Abstract

Information system integration during merger and acquisition (M&A) is a critical process when organizations are joining. The integration process consisting of several crucial activities for making organizations being able to work together. Organizations need to evaluate internal and external factors in order to make the process work. In this paper we evaluated critical success factors, challenges and information systems (IS integration methods from interviews with employees in a case study to identify the most critical activities for a successful IS integration. The investigated process stretches from the introduction of the M&A to the actual integration of two organizations IT-stacks. The identified crucial activities found were top management support, organizational culture, importance of competence, internal communication, maintaining information system security, communication with end-users and managing legacy system. Evaluating these topics and creating a short and a long-term plan on how to integrate and work with these are essential to succeed with the IS integration in any M&A.

Introduction

One way for organizations to grow is through merger and acquisition (M&A) which could be an excellent opportunity to expand but are equally as challenging. Henningsson, Yetton [1] mentions that 60-70% of the M&As causes organizational problems rather than creates financial values. One process that is problematic and cause confusion is the information system (IS) integration, which today plays a central part in the realization of an M&A. According to Sarrazin and West [2], 45-60% of the expected benefits, such as financial gains and market shares, are directly dependent on IS integration. Accenture [3] reports that IS integration is the second most important reason for M&A failures, which can cause billions of dollars in losses if not prioritized.

IS integration related to an M&A is often described as consisting of the same key processes including due diligence and the IS integration processes [1, 4-7]. These processes, together with success factors and challenges, create a blueprint for how IS integration can be done in practice. However, reality is more complicated, and the realization of the process is one of the most critical steps of the M&A [5]. Haspeslagh and Jemison [8] have broken down the M&A process into two phases. The pre-phase and the post-phase, where the most critical phase is stated to be the pre-phase [8]. Henningsson [4] later redefined these phases, including several sub-processes. Harrell and Higgins [6]; Lam [9] as well as Soliman, Clegg [10] identified several success factors and challenges that are critical to a successful IS integration. Another identified challenge in the literature is organizational culture. During the integration of two companies, organizational culture is considered as an important challenge to manage [11]. According to Henningsson [4], only 18 % of the M&As deals with post-phase challenges, while 82 % struggle with pre-phase challenges. Technology integration during an M&A is a crucial part of the upcoming organization. All major processes of an organization are dependent on the information IS provides, e.g. decision-making, resource allocation task performance, risk identification [6, 12].

Conclusions and future research direction

This paper discuss the question: What needs for making an IS integration a successful system integration in an M&A? It does so from a process perspective, and it can be concluded that merging organizations need to consider some activities carefully before the IS integration takes place. Top management support is of high priority for employees to be able to complete the process of IS integration. Except for the categorizing of phases, the main distinction between literature and practice in this research is the importance of evaluating work methods when legal restrictions are still in place. Finding a way to conduct the IS integration during the time organizations cannot talk to each other is important for the day one launch of the new organization. To have functioning IS at day one, the organization needs to have clear communication and integrate the two organizational cultures with a short and longterm plan. This plan should also include the structuring of the combined IT-stacks and should evaluate which systems to kill or to re-invest in. Organizations also need to put effort into merging the cultures and unite employees early in the M&A. It will facilitate the workflows between employees and involved organizations. Transparency is crucial in and between involved organizations. This transparency is especially important from top management level, that clearly needs to communicate organizational goals, integration methods and approaches to all levels of participating organizations. It can also be claimed, even if the respondent did not give it high priority, that manage the question of legacy systems is crucial. The merging organizations need to have a working solution up and running at the day one launch. Otherwise, it could have consequences for the customers due to unavailable services. Hence, it is not viable for the future, building on old systems. It will eventually end up in technical debt, costly to both invest in and to deconstruct.

Abstract

     Explosive growth in big data technologies and artificial intelligence (AI) applications have led to increasing pervasiveness of information facets and a rapidly growing array of information representations. Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information and consequently affect human performance. Extant research in cognitive fit, which preceded the big data and AI era, focused on the effects of aligning information representation and task on performance, without sufficient consideration to information facets and attendant cognitive challenges. Therefore, there is a compelling need to understand the interplay of these dominant information facets with information representations and tasks, and their influence on human performance. We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary for these complex information environments. To this end, we propose and test a novel “Adaptive Cognitive Fit” (ACF) framework that explains the influence of information facets and AI-augmented information representations on human performance. We draw on information processing theory and cognitive dissonance theory to advance the ACF framework and a set of propositions. We empirically validate the ACF propositions with an economic experiment that demonstrates the influence of information facets, and a machine learning simulation that establishes the viability of using AI to improve human performance.

Introduction

     We have begun a conversation with machines that will last for the rest of our lives – that will also be remembered by those machines long after our own fragile memories have failed us.’ William Ammerman 2019

     Big data and artificial intelligence are changing the essence and form of our interactions with information and with information processing machines. No longer are human emotions confined to interpersonal relationships – instead, we find ourselves displaying genuine feelings and sentiment when confronted by intelligent agents and systems (Law, Chita-Tegmark, & Scheutz, 2021). Further, we have witnessed significant increases in information entropy due to social media data of questionable veracity and deliberate attempts to provide misinformation (Moravec, Minas, & Dennis, 2018). As a result, decision makers are confronted by information embedded with greater equivocality, veracity, and density. These developments portend fundamental changes in the nature of information that necessitate attention to the ways in which information is received, conceived, interpreted and acted upon. To this end, we conceptualize and develop the Adaptive Cognitive Fit (ACF) framework, to improve our understanding of how humans can and should leverage artificially intelligent technologies to make decisions in the emerging complex information ecosystems.

Conclusion

     The AI schema posited by ACF can be generalized to a wide range of future information systems and decision support design solutions, where AI can learn from human performance, and environmental variables, to help us in our pursuit of optimality. The projected trajectory of developments in the big data and AI ecosystems, and machine learning in particular “have intensified the speed, and our abilities, to create and deploy new knowledge for constructing theories” (Tremblay et al., 2021, Abbasi et al., 2016). ACF is a unique forward-looking theory, which aligns well with calls from researchers towards positivism (Kar & Dwivedi, 2020) such as the “theory in flux” paradigm (Tremblay et al., 2021): ACF is deeply rooted in prominent theoretical frameworks, and posits a clear application-oriented design science framework that combines AI with HI in its pursuit of optimal performance.

     Information systems discipline has an established culture of creatively theorizing and modeling the interactions between human behaviors, technology and information from an applied and design science perspective (Tremblay et al., 2021, and in general: MISQ Special Issue: Next Generation IS Theories, March 2021). Keeping in line with this valuable culture, we hope that ACF will provide vital insights to ensure the relevance and applicability of cognitive fit research to emerging big data and AI ecosystems.

Abstract

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Abstract

In recent years, the world’s population living in cities has been rapidly increasing. Cities are transforming their infrastructure in a smarter and more efficient way so that sustainable development forms part of their long-term strategy. However, this transformation does not always result in expected benefits due to a variety of factors such as an absence of social acceptance, a lack of holistic design and the development of unilateral interventions. An analysis of the scientific literature related to the evaluation of the impact of smart city actions revealed a gap in the holistic methods for their assessment. To this end, an accurate evaluation of implemented smart solutions focusing on the energy domain is necessary in order to assess the expected and realized impact of each solution. This paper proposes a seven-step methodology for assessing the impact of smart city interventions and presents a use case for the city of Espoo. A number of major findings were the outcome of our research and development work, such as the need for a thorough analysis of the long-term vision of the city, a combined top-down and bottom-up approach and the ongoing cooperation between all stakeholders involved in urban planning and transformation, in which necessary Key Performance Indicators (KPIs) are defined.

1. Introduction

1.1. The Necessity of Impact Assessment
According to the United Nations [1] 68% of the world’s population is projected to live in urban areas by the year 2050; cities, therefore, are anticipated to face new challenges in sustainably integrating further populace, such as a lack of appropriate infrastructure for sustainable energy and automated systems, applications and commercial arrangements, digital security, network connectivity and data capacity [2]. Cities will be required to transform their infrastructures in a smarter, more efficient and resilient way so that sustainable development forms part of their long-term strategy and a better quality of life is assured to their citizens. The concept of the smart city is multidimensional [3], with the aim of increasing urban sustainability and quality of life while pursuing economic development among its citizens [4]. Additionally, sustainability has gained popularity among scholars, environmental policy makers and government agencies [5]. However, a smart city is not just about creating technically viable urban areas and stimulating economic growth; it requires a multifaceted and multidimensional approach that promotes a structural transformation in the urban ecosystem, directing the development of cities towards their “smartness” in areas such as economy, governance, social, environment, mobility and ICT [6].

Abstract

     One of the most critical areas of concern in the online learning process is to design engaging courses that are easy to follow and interactive to keep the audience attracted when there is no in-person communication between instructor and learner. Online courses may be challenging for learners, instructors, and institutions as well. However, the employment of creative approaches incorporates into online training strategies aims to make learning more interactive for learners. This case study discusses the employment of a range of creative e-learning strategies and online technological tools to deliver knowledge in a virtual environment based on collaborative learning, learning by doing, and team-based learning techniques. Common challenges of e-learning faced by the instructor and learners and the way they are approached are also discussed. The introduced approach has been employed in a Management Information Systems course as a case study.

Introduction

     Efficient knowledge transfer is the key to sustaining success and development. The advancement of technology has made knowledge transfer and learning processes faster and more convenient and online learning through the Internet and web applications has enabled individuals to study globally. The Internet has provided people from all over the world to gain equal education opportunities and simply transfer knowledge. Online learning through the Internet and web applications enables individuals to study and teach from anywhere in the world in a cost-effective and time-saving manner [1]. Especially with the coronavirus outbreak and the limitation of in-person classes, virtual education has been welcomed. Furthermore, the hectic lifestyle, ease of internet access, and increase in access to educational tools are other reasons for the popularity of online courses [2]. The major customers of e-learning services are schools, universities, educational institutions, and the training department of industries. In industries, employees’ efficient performance is significantly reliant on frequent and regular education; however, since employees are almost busy attending in-person courses, delivering the training material by e-learning makes a significant contribution to keeping them updated. E-learning is an online learning platform that emerges in a formal context and employs a variety of multimedia and communication technologies for a convenient learning process [3]. E-Learning is moving toward more inclusive developmental approaches with the necessity to deliver more interesting, effective, and engaging e-learning courses [4].

     Flexibility is the main advantage of online learning. Unlike the traditional or face-to-face learning method, online learning and distance education have improved businesses and education systems in many aspects [5]. Distance education provides students with the opportunity to get connected to their counterparts in different parts of the world while the instructor assigns them in teams to deliver group assignments remotely and engage in video calls and distance meetings unlimitedly [3].

Conclusion

     The importance of delivering creative approaches in teaching is highlighted since a considerable shift has been witnessed in the classroom environment by the spread of e-learning. E-learning courses are required to assure the attendee’s improvement in competency, knowledge, and skill by providing accurate and applicable processes for knowledge transfer. In this case history, methods that have been employed in an online MIS course to ensure that the class is collaborative, and students learn by doing in a team-based environment have been introduced. The feedback received from students by taking technology-based approaches in teaching MIS and students’ evaluation records has demonstrated the success of the approach; however, to realize the reliability of the approach in comparison to similar teacher-oriented e-learning courses should be analyzed using students’ evaluation records in different courses. Also, a systematic feedback collection is required to ensure the proposed e-learning methods are convenient and useful. Feedback collection after each program helps identify the barriers to effective learning, select effective methods of problem-solving, and improve the system.

Abstract

1 Introduction

2 Literature review

3 Research methodology

4 Discussion

5 Conclusions

References

Abstract

     This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures.

Introduction

     Huge competition and fluctuating demand patterns increase the data generation in the supply chain (SC) (Arunachalam et al., 2018). This pushes the companies to adopt the supply chain analytics (SCA) so as to gain competitive advantage (Davenport and O’dwyer, 2011; Shafiq et al., 2020). Adopting the SCA practices improves the accuracy and overall performance of the SC. As firms are already using statistical methods for decision making in one way or another, some practitioners are against the implementation of SCA because of the time and cost incurred in adoption of these solutions (Kusiak, 2006; Trkman et al., 2010). Implementing SCA in businesses is a tricky task because of the uncertainty associated with the data and the changing requirements of the customer (Handfield and Nicholas 2004; Liberatore & Luo, 2010; Huner et al., 2011; LaValle et al., 2011; Manyika et al., 2011).

Conclusions

     The MCDM methods support the decisions given in the business, which contains many criteria that have to be satisfied to make any decision. The uniqueness of this study for the best big data practice to improve the overall performance of the supply chain is that, the qualitative and quantitative factors with different scales are combined in the same technique, also we need to know the risk concept in the analysis and last, TODIM method is applied to this problem. Although there are many decision-making methods that are based on complex calculations, the proposed framework can be applicable easily by the companies in various industries in order to make decisions based on many criteria. To get more accurate data and efficient decisions in the decision-making process, we can implement fuzzy logic to the TODIM method and Pythagorean Fuzzy TODIM so that we can reduce uncertainty and ambiguity in the decisions taken by the companies. The criteria chosen in this study are relevant to the Indian retail supply chain. These criteria can vary across the industries. Hence, the dominance values change with criteria which are different for different industries like for some industry cost may be the main criteria but for others demand management is the main criteria, so it varies from industry to industry. Hence, obtained priorities may not be generalized.

Abstract

1. Introduction

2. Literature Review

3. Research Methodology

4. Study Results

5. Conclusion

References

Abstract

     Big Data as Service (BDaaS) provides an alternative solution to overcome the challenges when a business embarks on adopting and implementing a big data strategy. Although BDaaS provides a viable alternative for SMMEs, there is limited research on the specific factors that affect the adoption of BDaaS in SMMEs. The study aims to explore the factors that affect the adoption of BDaaS in SMMEs. The study adopted the TOE framework as the lens to explore the technological, organizational, and environmental factors affecting the adoption of BDaaS in SMEs. The study results indicate that SMMEs need to consider technological, organizational, and environmental factors when adopting BDaaS. The study contributes to the body of knowledge on factors that affect the adoption of BDaaS in SMEs based on the study results. Despite the limitation of the study of not being empirical (used secondary data), the study contributes to the body of knowledge on factors affecting the adoption of BDaaS in SMEs. In addition, the study may stimulate further research on factors that affect the adoption of BDaaS in SMEs using other research methods.

Introduction

     The concept of digital transformation is generally related to the desire to leverage new technologies to gain and sustain a competitive advantage in a digital age, where products and services are required to be delivered both online and offline [1]. The digital transformation implemented successfully requires not only the acquisition and deployment of technological resources, but it is a managerial aspect too including matters such as the re-design of processes in the business, training, and investing in resources such as organizational capabilities and human resources [2]. Due to businesses’ newly discovered asset, big data, businesses now build their transformation models focusing on leveraging the organization’s big data. Through the adoption of Big Data, SMMEs can generate value from their data insights and as a result, gain significant competitive advantage, generate increased profits, and increased operational efficiency. Big Data as Service provides an alternative solution to overcome the challenges when a business embarks on adopting and implementing a big data strategy. BDaaS vendors utilize cloud platform solutions to provide to businesses for cost-cutting and efficiency in managing the business’ big data [3]. The study explores factors affecting the adoption of Big Data as a Service in SMEs. The structure of the rest of the paper is as follows: Section 2 Literature Review, Section 3 Research Methodology, Section 4 Study Results, and Section 5 Conclusion.

Conclusion

     This research study conducted a systematic review of the literature to explore and conceptualize BDaaS factors affecting adoption in SMMEs. The research objectives were to explore factors affecting the adoption of BDaaS in SMMEs. The results show a decrease and an increase of published of articles on the factors affecting BDaaS adoption in SMMEs during period under-review. The European region had the highest number of published articles on the factors affecting BDaaS adoption in SMMEs. The qualitative research method was the most used method on reviewed published articles during period under-review. The study results revealed the most of the technological factors (security/privacy concerns, complexities, costs, and resources) affect BDaaS adoption in SMMEs. The study results indicate that organizational factors (organizational readiness, employee knowledge, financial costs, and infrastructure) affect BDaaS adoption in SMMEs. Lastly, the study results show that three environmental factors (legislation, vendor capabilities, and competition) affect BDaaS adoption in SMMEs.

Abstract

     Resource-constrained Internet of Things (IoT) devices are executing increasingly sophisticated applications that may require computational or memory intensive tasks to be executed. Due to their resource constraints, IoT devices may be unable to compute these tasks and will offload them to more powerful resource-rich edge nodes. However, as edge nodes may not necessarily behave as expected, an IoT device needs to be able to select which edge node should execute its tasks. This selection problem can be addressed by using a measure of behavioural trust of the edge nodes delivering a correct response, based on historical information about past interactions with edge nodes that are stored in memory. However, due to their constrained memory capacity, IoT devices will only be able to store a limited amount of trust information, thereby requiring an eviction strategy when its memory is full of which there has been limited investigation in the literature. To address this, we develop the concept of the memory profile of an agent and that profile’s utility. We formalise the profile eviction problem in a unified profile memory model and show it is NP-complete. To circumvent the inherent complexity, we study the performance of eviction algorithms in a partitioned profile memory model using our utility metric.

Introduction

     Resource-constrained Internet of Things (IoT) devices (henceforth called IoT devices) have previously been used to perform sensing and actuation after receiving commands from a server. However, there is increasing interest in having those resource-constrained devices autonomously performing actions. With the limited resources these devices have, they will not be able to perform expensive computation on their own and will need to interact with other devices to complete some computation, such as by offloading tasks to resource-rich edge nodes (henceforth called edge nodes) [1].

     For redundancy multiple edge nodes should be provisioned in the network, which means that there is a need for IoT devices to select which edge node should be interacted with. However, there is a risk that interactions may not be correct or successful, e.g., due to network attacks, edge nodes being overloaded with tasks, or edge nodes intentionally misbehaving. One approach to mitigate this threat is to use a measure of behavioural trust to select which edge node to offload a task to. This measure of behavioural trust can be formed by recording opinions of past interactions with other nodes such that trust models (such as the Beta Reputation System [2]) can use these records to quantitatively assess whether nodes will provide services correctly in the future.

Conclusions and future work

     In this work we have investigated how to manage information stored on resource constrained agents that is used to assess behavioural trust in other agents. Work in cache replacement policies are not directly applicable, as deleted information used to evaluate trust cannot be recreated or re-obtained.

     We showed that selecting which information to keep and/or to evict is NP-complete, so simulations of 11 different eviction strategies were performed to gauge their performance. Results for our proposed trust model-agnostic utility function, showed that MinNotInother, NotInOther, Chen2016, FiveBand, FIFO, LRU, LRU2 and Random performed well. However, having the MinNotInOther eviction strategy that considers relevant items in other buffers can improve trust information utility when the memory size is small. Finally, we investigated how to size these memory regions under memory constraints via obtaining a solution to the Bounded Knapsack Problem.

     Future work will need to consider heuristics to decide if an item should be evicted, taking into account both utility and the cost of re-adding items (e.g., verifying digital signatures [30]), plus other considerations such as data freshness in highly mobile networks. Another area for future work is considering how to specialise the utility function. In this work we have focused on evaluating utility per agent. Instead the utility function could consider the global balance between the information stored on agents, this may involve focusing on storing information on the best and worst performing agents, or by trading-off detail on some agents in order to store cryptographic information on more agents. Finally, distributed storage of trust information could be considered. However, this will incur additional storage costs to ensure the integrity, confidentiality and authenticity of behavioural trust information.

Abstract

Introduction

Literature Review

Case Review of Retail Sector in UAE

Conclusion

References

Abstract

     Despite research on big data analytics being conducted in past, there are no studies focused on the opportunities i.e., growth prospects and its associated challenges in specific industries in UAE. In this study, big data analytics were used in SMEs retail sector in UAE region to assess the use of these technologies. Companies can use these to gain new understandings from their customers and suppliers perspective utilizing big data analytics techniques to take informative decisions on pricing, inventory management, payment solutions and marketing decisions. Big data can be used to help SMEs foresee the needs and preferences of their target audiences. The focus of this study is on SMEs since these are the pillar of every economy and can make quick adjustments to productivity changes. It aims to analyze and identify major chances and threats of Big Data and propose the best ways for SMEs to utilize it for improving their efficiency and business practices.

Introduction

     The COVID-19 viral pandemic has had a significant impact on corporate growth across several industries. Small and midsized enterprises (SMEs) are among the most economically disadvantaged, a fact that has prompted governments of all views to enact unprecedented measures to help small businesses – such as tax holidays, soft loans, and implementation of digital technology to increase operational efficiency and ultimately revenue generation. On a regional level, several regulators and authorities have taken proactive measures to mitigate the impact on small enterprises. The ability of a small business to pivot and adapt to a new reality reflects its financial vulnerability, and it has been seen that businesses are innovating in the face of adversity – whether it’s changing existing services, catering to the requirements of newly housebound consumers, or adjusting product offerings to support government efforts to combat the coronavirus [1]. Most bigger firms are undergoing much-needed digital transformations on a small or large scale in order to stay competitive and be very customer-centric. Economic growth is bolstered by a growing SME sector, which decreases unemployment and poverty as well as encourages entrepreneurial activity and thus making it important for any country [1]. The Emirate is working to transition from an oil and gas dependent GDP to a free – market economy focused on trade, services, and manufacturing [2]. The contemporary market economy, on the other hand, relies heavily on SMEs, which account for over 40% of Dubai’s GDP [3].

Conclusion

     There has been a dramatic increase in the amount of data generated by both real and virtual worlds in the last few decades. Companies of allsizes use big data toolsto manage vast amounts of data and obtain comprehensive information that advances their business intelligence and allows them to make smart decisions. The retail business is one of the most diverse businesses, and some retailers are among the UAE’s top corporations. Simultaneously, the retail industry has many tiny “mom-and-pop” establishments, making it vital to many families’ livelihoods. Big Data Analytics has the potential to help these people. By analyzing data and creating correlations between diverse things, Big Data Analytics can enable these stores to discover new changes in their units. Finally, Big Data Analytics must be recognized as a crucial aspect in making the SMEs retail sector’s business a success story, considering all the facts, figures, and prospects covered in the article. If SMEs want to take advantage of Big Data’s capabilities, they must first undergo a cultural transformation. This necessitates them investigating data-handling tools and procedures outside of their smaller organizations, as well as being prepared to actively use Big Data in their decision-making processes. They must be prepared to dive in and explore the ever-expanding ocean of data that awaits them out there. Sayings alone, however, will not be enough to get SMEs on the path to data analytics. All shareholders, including domestic and worldwide regulators, the IT diaspora, the industry group, and the digital transformation community, face a difficult dilemma as a result of the previously identified issues. Further investigation and research can be done by doing empirical study and collecting raw data from various SMEs in retail sector in U.A.E. And then meaningful information can be extracted for further analysis to demonstrate the feasibility and reliability of the model mentioned in the paper

Abstract

Introduction

Theoretical background and literature

Environmental concerns in travel, tourism and hospitality

Methodology

Variables

Model specification

Key findings

Discussion and conclusion

Conclusion and limitations

Acknowledgements

References

Abstract

     By deploying big data analytical techniques to retrieve and analyze a large volume of more than 2.7 million reviews, this work sheds light on how environmental concerns expressed by tourists on digital platforms, in the guise of online reviews, influence their satisfaction with tourism and hospitality services. More specifically, we conduct a multi-platform study of Tripadvisor.com and Booking.com online reviews (ORs) pertaining to hotel services across eight leading tourism destination cities in America and Europe over the period 2017–2018. By adopting multivariate regression analyses, we show that OR ratings are positively influenced by both the presence and depth of environmental discourse on these platforms. Theoretical and managerial contributions, and implications for digital platforms, big data analytics (BDA), electronic word-of-mouth (eWOM) and environmental research within the tourism and hospitality domain are examined, with a view to capturing, empirically, the effect of environmental discourse presence and depth on customer satisfaction proxied through online ratings.

Introduction

     Human consumption activities have been found to generate natural resources depletion and jeopardize the very existence of the planet (United Nations, 2013). Tourism is no exception. As a subset of human activities, tourism-related activities have been found to consume considerable natural resources. In 2010, it was estimated that tourism-related activities consumed (on average) 3,575MJ of energy per trip, 6,575 liters of water per tourist, 42 m2 of land per bed, 1,800 g of food per tourist/day, and generate 250 kg CO2 of emission per trip (Gössling & Peeters, 2015). The overall situation is getting worst over time, with growth factors over the period 2010–2050 estimated to be 2.64 for energy and CO2 emissions, 1.92 for fresh water, 2.89 for land use and 2.08 for food use. Tourism activities – the tourism and hospitality infrastructure, and transit activities – have a detrimental impact on the environment (for example, in the form of CO2 emission). An excess of tourism flow, known as over-tourism, is identified as one of the major issues associated with tourism activities and has been increasingly researched (Oklevik et al., 2019).

Discussion and conclusion

     Building on more than 2.7 million ORs collected from Booking.com and Tripadvisor.com (Li et al., 2018; Mariani et al., 2018), and examined with sophisticated big data analytical techniques, this work has described consumers’ behaviors and how consumers perceive and deal with sustainability issues, and if consumers’ environmental discourse affects their OR ratings. By adopting multivariate regression analyses, we have shown that OR ratings are positively influenced by both environmental discourse presence and depth on both the platforms analyzed. Overall, our findings shed light on the presence and impact on customer satisfaction of consumers’ environmental concerns embedded in tourists’ UGC, across several leading tourism destinations based in two different continents.

Abstract

1. Introduction

2. Literature review: conceptualising BDA and SCO

3. Research methodology

4. Empirical findings

5. Proposed framework

6. Discussion and implications

7. Conclusions

Disclosure statement

Notes on contributors

References

Abstract

     Undoubtedly, due to the increasingly competitive pressures and the stride of varying demands, volatility and disturbance have become the standard in today’s global markets. The spread of Covid-19 is a prime example of that. Supply chain managers are urged to rethink their competitive strategies to make use of Big Data Analytics (BDA), due to the increasing uncertainty in both demand and supply side, the competition among the supply chain partners and the need to identify ways to offer personalised products and services. With many supply chain executives recognising the need of ‘improving with data’, supply chain businesses need to equip themselves with sophisticated BDA methods/techniques to create valuable insights from big data, thus, enhancing the decision-making process and optimising the efficiency of Supply Chain Operations (SCO). This paper proposes the building blocks of a theoretical framework for understanding the impact of BDA on SCO. The framework is based on a Systematic Literature Review (SLR) on BDA and SCO, underpinned by Task-Technology-Fit theory and Institutional Theory. The paper contributes to the literature by building a platform for future work on investigating factors driving and inhibiting BDA impact on SCO.

Introduction

     Over the past decade, the manufacturing sector has been in the midst of a fourth wave of technological advancement (Rüßmann et al. 2015). During this, a plethora of manufacturing and supply chain businesses have transformed their operations into intelligent/smart manufacturing. This is by adopting a variety of innovative technologies such as autonomous robots, simulations, horizontal and vertical systems integration, internet of things [IoT], cybersecurity, cloud services, additive manufacturing, and most importantly, big data analytics (Wamba et al. 2020). Several noticeable researchers and practitioners have recognised the significance and applicability of Industry 4.0 (I_4.0) for operations, logistics and production management at large (Sivarajah et al. 2017; Rahman et al. 2022). However, relatively little is known about the impact of BDA on SCO – particularly focussing on the five dimensions of SCO: demand planning, production and manufacturing, logistics, procurement, and inventory. Among the extant research studies published, a few shed light on the link between IoT and SCO (e.g. Ben-Daya, Hassini, and Bahroun 2019) and the impact of additive manufacturing on SCO, processes and performances (e.g. Li, Tang, et al. 2017).

Conclusions

     Arguably, SCO and BDA can enable the dynamic capabilities of firms, allowing decision makers to enhance the corporate or company abilities or to better sense emerging opportunities and threats. This paper presents the past trends and current state of BDA research published specifically in the context of SCOs and its respective dimensions. We proposed a conceptual framework based on two research questions: Investigating the benefits of implementing BDA methods/techniques to achieve better decision-making for SCO? and Investigating the benefits of implementing BDA methods/techniques in achieving optimisation of SCOs? The continuing interest and the use of BDA specifies that in future research studies academics, researchers and practitioners may focus on the factors driving and inhibiting BDA to further propose robust solutions to the service related problems. The intention in conducting this detailed investigation was to provide a useful and yet usable source of information for future researchers. The discussion also highlighted several research limitations and future directions for BDA applications within the SCO research area to catalyse the research development of the topic.

Abstract

Introduction

Related works

Integrated cybersecurity risk management (i-CSRM)

Integrated cyber security risk management tool (i-CSRMT)

Evaluation of i-CSRM

Discussion

Conclusion

Declarations

References

Abstract

     Cyber security risk management plays an important role for today’s businesses due to the rapidly changing threat landscape and the existence of evolving sophisticated cyber attacks. It is necessary for organisations, of any size, but in particular those that are associated with a critical infrastructure, to understand the risks, so that suitable controls can be taken for the overall business continuity and critical service delivery. There are a number of works that aim to develop systematic processes for risk assessment and management. However, the existing works have limited input from threat intelligence properties and evolving attack trends, resulting in limited contextual information related to cyber security risks. This creates a challenge, especially in the context of critical infrastructures, since attacks have evolved from technical to socio-technical and protecting against them requires such contextual information. This research proposes a novel integrated cyber security risk management (i-CSRM) framework that responds to that challenge by supporting systematic identification of critical assets through the use of a decision support mechanism built on fuzzy set theory, by predicting risk types through machine learning techniques, and by assessing the effectiveness of existing controls.

Introduction

     Critical infrastructures (CIs), such as energy and healthcare, heavily rely on Information and Communication Technology (ICT) to support reliable service delivery. Such integration of ICT to CIs introduces a number of advantages, such as higher degree of flexibility, scalability and efficiency in the communication and coordination of advanced services and processes. On the other hand, the increase usage of ICT in CIs creates new opportunities for cyber attacks and increases the vulnerability of those systems. Due to the importance of critical infrastructures, there are recently an increased number of attacks that are evolving in terms of sophistication, persistence and the resources that attackers have available. Such attacks consider not just the technical limitations of the relevant technologies but also the contextual information related to the critical infrastructure.

     Despite of several existing works on cybersecurity risk management, the literature fails to present works that consider such contextual information when performing risk management for critical infrastructures. Moreover, existing works focus more on the prediction of risks and do not consider—as part of the same process—necessary controls that mitigate those risks. Our work advances the state of the art through the integration of cyber threat intelligence (CTI) to the risk management process, to understand contextual information related to the threat actor’s behaviour, tactics, techniques and procedures (TTP) and indicators. Moreover, it provides a unified process that integrates both risk prediction and risk mitigation with the aid of machine learning.

Conclusion

     Risk management is a continuous process for maintaining the effective functioning of critical assets for any organisational context. In particular, critical infrastructures need resilience for the service delivery and risk management is an essential component to achieve this. The threat landscape is constantly evolving with new techniques and more sophisticated organised attacks. Therefore, it is necessary for the risk management activities to consider the threat context to assess and manage the risks. This research proposes the integrated cyber security risk management framework (i-CSRM) that adopts various existing standards and cyber threat intelligence data for risk management. i-CSRM also includes machine learning (ML) models to predicate the risk types so that organisations can undertake the necessary proactive measures to tackle the risks. The framework also includes a tool support to automate some of the risk management activities. Finally, i-CSRM is applied in a CI-based industrial context and the results of applying the framework are very promising. Specifically the studied context was able to identify and assess risks using i-CSRM and determine the right level of control for the overall business continuity. The participants’ observation is that i-CSRM is a practical approach for the risk management, and integration of CTI makes the risk management activities more effective. We believe that the proposed i-CSRM framework, its process and supporting tool will significantly impact the cybersecurity domain and state of the art in general. The i-CSRM framework focuses only on the supervised learning method, which requires labelled dataset. As a part of our future research, we would like to deploy the i-CSRM in different CI context and implement different data sets for the risk type predication. Additionally, it is necessary to develop a checklist to make the process easy to use for risk assessment and management.

Abstract

     Considering constantly increasing global competition in the market and developing technologies, information systems (ISs) have become an important component of the business world and a vital component of intelligent systems. An IS provides support for planning, controlling, analyzing activities, and support in decisions by managing data throughout the organization to assist executives in their decisions. The main function of an IS is to collect data spread between various parts of the organization and business partners and to process these collected data to form reliable information, which is required for decision making. Another critical function of an IS is to transfer the necessary information to the point-of-need in a timely manner. ISs assist in the conversion of data and information into meaningful outcomes. An IS is a combination of software, data storage hardware, related infrastructure, and people in the organization that use the system. Many business organizations rely on management information systems (MISs), and they conduct their critical operations based on these systems. The existence of an efficient MIS is a requirement for the sustainability of any business. However, MIS’s efficiency depends on the business’s requirements and nature. The compatibility of MIS with business in the company is vital for the successful implementation of these systems. The current study analyzes differences in expectations of manufacturing and service industries from MISs. For this aim, a fuzzy multi-criteria group decision-making (F-MCGDM) model is proposed to determine the differentiating success factors of MIS in both manufacturing and service industries. Findings indicate that there are considerable differences in the needs of both industries from MIS.

Introduction

     The availability of processed data is critical for any type of business, especially in a highly competitive environment, because real-time access to correct information provides executives of an organization to achieve an advantage against their competitors by taking efficient, effective, timely, and strategically reliable decisions. Collecting the required data, processing to convert it into information, and storing this information are the main functions of an IS. These systems are defined as the combination of people, hardware, software, data, procedures, and policies [1]. People in the system are called end users or clients, who use, develop and operate the system. IS design is made toward end users’ requirements by system analysts. Hardware includes computers and other equipment related to the process, storage, and transfer of data. Additionally, this hardware is a physical interface between the IS software and the end user. All these components interact with each other to handle information that flows inside the organization, and in many cases between peers in the supply chain.

     Most organizations require an information technology (IT) infrastructure. IT is concerned with modern enterprise management functions such as manufacturing, marketing, banking, and financing [2]. A well-designed IT infrastructure is a necessity for a well-performing IS. If IS is thought of as a production process in a manufacturing facility, information can be assumed as the final product. If the final product (information) cannot be delivered under a certain lead time and if it does not meet the customers’ (clients’) requirements, customers will not be satisfied with the product, and the company will lose its customers and future revenues [3]. IT contributes to innovation efforts by supplying increased communication and coordination between different departments and helps reduce the cost of transactions in an organization [4]. A successfully implemented IS, supported by a sufficient IT infrastructure, increases the performance of the whole supply chain of the organization via increased flexibility achieved in all operations between the peers in the chain [5].

Conclusions

     Requirements of today’s businesses increased the importance of ISs. Rapidly changing market conditions and customer needs force companies to react quickly to these changes. Adapting to constantly developing technological trends requires the existence of an effective MIS in organizations to aid executives in their decisions to acquire a sustainable business. However, ensuring the successful implementation of MIS requires a good fit of these systems with the organization’s business. Understanding the differences in business needs and differentiating factors related to MIS’s success must be determined for different industries. Therefore, an F-MCGDM framework is proposed in this study to fill the gap in the literature by determining the differences in expectations of manufacturing and service industries from MIS.

     The application of the proposed F-MCGDM framework to manufacturing and service industries to evaluate the success factors of MISs revealed a considerable difference between the expectations and requirements of companies in these industries from the MISs. Selecting the right MIS according to the most important factors of the company by considering the differentiating factors is important for the actual performance of MISs after the implementation.