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The advancement of digital maturity at a business level has proven to be challenging, with limited time, resources and skills in extracting and mapping business processes. In order to advance digitalization, the mapping of all business activities is essential. To close this gap, this research is aimed at enabling the operational level extraction and classification of business activities using a graphical methodology. This objective is accomplished through the development of a digital tool that leverages Software Development Life Cycle (SDLC) principles.

This research is focused on facilitating rapid extraction of business activities with the adoption of a graphical methodology. Business activities are captured through a digital tool in a structured, interlinked and hierarchical manner.

Notably, this work is novel as it introduces a new graphical tool to replace traditional tools in the domain of Business Process Mapping (BPM). This graphical tool is distinguished by a unique feature; that is a self-configuring database with the flexibility to capture all process options, providing innovative solutions to the challenges faced in digitalizing business processes.

This work is original in that a new graphical interface is developed to replace traditional tools in the space of BP data gathering. The unique challenge of an auto configuring database with flexibility to capture all process options is presented.

This study aims to review the current literature on the positive and negative effects of digitalisation in preventing corruption. It analyses existing research patterns and provides recommendations for future studies.

This paper employed bibliometric analysis and systematic review to scrutinise 190 papers from the Web of Science database from 2000 to 2023. Biblioshiny on R Studio was used for advanced bibliometric analysis to determine publication dynamics, influential journals, publications and impactful authors and a three-field plot to analyse relationships among countries, keywords and journals.

This study provides a bibliometric analysis of the past and actual developments in the field related to the effects of digitalisation on corruption. Based on the systematic literature review on a sample of the 50 most influential articles, this study identified background theories employed, the primary research methodologies adopted and valuable insights into both the positive and negative aspects of the impact of digitalisation on corruption.

This study provides an extended overview of the effects of digitalisation on corruption and advances new avenues for further research related to this field. The white and dark sides of the effects of digitalisation on corruption are highlighted. Furthermore, the study identifies the need for further research in this field to gain a more in-depth understanding of the nexus between digitalisation and corruption.

Today social media capabilities have enabled businesses and enterprises to more collaboration, engagement and co-creation with their customers. So the current paper expands on this notion. The aim of this study is a bibliometric analysis to examine the trends of publications in the field of co-creation based on social media.

To data collection of quantitative analysis, Scopus database was selected and the collected data were analyzed using Bibliometrix-package. The Web of Science also was selected to retrieve highly cited and hot papers for qualitative part of analysis besides top 10 Scopus highest citation per year documents on June 6, 2020.

The results indicate insights into research trends pertaining to social media-based co-creation, as follows: starting jump to the publications occurred in this researches from the year 2008 and the growth trend is progressing in recent years; the stressful points are “co-design,” “co-creation” and “value co-creation” and concepts such as “open innovation,” “co-innovation” and “co-new product design” are new topics that guide future direction; the USA and UK are leaders in number of multiple and single publications; the most active and top journals that are better suited to achieving a high citation rate per year for a related paper were introduced. In addition, the top documents and highly cited papers were qualitatively analyzed on the basis of times cited per year.

The current study is not free of limitations. The database was limited to only Scopus. So the patterns and trends generated in the study may not be generalized to all social media-based co-creation research. Of course, the authors did not intend to ignore other contributions. It is mainly because of the number of documents retrieved from Scopus database and the coverage, Scopus was selected. Moreover, other types of research techniques such as correspondence analysis can be incorporated to generate additional meaningful insight.

In this time of social media and user-generated content portals, co-creation through social media has become quite popular. So the main innovation of this study is providing a visual presentation of the trends and patterns in the evaluation of social media-based co-creation from the first document about the research area published till 2020. The results of this paper can shed light on the factors that strengthen the contribution of studies in a research area. Generally, the bibliometric items the authors analyzed essentially show the entire field picture and guide researchers toward understanding future trends to produce impactful studies.

Aligning corporate communications through different information sources is a great challenge for marketers, especially those operating in the tourism sector, which has been harshly affected by the recent COVID-19 pandemic. This paper provides a deep analysis of the implementation of seven basic principles of the integrated marketing communications (IMC) paradigm in a crisis situation.

In-depth interviews with tourism and hospitality service providers were conducted in the fourth quarter of 2021 in Croatia, a destination that showed remarkable results in terms of the number of international tourist arrivals during the pandemic.

Most firms successfully transitioned from tactical to strategic IMC implementation. Some problems were reported in the coordination of communication tools and channels. Whereas the use of digital technology was enhanced, database management did not receive sufficient attention. Message clarity represented the greatest challenge, while consumer-centric communication was the most neglected principle. Relationship building was pursued mainly through B2B rather than B2C communication, whereas brand equity development pursued through communication mix mostly focused on increases in awareness, perceived quality and attitudinal loyalty.

This research is qualitative in nature and provides opinions on IMC adoption from the managerial perspective only.

This paper provides guidelines for the successful integration of marketing communications (marcom) in an extremely ambiguous and uncertain environment.

The contribution of this work lies in the proposal of a new refined and expanded theoretical framework of IMC principles and numerous marcom strategies for operating during the COVID-19 pandemic, thus providing relevant implications for academia and industry.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

This research paper aims to provide a comprehensive overview of the determinants influencing organisations decisions to adopt crowdsourcing. By synthesising existing literature, it seeks to identify critical factors that act as enablers or inhibitors in the adoption process and propose a framework for understanding crowdsourcing adoption within organisational contexts.

This study employed a systematic literature review methodology to examine the determinants influencing organisations' decisions to adopt crowdsourcing. The review encompassed research articles from the Web of Science and Scopus databases, spanning 2006 to 2021. Additionally, morphological analysis was conducted to categorise the identified determinants into three distinct contexts: technological, organisational and environmental. This methodological approach facilitated a comprehensive exploration of the factors shaping crowdsourcing adoption within organisational settings, allowing for a nuanced understanding of the phenomenon across different dimensions.

The study identifies 12 determinants influencing crowdsourcing adoption, categorised into technological, organisational and environmental dimensions. These determinants include technological compatibility, organisational readiness, top management support, crowd readiness and availability of third-party platforms. While some determinants primarily act as enablers, others exhibit dual roles or serve as inhibitors depending on contextual factors.

The findings offer valuable insights for scholars, practitioners, and organisational leaders seeking to leverage crowdsourcing as a strategic tool for innovation and competitiveness. The assessment scale of drivers and barriers developed in this research offers a systematic approach for evaluating the factors influencing crowdsourcing adoption, providing a nuanced understanding of innovation adoption dynamics. Theoretical implications include advancements in morphological analysis methodology and a nuanced understanding of innovation adoption dynamics. Managerial implications highlight strategies for enhancing organisational readiness, leveraging leadership support and mitigating adoption risks. Overall, the study provides a foundation for future empirical research and practical guidance for organisations planning to adopt crowdsourcing initiatives.

This research contributes significantly to crowdsourcing by presenting an integrated and theoretically grounded framework. By consolidating adoption determinants from diverse contexts, this study clarifies the understanding of crowdsourcing adoption. The framework offers practical value to managers and decision-makers, equipping them with a structured approach to assess and navigate the challenges associated with effectively adopting crowdsourcing. As such, this study contributes to advancing crowdsourcing practices and supports more informed managerial decision-making in innovation and knowledge sourcing.

The aim of this chapter is to develop a conceptual model for the analysis of sustainable development and the ranking of cities based on selected standard criteria and metrics of smart cities. The conceptual framework contains standard and measurable indicators that influence the creation and survival of smart cities that could be self-sustaining, Green Resilient. We can measure the level of smartness of cities from two perspectives, first from the point of view of the degree of automation of services, infrastructure, buildings, transport, etc. and second from the point of view of planning the further development of the city in order to raise the quality of life of its citizens. Indicators should satisfy the principles of specificity, measurability, availability, relevance and timeliness (Schomaker, 1997). Researchers in the field of smart cities define different sets of characteristics, on which they construct a system of indicators that together describe the degree of development of a smart city. It must be taken into account the fact that there is no single set of indicators that would refer to the level of smartness of the city. It is a complex phenomenon, which occurs differently depending on the conceptual framework and the goal of classifying cities according to the complex characteristics of smartness, especially if you take into account the fact that the general well-being and quality of life of citizens are more important than only indicators of the quality of city services.

The purpose of the study is to compare methodologies for mapping a historic building, with image capture by smartphones and drones, using photogrammetric techniques for three-dimensional (3D) modeling of the structure. Processes and products are also analyzed, as well as possibilities for storing and visualizing data for structuring a cadastre of historical and artistic heritage are studied.

For mapping with smartphones, the overlapping of photographs was guaranteed, with data acquisition using three different cameras, on the same date as the aerial survey. The models were made from different combinations of camera use. For storage, a conceptual model based on ISO 19.152:2012 is proposed, which was implemented in the MongoDB, resulting in a database for storage. The visualization was carried out on the Cesium ion platform.

The results indicate that the terrestrial 3D reconstruction using smartphones is an efficient alternative to the historical and artistic cadastre, presenting texture quality superior to the aerial survey in a shorter production time. When dealing with the conceptual model, the LADM (Land Administration Domain Model) standardization guarantees interoperability and facilitates data exchange. In addition, it proved to be flexible for the creation of thematic profiles, supporting their effective storage. The insertion of data in the visualization platform was simple and effective, and it even generated sharing links for visualization of the models.

The study analyses a low-cost method with the use of easily accessible devices, with a combination of methodologies and applied techniques. The data storage and visualization method is also simple and flexible, suitable for application in the cadastre of historical heritage.

Digital learning systems are crucial for education and data collected can analyse students learning performances to improve support. The purpose of this study is to design and build an asynchronous hardware and software system that can store data on a local device until able to share. It was developed for staff and students at university who are using the limited internet access in areas such as remote Northern Territory. This system can asynchronously link the users’ devices and the central server at the university using unstable internet.

A Learning Box has been build based on minicomputer and a web learning management system (LMS). This study presents different options to create such a system and discusses various approaches for data syncing. The structure of the final setup is a Moodle (Modular Object Oriented Developmental Learning Environment) LMS on a Raspberry Pi which provides a Wi-Fi hotspot. The authors worked with lecturers from X University who work in remote Northern Territory regions to test this and provide feedback. This study also considered suitable data collection and techniques that can be used to analyse the available data to support learning analysis by the staff. This research focuses on building an asynchronous hardware and software system that can store data on a local device until able to share. It was developed for staff and students at university who are using the limited internet access in areas such as remote Northern Territory. This system can asynchronously link the users’ devices and the central server at the university using unstable internet. Digital learning systems are crucial for education, and data collected can analyse students learning performances to improve support.

The resultant system has been tested in various scenarios to ensure it is robust when students’ submissions are collected. Furthermore, issues around student familiarity and ability to use online systems have been considered due to early feedback.

Monitoring asynchronous collaborative learning systems through analytics can assist students learning in their own time. Learning Hubs can be easily set up and maintained using micro-computers now easily available. A phone interface is sufficient for learning when video and audio submissions are supported in the LMS.

This study shows digital learning can be implemented in an offline environment by using a Raspberry Pi as LMS server. Offline collaborative learning in remote communities can be achieved by applying asynchronized data syncing techniques. Also asynchronized data syncing can be reliably achieved by using change logs and incremental syncing technique.

Focus on audio and video submission allows engagement in higher education by students with lower literacy but higher practice skills. Curriculum that clearly supports the level of learning required for a job needs to be developed, and the assumption that literacy is part of the skilled job in the workplace needs to be removed.

To the best of the authors’ knowledge, this is the first remote asynchronous collaborative LMS environment that has been implemented. This provides the hardware and software for opportunities to share learning remotely. Material to support low literacy students is also included.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.