacturing firms in Brazil. This work’s statistical method includes confirmatory factor analysis and structural equation modeling. Our results indicate that a package of resilience capabilities - collaboration, flexibility, visibility, and analytical orientation - positively affect supply chain resilience. Improving such capabilities, therefore, will allow supply chains to recover better from disruptions. It was also discovered, however, that supply chains do not recover from disruptions by way of supply chain risk management alone. Mutual impacts also exist between the group of resilience capabilities and supply chain risk management. RESUMEN La capacidad de recuperarse de las disrupciones es un factor importante para las organizaciones y sus cadenas de suministro. Se utilizaron datos empíricos para investigar los factores que afectan la recuperación de las cadenas de suministro ante disrupciones, como la colaboración, la visibilidad, la flexibilidad, la orientación analítica y la gestión de riesgos. Se realizó una revisión de la literatura para elaborar el cuestionario que se aplicó a las industrias manufactureras en Brasil. Los métodos estadísticos utilizados incluyen el análisis factorial confirmatorio y el modelado de ecuaciones estructurales. Los resultados indican que el paquete de capabilidades de resiliencia‒ colaboración, flexibilidad, visibilidad y orientación analítica‒ afecta positivamente la resiliencia de la cadena de suministro. Por lo tanto, mejorar estas capabilidades permitirá que las cadenas de suministro se recuperen más adecuadamente de las disrupciones. También se descubrió que la gestión de riesgos por sí sola no favorece la recuperación de las disrupciones. Además, existe un impacto mutuo entre las capacidades de resiliencia y la gestión de riesgos. RESUMO A habilidade em se recuperar de interrupções é um importante fator para as organizações e suas cadeias de suprimentos. Dados empíricos foram utilizados para investigar fatores que impactam a recuperação das cadeias de suprimentos em face de interrupções, como a colaboração, visibilidade, flexibilidade, orientação analítica e a gestão de riscos. Uma revisão de literatura foi conduzida para desenvolver o questionário que foi aplicado em indústrias de transformação do Brasil. Os métodos estatísticos utilizados incluem a análise fatorial confirmatória e a modelagem de equações estruturais. Os resultados indicam que o pacote de capabilidades em resiliência – colaboração, flexibilidade, visibilidade e orientação analítica - impacta positivamente a resiliência das cadeias de suprimentos. Assim, aprimorar essas capabilidades permitirá uma recuperação mais adequada de interrupções. Também se constatou que a gestão de riscos, sozinha, não favorece a recuperação de interrupções. Adicionalmente, existe um impacto mútuo entre as capabilidades em resiliência e a gestão de riscos.

The ability to recover from disruptions is important for organizations and supply chains. Empirical data were used to investigate factors that affect supply chain recovery from disruptions, including collaboration, visibility, flexibility, analytical orientation, and supply chain risk management. A literature review was conducted to build an online questionnaire that was applied to manufacturing firms in Brazil. This work’s statistical method includes confirmatory factor analysis and structural equation modeling. Our results indicate that a package of resilience capabilities - collaboration, flexibility, visibility, and analytical orientation - positively affect supply chain resilience. Improving such capabilities, therefore, will allow supply chains to recover better from disruptions. It was also discovered, however, that supply chains do not recover from disruptions by way of supply chain risk management alone. Mutual impacts also exist between the group of resilience capabilities and supply chain risk management.

Both citizens and public servants have both voluntary and mandatory interactions with government through technology and these interactions range from unstructured to highly structured based on the technology or business processes implemented. Using data on the 50 U.S. states, we develop taxonomies of IT-enabled innovation along two dimensions – volitional vs non-volitional and structured vs unstructured – for both internal (employee-facing) and external (citizen-facing) investments. Using configurational analysis, we link portfolios of IT-enabled innovation investments to internal and external performance outcomes and find that a broad-based approach to IT-enabled innovation leads to the best outcomes while lack of investment hinders performance. We also find that for internal-focused innovation, unstructured innovations – whether volitional or non-volitional – tend to lead to better outcomes than structured innovations, while for external-focused innovation the most successful innovations noted were structured or volitional or both. As such, our findings highlight the differences between employee- and citizen-facing IT-enabled innovation, helping to guide policy makers and future researchers.

The COVID-19 pandemic, a worldwide health and humanitarian crisis, has created unique challenges for citizens, governments, and organizations alike. Business leaders ask ‘what is the new normal, post-pandemic?’ while industries become more complex and uncertain. Premises sit empty, employees work remotely, and customers possess less disposable income. However bleak, opportunities do exist. Organizations will, however, need to transform. Technology will need to be used to respond to the pandemic, and organizations must re-structure to better function. This paper examines the role that digital technologies can play in responding to pandemics and outlines four classifications of digital technologies for pandemic response. An as-is/to-be pathway analysis is presented using case studies from Chinese enterprises to provide trajectory guidance for moving forward to the new normal. After analysis from an incorporated technology-centric and business model-based framework perspective, six fundamental enterprise strategies are presented that are derived from literature and empirical observations.

This panel explores the development of innovative, integrative, and versatile strategies to facilitate more practical and effective use of intelligent cyber-physical technologies from a variety of perspectives, including engineering, regulation, management, and research. With the same goal of sustaining the development of emerging technologies to best benefit our communities, this panel shares their different approaches in terms of engineering solutions for real-time controlling in cyber-physical systems, regulatory strategies to overcome the conflict between efficiency and autonomy, artifacts for artificial intelligence project management, and meeting researcher needs through large-scale cyberinfrastructure. The selected cases discussed in this panel not only highlight the critical challenges in implementing cyber-physical technologies into real applications but also suggest promising strategies to overcome those issues from diverse facets.

Emergencies continue to become ever more complex; responding to them, therefore, often is beyond the capabilities and capacities of any single public agency. Hence, collaboration among these actors is necessary to prepare for, respond to, and recover from such events. This seldom occurs in an effective or efficient manner, however. Drawing on resource dependence theory and the concept of social capital, this paper reveals that different types of collaborative relationships exist within the collaborative network. Super Ministry Reform of Emergency Management in China serves as a case in point. By evaluating network efficiency and classifying the collaborative relationships of involved government agencies, four types are identified: resource-redundant; resource-complementary; resource-dependent; and resource-isolated. The different influences of collaborative relationships explain why the reform is not that effective, although it has led to the merger of several core departments in the emergency management network. The findings are a reminder to consider network structure and collaboration types when engaging in institutional design.وما زال تعقيد حالات الطوارئ في ازدياد وبالتالي فإن الاستجابة لها غالبًا ما تتجاوز قدرات وإمكانيات أي وكالة عامة واحدة. ومن ثم، فإن التعاون بين الوكالات العامة هو الإعداد لحالات الطوارئ والاستجابة لها والتعافي منها. غير أن التعاون بين الوكالات العامة نادرًا ما يتم بطريقة فعالة أثناء حالات الطوارئ. مع مناقشة نظرية الاعتماد على الموارد وراس المال الاجتماعي. نعتقد أن هناك أنواع مختلفة من العلاقات التعاونية في الشبكة التعاونية، بالاعتماد على هيكل الشبكة. تأخذ هذه الورقة إصلاح الوزارة العليا لإدارة الطوارئ في الصين كدراسة حالة. نجد أربعة أنواع مختلفة: الموارد الزائدة وتكامل الموارد والاعتماد على الموارد وعزل الموارد وذلك من خلال تقييم كفاءة الشبكة وتصنيف العلاقات التعاونية للوكالات الحكومية ذات الصلة. وتفسر التأثيرات المختلفة للعلاقات التعاونية بدقة سبب عدم فعالية الإصلاح وذلك بالرغم من دمج عدة إدارات أساسية في شبكة إدارة الطوارئ. وتذكرنا هذه النتائج بالنظر في هيكل الشبكة وأنواع التعاون عند تصميم المؤسسات. كلمات مفتاحية :ادارة الطوارئ , التعاون بين الوكالات , تحليل الشبكات الاجتماعية.紧急情况变得越来越复杂，因此仅凭单一公共机构的能力和实力，往往不足以应对紧急状况。公共机构之间合作，是为了随时准备应对紧急情况，并在状况出现后即时恢复。然而，在紧急情况出现时，公共机构之间的合作很少奏效或者有效率。通过对资源依赖理论和社会资本的讨论，我们认为由于网络结构的不同，协作网络中存在不同类型的协作关系。本文以中国应急管理部改革为例。在对网络效率进行评估，对政府机构的协作关系进行分类后，我们得到了四种不同的协作类型：资源冗余型、资源互补型、资源依赖型和资源孤立型。不同协作关系的影响不同，因此即使合并了应急管理网络中的几个核心部门，改革却不够有效。因此，在建立制度时，要考虑到网络结构和协作类型。 关键词：应急治理、机构间协作、社会网络分析.

Digital assets of organizations are under constant threat from a wide assortment of nefarious actors. When threats materialize, the consequences can be significant. Most large organizations invest in a dedicated information security management (ISM) function to ensure that digital assets are protected. The ISM function conducts risk assessments, develops strategy, provides policies and training to define roles and guide behavior, and implements technological controls such as firewalls, antivirus, and encryption to restrict unauthorized access. Despite these protective measures, incidents (security breaches) will occur. Alongside the security management function, many organizations also retain an incident response (IR) function to mitigate damage from an attack and promptly restore digital services. However, few organizations integrate and learn from experiences of these functions in an optimal manner that enables them to not only respond to security incidents, but also proactively maneuver the threat environment. In this article we draw on organizational learning theory to develop a conceptual framework that explains how the ISM and IR functions can be better integrated. The strong integration of ISM and IR functions, in turn, creates learning opportunities that lead to organizational security benefits including: increased awareness of security risks, compilation of threat intelligence, removal of flaws in security defenses, evaluation of security defensive logic, and enhanced security response.

Illegal dumping is an increasingly costly problem with profoundly negative consequences for the livability and sustainability of our communities. The problem of illegal dumping is particularly acute in the developing world. While the literature is rich in descriptive studies on illegal dumping, few studies leverage large-scale spatial-temporal data through innovative analytical tools to study the actual dynamics of household illegal waste dumping. Our study aims to fill this gap by developing a multilevel theoretical model with which to illustrate the impact of illegal dumping. We explore the spatial-temporal distribution of illegal dumping cases using data mining. Next, we integrate datasets reflecting different levels into a hierarchical data structure organized by membership function. We then use a hierarchical generalized linear model to validate our multilevel model. The results indicate that the spatial factors have a significant relationship with illegal dumping, whereas the direct influence of temporal and community-level factors on illegal dumping is insignificant. Furthermore, the moderating effect of management level and public order on the relationship between spatial features and illegal dumping is significant. Based on our results, we offer several suggestions for preventing illegal dumping.

Cities, like any complex adaptive systems, may become increasingly fragile if not properly managed. To date, the literature has focused primarily on the examination of cities within fragile countries. This has resulted in a dearth of studies that have looked at how developed (or even advanced) cities that operate in relatively stable countries and/or environments might allow unresolved issues to accumulate in the city: degrading its ability to function. Studying fragility in developed cities is a worthwhile undertaking given their economic, social, and political significance. This paper puts forth a conceptual framework to understand the nature of fragile cities in the developed world. Our framework frames fragility as a function of unresolved fractures of social compacts that degrades a city's ability to function over time and stress exacerbates its effects. Drawing on over two dozen incidents from developed cities, we ground the framework and illustrate its value.

Today, information systems are regularly weaponized for political disruption. This threat now encompasses electoral processes, given their increasing dependence on information systems both directly and indirectly. The chapter elaborates on a framing devise—ALERT (the Actors, Levers, Effects, and Response Taxonomy)—to study how information systems can be manipulated and the associated set of responses to such manipulation, which in turn can generate theories about election interference. Illustrative examples of such interference guide the reader through the ALERT framework in the foreign election interference context. Through the description of the ALERT framework and the copious examples reported, the chapter concludes by underlining the importance of a clear understanding of the dynamics related to the Actors involved in information warfare, the Levers used, the Effects in the political and, specifically, electoral fields, which all serve as a tool for obtaining appropriate Responses in the fight against weaponization for political disruption.

COVID-19 severity and mortality risk are greater for older adults whereas economic impact is deeper for younger adults. Using the Health Belief Model (HBM) as a framework, this study used a web-based survey to examine how perceived COVID-19 susceptibility and severity and perceived efficacy of recommended health behaviors varied by age group and were related to the adoption of health behaviors. Proportional odds logistic regression was used to examine the relationship between age group and perceived COVID-19 susceptibility, severity, impact, and health behavior efficacy and adoption. Structural equation modeling based on HBM constructs examined the relationships between health beliefs and behaviors. Data from 820 participants (Ontario, Canada) were analyzed (age: 42.7, 16.2 years; 79% women). Middle-aged and older adults reported greater concerns about the personal risk of hospitalization and mortality, economic impact, and social impact of COVID-19 than young adults. Middle-aged adults also reported greatest concern for other age groups. Adoption and perceived efficacy of health behaviors was similar across age groups with few exceptions. Both middle-aged and older-adults were more likely to perceive their own and each other's age groups as responding adequately to COVID-19 compared to young adults. Structural equation modeling indicated perceived benefits of health behaviors were the primary driver of behavior uptake, with socioeconomic factors and perceived severity and susceptibility indirectly associated with uptake through their influence on perceived benefits. Overall, these results suggest adoption of health behaviors is very high with few differences between age groups, despite differences in perceived impact of COVID-19. Public health communications should focus on the benefits of health behaviors to drive adoption.

IntroductionThe shape of the glucose curve during an oral glucose tolerance test (OGTT) reflects β-cell function in populations without diabetes but has not been as well studied in those with diabetes. A monophasic shape has been associated with higher risk of diabetes, while a biphasic pattern has been associated with lower risk. We sought to determine if phenotypic or metabolic characteristics were associated with glucose response curve shape in adults with type 2 diabetes treated with metformin alone.Research design and methodsThis is a cross-sectional analysis of 3108 metformin-treated adults with type 2 diabetes diagnosed ResultsThe monophasic profile was the most common (67.8% monophasic, 5.5% biphasic, 26.7% continuous rise). The monophasic subgroup was younger, more likely male and white, and had higher body mass index (BMI), while the continuous rise subgroup was more likely female and African American/black. HOMA2-S and fasting glucose did not differ among the subgroups. The biphasic subgroup had the highest early, late, and total insulin and C peptide responses (all pConclusionsBased on the large multiethnic GRADE cohort, sex, race, age, and BMI were found to be important determinants of the shape of the glucose response curve. A pattern of a continuously rising glucose at 2 hours reflected reduced β-cell function and may portend increased glycemic failure rates.Trial registration numberNCT01794143.

Advanced persistent threat (APT) is widely acknowledged to be the most sophisticated and potent class of security threat. APT refers to knowledgeable human attackers that are organized, highly sophisticated and motivated to achieve their objectives against a targeted organization(s) over a prolonged period. Strategically-motivated APTs or S-APTs are distinct in that they draw their objectives from the broader strategic agenda of third parties such as criminal syndicates, nation-states, and rival corporations. In this paper we review the use of the term - Advanced Persistent Threat - and present a formal definition. We then draw on military science, the science of organized conflict, for a theoretical basis to develop a rigorous and holistic model of the stages of an APT operation which we subsequently use to explain how S-APTs execute their strategically motivated operations using tactics, techniques and procedures. Finally, we present a general disinformation model, derived from situation awareness theory, and explain how disinformation can be used to attack the situation awareness and decision making of not only S-APT operators, but also the entities that back them.

The concept of robustness is critically analyzed. Although robustness has been widely adopted across scientific disciplines, no comprehensive and systematic conceptualization on robustness exists. This paper develops a framework to conceptualize organizational robustness. The framework outlines mechanisms of organizational robustness in three layers - strategical mechanisms, functional mechanisms, and infrastructure mechanisms. The complex role of information systems for organizational robustness is also discussed.

Thanks to their enormous potential for creating more efficient processes and solving vexing problem, cognitive computing systems (CCSs) are increasingly prevalent in the public sector. However, their full deployment is stymied by all of the problems faced by private firms (e.g. organizational issues, people issues and technology issues) as well as problems that are unique to the public sector including stakeholder groups with conflicting goals and a demand for full transparency. In this study, we develop a public-sector centric maturity model approach to CCSs that acknowledges and addresses these problems while providing a path to evaluate, assess and guide CCS initiatives. By following this model, the public sector can reap the rewards of CCS deployment and provide better outcomes for its citizenry.

Chatbots — computer programs designed to interactively engage with users, replicating humanlike conversational capabilities during service encounters — have been increasingly deployed across a wide range of Internet-based public services. While chatbots provide several advantages (e.g., improved user experience with reduced waiting times to service access), the surge of chatbot use in public service delivery has frequently been plagued with controversy, poor publicity, and legal challenges. One important reason for this is that users of the services, and the wider public, do not always feel that chatbot-mediated services demonstrate the appropriate public service values. We investigate the public service value dimensions required in chatbots designed for use in the public sector. Specifically, we (a) review chatbots and their use in the delivery of public services; and, (b) develop a framework of how public service values can be exemplified by chatbots. Our study provides implications and evaluation criteria for stakeholders in chatbot assisted public services, including researchers, public managers, and citizens.

Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas&mdash, or concepts&mdash, in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development, (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and, (c) achieving the digital transformation and sustainability of cities through the use of AI technologies&mdash, such as big data, automation and robotics&mdash, is the central community discussion topic.