An Integrated SEM-Neural Network for Predicting and Understanding the Determining Factor for Institutional Repositories Adoption

Asadi, Shahla; Abdullah, Rusli; Jusoh, Yusmadi Yah

doi:10.1007/978-3-030-29513-4_38

Cited by 4 publications

(6 citation statements)

References 35 publications

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“…The factor "intention", which is the dependent variable in the proposed study, has been calculated in the output layer of the ANN model. The root-mean-square error (RMSE) was used to assess the precision of the ANN model that was developed [195] in both the training and testing datasets. As shown in Table 8, the average RMSE values of the training and testing procedures are relatively small at 0.1101 and 0.1022, respectively.…”

Section: Analysis Of Neural Network For Cloud Computing Adoptionmentioning

confidence: 99%

A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

et al. 2020

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Cloud computing (CC) delivers services for organizations, particularly for higher education institutions (HEIs) anywhere and anytime, based on scalability and pay-per-use approach. Examining the factors influencing the decision-makers’ intention towards adopting CC plays an essential role in HEIs. Therefore, this study aimed to understand and predict the key determinants that drive managerial decision-makers’ perspectives for adopting this technology. The data were gathered from 134 institutional managers, involved in the decision making of the institutions. This study applied two analytical approaches, namely variance-based structural equation modeling (i.e., PLS-SEM) and artificial neural network (ANN). First, the PLS-SEM approach has been used for analyzing the proposed model and extracting the significant relationships among the identified factors. The obtained result from PLS-SEM analysis revealed that seven factors were identified as significant in influencing decision-makers’ intention towards adopting CC. Second, the normalized importance among those seven significant predictors was ranked utilizing the ANN. The results of the ANN approach showed that technology readiness is the most important predictor for CC adoption, followed by security and competitive pressure. Finally, this study presented a new and innovative approach for comprehending CC adoption, and the results can be used by decision-makers to develop strategies for adopting CC services in their institutions.

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Section: Analysis Of Neural Network For Cloud Computing Adoptionmentioning

confidence: 99%

A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

et al. 2020

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“…The above-stated researchers have all proven the positive association between EO of employees and BDAC. Besides technological meaning, BDA offers a new era of intelligence and knowledge (named as big data analytical capability) to make dynamic managerial decisions for an entrepreneurial-oriented firm (Shamim et al, 2019) that is empirically evidenced by various researchers and scholars in last one decade (Asadi et al, 2020;Aydiner et al, 2019a, b;Ciampi et al, 2021;Dubey et al, 2020). As mentioned above, researchers presented the significant association of EO and BDAC, but it lacks in the specific context of four-and five-stars hotels of Malaysia that are intended to address in this study.…”

Section: Entrepreneurial Orientation and Big Data Analytical Capabili...mentioning

confidence: 99%

“…Although, big data, AI and entrepreneurial orientation have gained substantial attraction in recent studies (Abubakar et al, 2019;Cenamor et al, 2019;Dubey et al, 2019;Gupta et al, 2018). Nonetheless, inadequate studies have been conducted that broadly contemplate the noteworthy forerunner of firms' intention to adopt these technologies and their relevance (Asadi et al, 2020). In particular, tourism-related big data and AI have not been explicitly explored in the literature (Doborjeh et al, 2022;Li et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…(2022),Chen et al (2020),Ciasullo et al (2022), andDubey et al (2020) have shown the positive direct association between EO and IT-based capabilities (named as big data analytical capabilities and AIC). For this,Asadi et al (2020) andLafuente et al (2023) empirically tested that technological revolutions urge the service sector to develop IT-based capabilities to coinnovate the business processes and its outcomes.…”

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confidence: 99%

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Augmenting hotel performance in Malaysia through big data analytics capability and artificial intelligence capability

Naz,

Haider,

Khan

et al. 2023

JHTI

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PurposeAt the forefront of current research is the investigation of how big data analytics capability (BDAC) and artificial intelligence capability (AIC) can enhance performance in concert. Therefore, current study intended to conduct more deep research into emerging phenomena and attempts to cover the gap by exploring how entrepreneurial orientations (EO) emphasize the use of two emerging capabilities under the moderating role of environmental dynamism which in turn augment co-innovation and hotel performance.Design/methodology/approachData were collected from four-star and five-star hotels located in Kula Lumpur and Langkawi in Malaysia. A total of 260 responses were obtained from IT staff and senior managers with the assistance of a Manpower agency for data analysis. The hypotheses were examined by analyzing the data using PLS-SEM technique through Smart PLS 3 software.FindingsThe result revealed that EO has a positive and significant effect on co-innovation (CIN). Additionally, the BDAC and AIC have been tested and proven to be potential mediators between EO and CIN. Also, environmental dynamism as moderator has positive and significant effect on BDAC and co-innovation performance, however, not significant impact on AIC and co-innovation performance. Lastly, findings displayed positive and significant moderated mediation impact of environmental dynamics on BDAC and CIN with hotel performance, but not significant influence on AIC and co-innovation with hotel performance. For theoretical corroboration of the research findings, the current study integrated EO, resource-based view theory and contingent dynamic capabilities (CDC), because neither single stance can explicate an extant research framework.Practical implicationsThis study anticipated the several implications for the entrepreneurs of hospitality industry. Managers are recommended to invest in the entrepreneurial traits of the employees/organizations and make strategic readjustment of their capabilities for sustained business performance.Originality/valueThe study goes beyond the normal inquiry by investigating moderated mediation impact of environmental dynamism between two emerging capabilities, co-innovation and hotel performance relationships. Another novelty of this study is to culminate the exploitation and adoption of emerging IT-based capabilities in cross domains of management, entrepreneurship, information systems management within the hotel industry.

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“…The output layer of the ANN model was used to calculate the factor "resilience," which is the dependent variable in this study. In both the training and testing datasets, sum of square error (SSE) and percent incorrect predictions (PIP) was utilized to determine the precision of the ANN model produced (Asadi et al, 2019). As indicated in Table 1, the average SSE for the training and testing methods is 66.1064 and 29.61, respectively, whereas the average PIP for training and testing is 33.75% and 35.11%.…”

Section: Neural Network Analysismentioning

confidence: 99%

Revisiting Social Media Crisis Communication Model for Building Resilience via Artificial Neural Network Analysis

Bukar¹,

Jabar²,

Sidi³

et al. 2021

IJARBSS

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The devastating effects of crises and how social media usage assists or informs stakeholders are attracting attention recently. This study explored the literature on social media crisis communication and resilience and evaluated the predictors of public resilience and the impact of social media activities on public resilience. Using the artificial neural network (ANN) approach, this study sought to determine the effect of the crisis, crisis response, and social media interaction on public resilience. The ANN evaluates the mean value of the normalized performance from ten neural networks generated through cross-validation. The results indicate crisis response and social media interaction as the most significant predictors of resilience. Also, the findings show that it is desirable to continue studying the public's roles, motives, and actions on social media. The study offered theoretical justification to advance effective social media crisis management and communication.

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An Integrated SEM-Neural Network for Predicting and Understanding the Determining Factor for Institutional Repositories Adoption

Cited by 4 publications

References 35 publications

A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

Augmenting hotel performance in Malaysia through big data analytics capability and artificial intelligence capability

Revisiting Social Media Crisis Communication Model for Building Resilience via Artificial Neural Network Analysis

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