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Rapid urbanization and development of pilgrimage cities cause significant problems for the environment and society, leading to long-term challenges. Despite several discussions on city sustainability, the literature does not address some of the specific problems of pilgrimage cities. Hence, this study attempts at developing a method to examine the growth pattern and sustainability of pilgrimage cities in southern part of India.

The benchmarking method and the social, economic and environmental dimensions of sustainability are considered to construct the Pilgrimage City Sustainability Index (PCSI). Appropriate variables and categories are identified through a literature review and expert opinion survey. The benchmark values of the variables are derived by contemplating the pilgrimage cities of Tamil Nadu, one of the states with the largest tourist arrivals in India. Subsequently, three prominent pilgrimage cities from Tamil Nadu were chosen for the case study and the method was tested.

The result reveals that the cities investigated are performing above average in the sustainability index, with slight variations in their dimension scores. While the category scores of cities assist in identifying macro-level issues, the variable scores provide an insight into micro-level issues. Furthermore, the gap analysis between the benchmark and the present value of each variable discloses the immediate area of attention in each city. Thus, the cities could set more specific targets, frame strategies and/or collaborate with matching cities to bridge these gaps.

This index assessment provides a comparison of the pros and cons of these pilgrimage cities and helps identify their demand and supply. Policymakers can find appropriate tools and approaches that aid in sustainable urban development and tourism management.

To the best of our knowledge, this is the first study in emphasizing the application of the benchmarking method to assess the sustainability of Indian pilgrimage sites. With appropriate modifications, this method can be used in varied contexts across the globe.

The purpose of this paper is to understand what inhibit or facilitate cloud computing (CC) assimilation.

The authors investigate the effects of two enablers, top management support (TMS) and government support (GS), and two inhibitors, organization inertia (OI) and data security risk (DSR) on CC assimilation. The authors posit that enablers and inhibitors influence CC assimilation separately and interactively. The research model is empirically tested by using the field survey data from 376 Chinese firms.

Both TMS and GS positively and DSR negatively influence CC assimilation. OI negatively moderates the TMS–assimilation link, and DSR negatively moderates the GS–assimilation link.

The results indicate that enablers and inhibitors influence CC assimilation in both separate and joint manners, suggesting that CC assimilation is a much more complex process and demands new knowledge to be learned.

For these firms with a high level of OI, only TMS is not enough, and top managers should find other effective way to successfully implement structural and behavioral change in the process of CC assimilation. For policy makers, they should actively play their supportive roles in CC assimilation.

A new framework is developed to identify key drivers of CC assimilation along two bipolar dimensions including enabling vs inhibiting and internal vs external.

This study aims to identify the critical factors (barriers and drivers) influencing the adoption of cloud computing (ACC) in the manufacturing sector in India.

In this study, a mixed methodology approach is used. Interviews are conducted to investigate factors (drivers and barriers) influencing the ACC, which are further categorized as controllable determinants (weaknesses and strengths) and uncontrollable determinants (threats and opportunities) using a SWOT analysis. Fuzzy analytic hierarchy process (FAHP) has been utilized to highlight the most critical drivers as well as barriers. Finally, decision-making trial and evaluation laboratory (DEMATEL) has been used to find the cause-effect relationships among factors and their influence on the decision of adoption.

The manufacturing sector is in the digital and value change transformation phase with Industry 4.0, that is, the next industrial revolution. The 24 critical factors influencing ACC are subdivided into strengths, weaknesses, opportunities and threats. The FAHP analysis ranked time to market, competitive advantage, business agility, data confidentiality and lack of government policy standards as the most critical factors. The cause-effect relationships highlight that time to market is the most significant causal factor, and resistance to technology is the least significant effect factor. The results of the study elucidate that the strengths of ACC are appreciably more than its weaknesses.

This study couples the technology acceptance model (TAM) with technology-organization-environment (TOE) framework and adds an economic perspective to examine the significant influences of ACC in the Indian manufacturing sector. Further, it contributes to the knowledge of ACC in general and provides valuable insights into interrelationships among factors influencing the decision and strategies of adoption in particular.

This is the first scholarly work in the Indian manufacturing sector that uses the analysis from SWOT and FAHP approach as a base for identifying cause-effect relationships between the critical factors influencing ACC. Further, based on the extant literature and analysis of this work, an adoption framework has been proposed that justifies that ACC is not just a technological challenge but is also an environmental, economic and organizational challenge that includes organizational issues, costs and need for adequate government policies.

The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method.

This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques.

The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively.

The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.

Software-as-a-Service (SaaS) has the potential to provide substantial opportunities for organizations to improve their information technology without cost and management concerns. However, organizations have not utilized it to the desired level because it is very challenging for them to completely transform their basic conventional methods of running software into SaaS as a high-tech method. On the other hand, organizations have doubt which factors should be mostly considered if they want to move to SaaS. Therefore, investigating the adoption of SaaS can contribute organizations to benefit from this technology. The purpose of this paper is to provide a good insight into SaaS technology adoption.

Considering Technology, Organization and Environment (TOE) framework and diffusion of innovation (DOI) theory as the basis, 22 university experts expressed their idea about the proposed model of SaaS adoption. Then, 30 IT professional in 15 IT enterprises that had adopted SaaS were asked to fill the questionnaire related to fuzzy Analytic Hierarchy Process (AHP) based on linguistic preference relations (LinPreRa) in order to rank the submitted criteria.

The findings demonstrate that all attributes of Technology (relative advantage, compatibility, complexity, trialability, observability and security and privacy), Organization (IT resource, sharing and collaboration culture) and environment (competitive pressure, social influence) are influential in the adoption of SaaS. Moreover, the top five influential factors are relative advantage, competitive pressure, security and privacy, sharing and collaboration culture and social influence based on adopter’s opinions.

For researchers, this study provides a useful literature, which can help them in related subject. In addition, it applies IT adoption theories in SaaS context that can be extended in future studies. For organizations, this study derives priority of factors by which they can make strong decisions about adoption of SaaS.

This study contributes to the adoption of SaaS technology using well-known IT adoption theories. A version of Fuzzy AHP based on LinPreRa was used in order to cover the limitations of previous methodologies of ranking the criteria.

Reflecting on Big Data’s assumed benefits, this study aims to identify the risks and challenges of data security underpinning Big Data’s socio-economic value and intellectual capital (IC).

The study reviews academic literature, professional documents and public information to provide insights, critique and projections for IC and Big Data research and practice.

The “voracity” for data represents a further “V” of Big Data, which results in a continuous hunt for data beyond legal and ethical boundaries. Cybercrimes, data security breaches and privacy violations reflect voracity and represent the dark side of the Big Data ecosystem. Losing the confidentiality, integrity or availability of data because of a data security breach poses threat to IC and value creation. Thus, cyberthreats compromise the social value of Big Data, impacting on stakeholders’ and society’s interests.

Because of the interpretative nature of this study, other researchers may not draw the same conclusions from the evidence provided. It leaves some open questions for a wide research agenda about the societal, ethical and managerial implications of Big Data.

This paper introduces the risks of data security and the challenges of Big Data to stimulate new research paths for IC and accounting research.

This study aims to identify the level of security from existing work, analyze categories of security as a service (SECaaS) and classify them into a meaningful set of groups. Further, the report will advise commercial applications and advice of SECaaS as an extended context to help firms make decisions.

This paper compares the SECaaS categories in Cloud Security Alliance (CSA) with the security clauses in ISO/IEC 27002:2013 to give a comprehensive analysis of those SECaaS categories. Reviewed from a number of related literature, this paper analyzes and categorizes SECaaS into three major groups including protective, detective and reactive based on security control perspectives. This study has discussed the three groups and their interplay to identify the key characteristics and problems that they aim to address.

This paper also adds new evidence to support a better understanding of the current and future challenges and directions for SECaaS. Also, the study reveals both the positive and negative aspects of SECaaS along with business cases. It advises on various sizes and domains of organizations to consider SECaaS as one of their potential security approaches.

SECaaS has been demonstrated to be one of the increasingly popular ways to address security problems in Cloud computing. As a new concept, SECaaS could be treated as integrated security means and delivered as a service module in the Cloud. However, it is still in infancy and not very widely investigated. Recent studies suggest that SECaaS is an efficient solution for Cloud and real industries. However, shortcomings of SECaaS have not been well-studied and documented. Moreover, reviewing the existing research, researchers did not classify the SECaaS-related categories.

The significance of cloud services in information technology (IT) is increasing as a means of achieving enhanced productivity, efficiency and cost reduction. Through cloud-based service, the reliability and scalability of an organization’s systems can be enhanced since organizations such as local governments are able to concentrate on their main business strategies. This research seeks to identify critical factors that may have an impact on the acceptance of cloud-based services, where the organizational context is based on local governments in Australia.

To formulate a more comprehensive IT innovation adoption model for cloud technology, factors from the technology-organizational-environment framework, desires framework and diffusion of innovation model were integrated. Data was obtained from 480 IT staff working in 47 local government organizations.

The research results show that the factors which had a statistically significant and positive impact on the adoption of cloud-based services in local governments were compatibility, complexity, cost, security concerns, expected benefits and organization size. It is likely that the outcomes from this research will provide insights to any organization seeking to make investment decisions on the adoption of cloud-based services.

Limitations include generalizability of the findings since the data is restricted to local government areas in Queensland, Australia. Further, the sample mostly included individuals with managerial positions and may not completely capture the cloud adoption factors relevant for front line IT employees. Another limitation is the possible omission of factors that may be relevant but not considered due to the selected theories. Lastly, this research did not differentiate between different types of cloud adoption such as private, public, community and hybrid models that are possible in this context.

The paper provides a combination framework of cloud-based service adoption based on a literature review on cloud adoption from an IS perspective. It adapts integrated model to establish a more comprehensive innovation adoption framework for cloud technology.

To effectively develop privacy policies and practices for cloud computing, organizations need to define a set of guiding privacy objectives that can be applied across their organization. It is argued that it is important to understand individuals’ privacy values with respect to cloud computing to define cloud privacy objectives.

For the purpose of this study, the authors adopted Keeney’s (1994) value-focused thinking approach to identify privacy objectives with respect to cloud computing.

The results of this study identified the following six fundamental cloud privacy objectives: to increase trust with cloud provider, to maximize identity management controls, to maximize responsibility of information stewardship, to maximize individual’s understanding of cloud service functionality, to maximize protection of rights to privacy, and to maintain the integrity of data.

One limitation is generalizability of the cloud privacy objectives, and the second is research bias. As this study focused on cloud privacy, the authors felt that the research participants’ increased knowledge of technology usage, including that of cloud technology, was a benefit that outweighed risks associated with not having a random selection of the general population. The newness and unique qualities of privacy issues in cloud computing are better fitted to a qualitative study where issues can emerge naturally through a holistic approach opposed to trying to force fit an existing set of variables or constructs into the context of privacy and cloud computing.

The findings of this research study can be used to assist management in the process of formulating a cloud privacy policy, develop cloud privacy evaluation criteria as well as assist auditors in developing their privacy audit work plans.

Currently, there is little to no guidance in the literature or in practice as to what organizations need to do to ensure they protect their stakeholders privacy in a cloud computing environment. This study works at closing this knowledge gap by identifying cloud privacy objectives.

The purpose of this paper is to integrate TAM model and TOE framework for cloud computing adoption at organizational level.

A conceptual framework was developed using technological and organizational variables of TOE framework as external variables of TAM model while environmental variables were proposed to have direct impact on cloud computing adoption. A questionnaire was used to collect the data from 280 companies in IT, manufacturing and finance sectors in India. The data were analyzed using exploratory and confirmatory factor analyses. Further, structural equation modeling was used to test the proposed model.

The study identified relative advantage, compatibility, complexity, organizational readiness, top management commitment, and training and education as important variables for affecting cloud computing adoption using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Also, competitive pressure and trading partner support were found directly affecting cloud computing adoption intentions. The model explained 62 percent of cloud computing adoption.

The model can be used as a guideline to ensure a positive outcome of the cloud computing adoption in organizations. It also provides relevant recommendations to achieve conducive implementation environment for cloud computing adoption.

This study integrates two of the information technology adoption models to improve predictive power of resulting model.