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This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image.

This work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study.

Results show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination.

The originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.

This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources.

The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques.

The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective.

The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field.

Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories.

The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.

Starting from a critical analysis of the main criteria currently used to identify marginal areas, this paper aims to propose a new classification model of such territories by leveraging knowledge discovery approaches and knowledge visualization techniques, which represent a fundamental pillar in the knowledge-based urban development process.

The methodology adopted in this study relies on the design science research, which includes five steps: problem identification, objective definition, solution design and development, demonstration and evaluation.

Results demonstrate how to exploit knowledge discovery and visualization to obtain multiple mappings of inner areas, in the aim to identify good practices and optimize resources to set up more effective territorial development strategies and plans. The proposed approach overcomes the traditional way adopted to map inner areas that uses a single indicator (i.e. the distance between a municipality and the nearest pole where it is possible to access to education, health and transportation services) and leverages seven groups of indicators that represent the distinguishing features of territories (territorial capital, social costs, citizenship, geo-demography, economy, innovation and sustainable development).

The proposed model could be enriched by new variables, whose value can be collected by official sources and stakeholders engaged to provide both structured and unstructured data. Also, another enhancement could be the development of a cross-algorithms comparison that may reveal useful to suggest which algorithm can better suit the needs of policy makers or practitioners.

This study sets the ground for proposing a decision support tool that policy makers can use to classify in a new way the inner areas, thus overcoming the current approach and leveraging the distinguishing features of territories.

This study shows how the availability of distributed knowledge sources, the modern knowledge management techniques and the emerging digital technologies can provide new opportunities for the governance of a city or territory, thus revitalizing the domain of knowledge-based urban development.

Managing a project involves taking a number of critical decisions that can have a crucial impact on the success or failure of the initiative. The analytical definition and visualization of the main components of a project can support project managers engaged to address the right issues at the right time. This article aims to identify crucial crossroads in the management of a project and to provide a visual representation of knowledge involved into a system of project components and decisions.

A design science process is adopted to define the initial goals and requirements and to develop the knowledge visualization framework. Expert feedback is also gathered to obtain a preliminary validation of the framework.

Moving from a system view of project dimensions, we identify eight types of strategic decisions, i.e. growth, problem shifting, goals balancing, escalation, rewarding, resource allocation, problem fixing and cooperation. We then present a visualization map of project decision making addressing six categories of knowledge (i.e. “what-knowledge”, “how-knowledge”, “who-knowledge”, “why-knowledge”, “what for-knowledge”, “when-knowledge”).

The framework needs further theoretical refinement in terms of more fine-grained decision types, other determinants and the reciprocal influence in the management of project activities.

The article can support project managers attempting to build a comprehensive view of project decisions, and it can be a basis to develop novel types of knowledge management systems for project-related applications.

The article proposes a new approach to sustain strategic decision making in project management by adopting a knowledge visualization view. Moreover, it provides an operational tool for managers and analysts at different levels engaged into the management of a project.

In times of crisis, innovation management and specifically Research and Development (R&D) investments are critical to temper company losses and stimulate higher revenues. Environmental policies, for their potential to stimulate environmental innovations and efficient management of resources, may hold a magnifying role in this relationship. By relying on the distinction between regulatory policies and institutional incentives, this paper argues about the moderating role of environmental policies between a firm's R&D expenses and its performance.

Hypotheses are tested on data collected from a sample of small and medium-sized Chinese enterprises after the 2008 financial crisis.

Findings reveal positive moderating effects of both regulatory pressures and institutional incentives, with a more significant effect of government support. The highest impact is reached when both these types of policies are present.

The theoretical and methodological relevance of this distinction, the importance of an appropriate mix of environmental policies in policymaking and their resilience building role in stimulating environmental innovations in the aftermath of crises are discussed.

The COVID-19 pandemic has stimulated a global movement of open innovation initiatives aimed to provide knowledge and tools to support policy decisions and actions in the emergency scenario. The authors describe an open innovation process aimed to build an information coordination system to reduce the infection diffusion within the population.

The authors use coordination theory principles to elaborate a framework of activities, resources and dependencies among information resources and producers in the COVID-19 emergency. The framework was applied to develop a case study aimed at describing a health emergency system implemented by Dyrecta Lab (a research laboratory on computer science) and CITEL (a medical research center).

The authors describe the existence of relevant “flow,” “fit” and “share” dependencies within the activities of infection containment and medical treatment. The authors identify eight key resources and a number of actors involved in those activities, and describe a platform able to gather a multitude of epidemic-related metrics with the purpose to address dependencies and support decision making.

The authors provide insights for advancing the academic discussion on process coordination principles in time-constrained, volatile and highly demanding scenarios.

The value of the authors’ research can be identified for practitioners engaged to develop innovative development projects for public utility. The authors provide a contribution also for first responders and health operators involved in management of the current and future emergencies.

The adoption of process coordination principles is a relatively new and powerful approach to analyze and optimize the processes that characterize the management of emergency scenarios. Besides, the study and application of open innovation in healthcare are partially limited.

Differently from traditional approaches that rely on the analysis of single dimensions of the tourism phenomenon, this study aims to experiment a systemic approach based on structured and unstructured data sources to elaborate a composite index to measure the tourist competitiveness of marginal areas, with the final aim to design and plan proper socio-economic development strategies.

The methodology adopted to carry out the study follows a four-step process and relies on indicators that are both relevant and accessible. The first step concerns the analysis of the literature about the existing approaches to calculate a tourism index. The second step concerns the definition of the indicators and the collection of data by using both structured and unstructured sources. The third step focuses on the population of the data set. Finally, the fourth step aims at calculating the tourism index through a composite-based methodology and using it for a pilot application in a Southern Italy province.

The study calculates a synthetic tourism index for each of the 97 municipalities of the Province of Lecce (a city located in the southeast of Italy). The proposed index combines administrative, institutional and open data sources to derive a single indicator for each municipality, thus supporting decision-makers in understanding the complex reality and competitiveness level of territories in the tourism industry.

The main elements of originality of the study are the breadth and typology of data sources considered to calculate the composite indicator of tourism competitiveness (both structured and unstructured); and the use of weighting and aggregation procedures in the methodological issues.

Successful businesses demand high-performing human resource management practices (HRMP) and effective knowledge management capacity (KMC) to enhance the overall organizational performance. Rapid growth of both local and multinational companies operating in knowledge-intensive industries has increased the global competition in the labor market, also for the developing economies. Therefore, attracting valuable human capital, retaining talents and managing effectively knowledge to deliver on the latest technologies and innovative solutions and services are the biggest challenges in the modern IT industry. The purpose of this paper is to examine the influence of HRMP on KMC through a cross-case analysis including four companies operating in Indian IT sector. Based on the existing studies in this field, five key HRMP have been identified (i.e. recruitment and selection, training and development, compensation and reward, employee retention and career development), as well as two key processes supporting the KMC (i.e. knowledge acquisition and knowledge sharing).

The paper adopts a qualitative research method based on a multiple case study, and it uses primary and secondary data collected through desk research and field interviews.

Results show the existence of HRMP aiming to enhance the individual learning, motivation and retention of employees for knowledge acquisition and knowledge sharing, in the strategic perspective to improve the organizational performance.

The paper presents some limitations, which may provide scope for future research. First, being the study a qualitative multiple case analysis, a more extensive research is needed to generalize the results and investigate further relationships existing between HRMP and KMC. Moreover, a deep investigation on the organizational performance dimensions (e.g. sales growth, cash turnover, financial goal achievement) is required to verify the existence of possible links. Finally, a further limit consists in considering only knowledge acquisition and knowledge-sharing processes, excluding another crucial knowledge management (KM) process such as knowledge application. As for the research implications, the paper contributes to investigate the role of HRMP in supporting KMC in Indian IT companies, which represents a context of research not so much investigated.

From a practitioner point of view, the study can be helpful to HR and KM managers for motivating employees to undertake learning processes and enhance their performance, as well as to acquire and share knowledge resources that are useful for the organization to remain innovative and stay competitive.

As the influence of HRMP on KMC has not been widely studied in the Indian IT industry, the study may open the field for further studies on a deeper investigation of the relationships existing between human resources management (HRM), KM and organizational performance in knowledge-intensive industries in India and, more in general, in developing economies.

The generation of new ventures within established companies, also known as corporate entrepreneurship (CE), is a process influenced by a set of individual and organizational factors. This paper aims to focus on creativity and human resource management enablers of CE, with the purpose to define an integrative framework and draw a set of related research propositions.

The paper relies on a multidisciplinary literature review in the fields of CE, creativity and organizational innovation.

The effectiveness of CE depends on a set of individual factors, distinguished into professional and psychological characteristics, and organizational factors, which include the system of values of the organization and the management practices applied in the same.

From a theoretical point of view, the paper develops an integrative framework of conditions that impact on CE and outlines a set of propositions and alternative research methods to test.

From a practitioner perspective, the study provides managers with a comprehensive set of factors enabling CE by leveraging the creativity of individuals and make it flourish through consistent human resource management practices.

The value of the paper stays in the integration of individual-related and organizational-related determinants of entrepreneurial performance.

This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an e-commerce company.

A case study approach is used to document the company’s experience, with interviews of key stakeholders and integration of obtained evidence with secondary data.

The paper presents an algorithm and a system to support a more efficient and smart management of reverse logistics based on a set of anticipatory actions, and continuous and automatic monitoring of returned goods. Improvements are described in terms of a number of key performance indicators.

The analysis and the developed system need further applications and validations in other organizational contexts. However, the research presents a roadmap and a research agenda for the reverse logistics transformation in Industry 4.0, by also providing new insights to design a multidimensional performance dashboard for reverse logistics.

The paper describes a replicable experience and provides checklists for implementing similar initiatives in the domain of reverse logistics, in the aim to increase the company’s performance along four key complementary dimensions, i.e. time savings, accuracy, completeness of data analysis and interpretation and cost efficiency.

The main novelty of the study stays in carrying out a classification of anomalies by type and product category, with related causes, and in proposing operational recommendations, including process monitoring and control indicators that can be included to design a reverse logistics performance dashboard.