Search results

The purpose of this paper is to explore the effects of personality traits on teachers’ technology acceptance.

To this end, a demographic information survey, Five-Factor inventory, and technology acceptance measure were used for data collection. The data were analyzed via structural equation modeling.

According to the findings, conscientiousness has no significant effect on behavioral intention (BI) whereas openness and extraversion have positive indirect effects. On the other hand, agreeableness and neuroticism have negative indirect effects on BI. It was seen that the developed model explains 70 percent of BI, 77 percent of attitude toward use, 42 percent of perceived usefulness, 64 percent of perceived ease of use, and 15 percent of computer self-efficacy (CSE). It was revealed that CSE mediates the indirect effects of different personality traits on these variables.

In this regard, highlighting the conveniences to be brought by using Tablet PCs and equipping teachers with competencies to use relevant technologies during teacher training and in-service training may be effective in developing BI to use Tablet PCs among teachers.

The present study is different from previous studies in that it examines more than one technology through more than one measurement, takes personality as the predictor of technology use tendencies, and focuses on causal relationships between technology use and personality.

Due to its ability to support well-informed decision-making, business intelligence (BI) has grown in popularity among executives across a range of industries. However, given the volume of data collected in health-care organizations, there is a lack of exploration concerning its implementation. Consequently, this research paper aims to investigate the key factors affecting the acceptance and use of BI in healthcare organizations.

Leveraging the theoretical lens of the “unified theory of acceptance and use of technology” (UTAUT), a study framework was proposed and integrated with three context-related factors, including “rational decision-making culture” (RDC), “perceived threat to professional autonomy” (PTA) and “medical–legal risk” (MLR). The variables in the study framework were categorized as follows: information systems (IS) perspective; organizational perspective; and user perspective. In Jordan, 434 healthcare professionals participated in a cross-sectional online survey that was used to collect data.

The findings of the “structural equation modeling” revealed that professionals’ behavioral intentions toward using BI systems were significantly affected by performance expectancy, social influence, facilitating conditions, MLR, RDC and PTA. Also, an insignificant effect of PTA on PE was found based on the results of statistical analysis. These variables explained 68% of the variance (R²) in the individuals’ intentions to use BI-based health-care systems.

To promote the acceptance and use of BI technology in health-care settings, developers, designers, service providers and decision-makers will find this study to have a number of practical implications. Additionally, it will support the development of effective strategies and BI-based health-care systems based on these study results, attracting the interest of many users.

To the best of the author’s knowledge, this is one of the first studies that integrates the UTAUT model with three contextual factors (RDC, PTA and MLR) in addition to examining the suggested framework in a developing nation (Jordan). This study is one of the few in which the users’ acceptance behavior of BI systems was investigated in a health-care setting. More specifically, to the best of the author’s knowledge, this is the first study that reveals the critical antecedents of individuals’ intention to accept BI for health-care purposes in the Jordanian context.

The purpose of this study was to demonstrate a cloud business intelligence model for industrial SMEs. An initial model was developed to accomplish this, followed by validation and finalization of the cloud business intelligence model. Additionally, this research employs a mixed-techniques approach, including both qualitative and quantitative methods. This paper aims to achieve the following objectives: (1) Recognize the Cloud business intelligence concepts. (2) Identify the role of cloud BI in SMEs. (3) Identify the factors that affect the design and presenting a Cloud business intelligence model based on critical factors affecting SMEs during pandemic COVID-19. (4) Discuss the importance of Cloud BI in pandemic COVID-19 for SMEs. (5) Provide managerial implications for using Cloud BI effectively in Iran’s SMEs.

In the current study, an initial model was first proposed, and the cloud business intelligence model was then validated and finalized. Moreover, this study uses a mixed-methods design in which both qualitative and quantitative methods are used. The fuzzy Delphi Method has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. The partial least squares method was also applied to validate the model. Data were also analyzed using the MAXQDA and Smart PLS software package.

In this research, from the elimination of synonym and frequently repeated factors and classification of final factors, six main factors, 24 subfactors and 24 identifiers were discovered from the texts of the relevant papers and interviews conducted with 19 experts in the area of BI and Cloud computing. The main factors of our research include drivers, enablers, competencies, critical success factors, SME characteristics and adoption. The subfactors of included competitors pressure, decision-making time, data access, data analysis and calculations, budget, clear view, clear missions, BI tools, data infrastructure, information merging, business key sector, data owner, business process, data resource, data quality, IT skill, organizational preparedness, innovation orientation, SME characteristics, SME activity, SME structure, BI maturity, standardization, agility, balances between BI systems and business strategies. Then, the quantitative part continued with the fuzzy Delphi technique in which two factors, decision-making time and agility, were deleted in the first round, and the second round was conducted for the rest of the factors. In that step, 24 factors were assessed based on the opinions of 19 experts. In the second round, none of the factors were removed, and thus the Delphi analysis was concluded. Next, data analysis was carried out by building the structural self-interaction matrix to present the model. According to the results, adoptability is a first-level or dependent variable. Regarding the results of interpretive structural modeling (ISM), the variable of critical success factors is a second-level variable. Enablers, competencies and SME characteristics are the third-level and most effective variables of the model. Accordingly, the initial model of Cloud BI for SMEs is presented as follows: The results of ISM revealed the impact of SME characteristics on BI critical success factors and adoptability. Since this category was not an underlying category of BI; thus, it played the role of a moderating variable for the impact of critical success factors on adoptability in the final model.

Since this study is limited to about 100 SMEs in the north of Iran, results should be applied cautiously to SMEs in other countries. Generalizing the study's results to other industries and geographic regions should be done with care since management perceptions, and financial condition of a business vary significantly. Additionally, the topic of business intelligence in SMEs constrained the sample from the start since not all SMEs use business intelligence systems, and others are unaware of their advantages. BI tools enable the effective management of companies of all sizes by providing analytic data and critical performance indicators. In general, SMEs used fewer business intelligence technologies than big companies. According to studies, SMEs understand the value of simplifying their information resources to make critical business choices. Additionally, they are aware of the market's abundance of business intelligence products. However, many SMEs lack the technical knowledge necessary to choose the optimal tool combination. In light of the frequently significant investment required to implement BI approaches, a viable alternative for SMEs may be to adopt cloud computing solutions that enable organizations to strengthen their systems and information technologies on a pay-per-use basis while also providing access to cutting-edge BI technologies at a reasonable price.

Before the implementation of Cloud BI in SMEs, condition of driver, competency and critical success factor of SMEs should also be considered. These will help to define the significant resources and skills that form the strategic edge and lead to the success of Cloud BI projects.

Most of the previous studies have been focused on factors such as critical success factors in cloud business intelligence and cloud computing in small and medium-sized enterprises, cloud business intelligence adoption models, the services used in cloud business intelligence, the factors involved in acceptance of cloud business intelligence, the challenges and advantages of cloud business intelligence, and drivers and barriers to cloud business intelligence. None of the studied resources proposed any comprehensive model for designing and implementing cloud business intelligence in small and medium-sized enterprises; they only investigated some of the aspects of this issue.

The purpose of this paper is to explore the role of business intelligence (BI) in a consulting company. The authors propose to analyze quality through data analysis and efficiency under different business contexts. The best processes and tools in data mining are also explored.

Management perspectives of data analysis from Florilla Consulting Company are collected using an inductive research approach. Based on a large sample of qualitative data, cost-and-benefit analysis is used to assess the BI technologies as a strategic necessity to Florilla Consulting Company.

Findings classify the best processes and tools of data analysis under different business scenarios. The authors also propose a revised process and tools for Florilla Consulting Company to be further evaluated by future research.

The insights offered in this paper derives authentic value for any consulting company that is interested to benefit from the opportunities bought by the BI technologies. Quality management also gets a new dimension when technology is integrated into business.

This study has challenged the way quality is managed in Florilla Consulting Company. The connection of BI to quality management is explored via an empirical study of a consulting company by linking theory with practice.

A crucial question still remains unanswered as to whether data analytics-oriented business intelligence (hereafter, BI) technologies can bring organizational value and benefits. Thereby, several researchers called for further empirical research to extend the limited knowledge in this critical area. In an attempt to deal with this issue, we presented and tested a theoretical model to assess BI effectiveness at the organizational benefits level in this research article.

The suggested research model expands the application of the DeLone and McLean model in BI technology success or effectiveness research from individual level to organizational level. A cross-sectional survey is developed to obtain primary quantitative data from business and technology managers who are depending on BI technologies to make operational, technical and strategic decisions in Jordanian-listed firms.

Empirical findings show that system quality, information quality and training quality are significant predictors of user satisfaction, but not of perceived benefit. Data quality was found to be a strong predictor of both perceived benefit and user satisfaction. The influence of perceived benefit on user satisfaction was significant in turn both factors positively affect organizational benefits.

This research paper is a pioneering effort to assess BI technology effectiveness at an organizational level outside the context of developed countries. To the best of the authors’ knowledge, no prior research has combined all dimensions used in this research in one single model.

In this research, we empirically examine the impact of Business Intelligence (BI) systems on operational capability in high-tech sectors. We also seek to understand the contextual factors that facilitate the adoption of BI systems.

We adopt Propensity Score Matching (PSM) and event study methodology, and analyze the financial data for a sample of 144 US firms which adopted BI systems from 2005–2014, and compare them to control firms without BI systems.

We find that the implementation of BI systems leads to higher operational capability, particularly for large high-tech firms with high technology intensity. We further show that technology intensity and firm size are important contextual factors for firms to reap the benefits of BI systems.

We demonstrate how benefits from the adoption of BI systems are likely to be strengthened. The benefits of BI systems depend on firms' technology intensity and firm size of high-tech firms. Accessing relevant and timely reports for decision-making is particularly important in the highly dynamic, volatile and competitive high-tech sectors.

We contribute to the literature by providing empirical evidence that the adoption of BI systems can improve firms' operational capability and show that technology intensity and firm size are important contextual factors for firms to reap the benefits of BI systems. We advance the understanding regarding the contextual factors in which firms are more likely to gain additional benefits from their adoptions of BI systems.

For the success of future investments in the implementation of continuous authentication systems, we should explore the key factors that influence technology adoption. The authors investigate the effect of various factors of behavioral intention through the new incorporation of a modified technology acceptance model (TAM) and diffusion of innovation theory (DOI). Also, the authors have created a new theoretical framework with constructs such as security and privacy risks (SPR), biometrics privacy concerns (BPC) and perceived risk of using the technology (PROU). In this paper, the authors conducted a structural equation modeling empirical research. This research is designed in such a way to respond to the trade-off between users’ concern for the protection of their biometrics privacy and their protection from risks.

The authors provide an extensive conceptual framework for both existing models (TAM and DOI) and the new constructs that the authors have added to the model. In addition, this research explores external factors, such as trust in technology (TT) and innovativeness (Innov). In addition, the authors have introduced significant constructs, to overcome the limitations of the TAM and to adapt it to the needs of the present research. The new theoretical framework the authors introduce in the present research concerns the constructs SPR, BPC and PROU.

The authors found that the main facilitators of behavioral intention to adopt the technology (BI) are TT, followed by compatibility (COMP), perceived usefulness (PU) and Innov. This research also shows that individuals are less interested in the ease of use of the technology and are willing to sacrifice it to achieve greater security. COMP and Innov also play a significant role. Individuals who believe that the usage of the behavioral biometrics continuous authentication (BBCA) technology would fit into their lifestyle and would like to experiment with new technologies have a positive intention to adopt the BBCA technology. The new constructs the authors have added are SPR, BPC and PROU. The authors’ results support the hypotheses that SPR is a facilitator to PU and PU acts as a facilitator to BI. Consequently, the hypothesis that individuals do not feel adequately protected by classical methods will consider the usefulness of the BBCA as a technology for their extra protection against risks is confirmed by the model. Also, with the constructs BPC and PROU, the authors examined if individuals’ concerns regarding their biometrics privacy act as inhibitors in the BI. The authors concluded that individuals consider that the benefits of using BBCA technology are much more important than the risks for their biometrics privacy since the hypothesis that the major inhibitor of BI is PROU is not supported by the model.

To the best of the authors’ knowledge, this research is among the first in the field that examines the factors that influence the individuals’ decision to adopt BBCA technology.

Artificial Intelligence-based smart farming technologies have brought impressive changes in farming. This paper aims at exploring the farmers’ intention to adopt smart farming technologies (SFT). Also, the authors intend to know how far the belief of farmers on land as God influences their decision to adopt SFT.

The data were gathered from 500 farmers chosen purposively. A well-crafted survey instrument was employed to amass data from farmers for measuring their adoption of SFT. As the authors sought to measure the farmers’ behavioural intention (BI) towards the adoption of SFT, the technology acceptance model developed by Davis (1989) came in handy, including perceived usefulness (PU), perceived ease of use (PEU) and BI. The authors have adopted this model as it was considered a superior model. The items on the attitude of confidence (AC) were adapted from Adrian et al. (2005). Survey instruments of Thompson and Higgins (1991) and Compeau and Higgins (1995) were also referred to finalize the statements relating to attitude towards use. Moreover, the authors developed items relating to the perceived belief of land as God based on frequent interaction with the farmers.

The study results divulged that attitude to use (AU) is directly influenced by the rural farmers’ PU, PEU and AC. Similarly, this investigation has observed behaviour intention directly influenced by the AU of farmers. It is observed that AU was the most influential variable, which ultimately influenced the BI to use SFT.

This study has an important limitation in the form of representing only the culture, belief and value system of farmers in India.

The outcome of this study will facilitate the policymakers to draw suitable policy measures keeping the sensitivities of the farmers in mind in their technology adoption drive. The agricultural officers can encourage farmers to take logical decisions by supplying adequate information in a time-bound manner. Marketers can make suitable adjustments in their sales and promotion activities that focus on farmers.

The belief of farmers on land as God has a small yet unmissable influence on farmers’ AU and BI in their technology adoption decision. Based on the above evidence, the authors recommend that marketers fine-tune their product design, product packaging and promotional activities keeping the belief and sensitivities of farmers at the core of their marketing campaign.

This article provides original insights by demonstrating the positive influence of PU, PEU and AC on technology adoption by farmers. This research is the first of a kind linking the belief of farmers on land as God with smart farming technology adoption in farming.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

The purpose of this paper is to study the role of business intelligence (BI) in achieving agility in supply chain context by examining the relationship between BI competence, agile capabilities, and agile performance of the supply chain.

A theoretical framework is developed drawing on the resource-based view, the dynamic capabilities perspective, and the competence-capability relationship paradigm, as well as an extensive review of the literature. Structural equation modeling is employed to analyze the data collected from Iranian manufacturers in the automotive industry.

The empirical results support the conceptualization of supply chain BI competence as a multi-dimensional construct comprising managerial, technical, and cultural competence, and confirm that it is a key enabler of supply chain agility in terms of both agile capabilities and agile performance. The results also provide support for partial mediation of agile capabilities on the relationship between BI competence and agile performance of the supply chain.

This paper provides a response to the identified need for empirical evidence on the benefits derived from BI, especially in the supply chain context. It also contributes to the existing supply chain agility literature by providing insight into the value and role of BI in enhancing agile capabilities and performance in the inter-organizational supply chain.