Search results

Organizations are fast adopting new technologies such as automation, analytics and artificial intelligence, collectively called intelligent automation, to drive digital transformation. When adopting intelligent automation, there is a need to understand the success factors of these new technologies and adapt agile software development (ASD) practices to meet customer expectations. The purpose of this paper is to explore the success factors of intelligent automation and create a framework for managers and practitioners to meet dynamic business demands. Total interpretive structural modeling (TISM) framework is a suitable approach to integrate quantitative measurement with qualitative semi-structured interviews capturing the context of the individual organization environment.

This paper identified agility factors and their interrelationships using a TISM framework. TISM results were validated using a one-tailed t-test to confirm the interrelationships between factors. Furthermore, the agility index of a case project organization was assessed using a graph-theoretic approach (GTA) to identify both the triggering factors for agility success and improvement proposals.

Results showed that leadership vision, organization structure and program methodology were driving factors. The TISM model was validated statistically and the agility index of the intelligent automation case project organization was calculated to be79.5%. Here, a GTA was applied and the triggering factors for improvement of the agility index were identified.

The limitations of the study are described along with the opportunities for future research as the field evolves through the rapid innovation of technology and products.

The increasing role of digital transformation in enterprise strategy and operations requires practitioners to understand how ASD practices must be planned, measured and/or improved over time through the implementation of automation, analytics and artificial intelligence programs. The TISM digraph provides a framework of hierarchical structure to organize the influencing factors, which assists in achieving organizational goals. This study highlights the driving factors which contribute to the success of intelligent automation projects and project organizations.

This is a first attempt to analyze the interrelationships among agility factors in intelligent automation projects (IAP) using TISM and the assessment of the agility index of a case IAP organization using a GTA.

This study aims to identify the factors influencing agile readiness in start-ups. Start-ups are being confronted with increased competition, customer demands, technological innovations and changes in the market environment. Adopting agile readiness for sustainable operations is a profitable and dependable way to improve the competition and reduce the number of failures of start-ups. The start-ups may investigate “how” after understanding the “whys.” The answers to these questions will be crucial to develop a strategy and a plan for luring clients, users, investors and partners. Therefore, this study will help in answering these crucial questions by using Total Interpretive Structural Modeling (TISM), whose main aim is to answer the key question of “what,” “how” and “why.” Using the “Total Interpretive Structural Modeling (TISM)” technique, this research tries to “describe,” “analyze” and “categorize” the agile readiness for sustainable operations enablers in start-ups.

Expert feedback and literature reviews from various start-ups led to the discovery of 10 enablers. In this study, the TISM technique was used to examine the inter-relationships between the enablers. The agile readiness for sustainable operations enablers was ranked and classified using the “Multiplication Applied to Classification (MICMAC)” technique. They were divided into four different categories: “autonomous,” “independent,” “linkage” and “dependent enablers.”

The results show that executive-level aid is the key agile readiness factor for sustainable operations. The next priority has been capability, experienced and skilled employees, market knowledge and environment agility. Leadership and clear vision have been given further priority. The next important is flexibility. The last and the least priority is given to receptive and strategic agility. This directional flow assists management in attaining adaptable sustainability, leading to continued growth in this dynamic environment.

The study focuses primarily on the agile readiness for sustainable operations of start-ups. This study offers a recommended list of crucial elements for start-ups, which may aid in creating guidelines for implementing agility for sustainable operations. This study provides academics with a TISM model that illustrates how start-ups can be ready to implement agility for sustainable operations. Future researchers could add more agility readiness variables to this study and validate this model across different start-ups.

Before implementing agile readiness for sustainable operations in start-ups, this study will aid managers and practitioners in the start-up business in understanding the relationships of enablers and identifying important readiness enablers.

The current study analyzes the agile readiness for sustainable operations in Start-ups. To the best of the authors’ knowledge, it is the first endeavor to use the “TISM approach” to examine the interrelationships across agile readiness for sustainable operations characteristics in start-ups.

Agile principles have been widely used in software development team practice since the creation of the Agile Manifesto. Studies have examined variables related to agile principles without systematically considering the relationships among key team, agile methodology, and process variables underlying the agile principles and how these variables jointly influence the achievement of software development agility. In this study, the authors tested a team/methodology–process–agility model that links team variables (team autonomy and team competence) and methodological variable (iterative development) to process variables (communication and collaborative decision-making), which are in turn linked to software development agility (ability to sense, respond and learn).

Survey data from one hundred and sixty software development professionals were analyzed using structural equation modeling methods.

The results support the team/methodology–process–agility model. Process variables (communication and collaborative decision-making) mediated the effects of team (autonomy and competence) and methodological (iterative development) variables on software development agility. In addition, team, methodology and process variables had different effects on the three dimensions of software development agility.

The results contribute to the literature on organizational IT management by establishing a team/methodology–process–agility model that can serve as a basis for developing a core theoretical foundation underlying agile principles and practices. The results also have practical implications for organizations in understanding and managing holistically the different roles that agile methodological, team and process factors play in achieving software development agility.

The purpose of this paper is to identify and analyze the interactions among different readiness factors for implementing agility in healthcare organization. Total interpretive structural modeling (TISM) based readiness framework for agility has been developed to understand the mutual interactions among the factors and to identify the driving and dependence power of these factors.

The identification of factors is done by TISM approach used for analyzing the mutual interactions between factors. Cross-impact matrix multiplication applied to classification analysis is utilized to find the driving and dependent factors of agile readiness in healthcare.

This paper identifies 12 factors of readiness for change in literature review, which is followed by an expert interview to understand the interconnection of factors and to study interrelationships of factors. The study suggests that factors like environmental scanning, resource availability, innovativeness, cost effectiveness, organizational leadership, training and development are important for implementing/improving the readiness of agility in healthcare organizations.

This research focuses mainly on readiness factors for agility in healthcare sector.

Top management must stress on readiness factors that have a strong driving power for efficient implementation of agility in healthcare. This study helps the managers to take quick decisions, and continuous monitoring of readiness factors would be more beneficial to improve the quality of service, which makes the organization more agile.

In this research, TISM-based readiness for agile framework structural model has been proposed for healthcare organizations, which is a new effort for implementation of agility in healthcare.

According to the definitions presented for agility drivers, it can be mentioned that supply chain risk factors in terms of feature have very close roles to agility drivers and both of these factors impact the uncertainty in the environment. However, the risk factors with a wider domain are more comprehensive. This study aims to examine the effectiveness of risk as a driver on the organizational agility.

To conduct the research, after identification of influential risk factors of supply chain and indicators of organizational agility, questionnaires needed for the research were designed and after confirming their validity and reliability were distributed among the member of the sample. To assess the relationship between supply chain risk and the amount of organizational agility, data obtained from the questionnaires were analyzed using structural equation modeling technique.

The results of data analysis showed that supply chain risk factors could be considered as a driver affecting the organizational agility. In addition, in this study, supply chain risk factors were ranked using interpretive structural modeling. The presented comprehensive model indicated that based on causal relationships between risk factors, sovereign risk as the basis for model (three levels) and product and customer satisfaction risks as the output of the model (Level 1) were considered.

This paper fulfills an identified need to study how the risk factors as drivers of supply chain agility can have effect on agility.

The purpose of this paper is to explore the factors that can facilitate agility in higher education and to analyze the interrelationship between the factors.

A structured model of factors facilitating agility in higher education was developed using total interpretive structural modeling (TISM). Cross-impact matrix multiplication (MICMAC) analysis helped in classifying the factors on the basis of their driving and dependency power.

An extensive literature review and expert opinion helped in identifying eight enablers that can promote agility in higher education. The ability to sense the environment, organizational structure, adoption of ICT, organizational learning, human resource strategies, leadership, readiness to change and collaboration with the stakeholders were the eight factors identified. The structural model revealed leadership as the most crucial enabler followed by human resource strategies and organizational structure.

The model has incorporated and prioritized all the crucial drivers of agility that can help universities and colleges design, adopt and implement policies and practices that would facilitate agility.

So far, the research on agility in higher education has looked into each factor in isolation. This research provides a comprehensive list of the factors and establishes the interplay between the factors making this study new and original.

The purpose of this paper is to identify factors affecting agile supply chain and evaluate the effect of these factors on profitability. To that end, after reviewing the theoretical foundations of this field, the authors identified the factors affecting supply chain agility and profitability and provided an appropriate conceptual model to measure and verify this relationship.

A 48-item questionnaire was prepared and distributed among 270 members of staff and managers of the company from which 240 questionnaire were completed and returned. The response rate was 88 percent. Using exploratory factor analysis (EFA), 37 indicators were selected and redundant questions were excluded. Results were analyzed using the structural equation modeling technique, and the relationships between factors were obtained and the impact of each supply chain agility factor on profitability was determined and prioritized.

Through EFA, the indicators related to each supply chain agility factor and profitability were extracted and using the literature, supply chain agility indicators were classified in four factors of speed, responsiveness, competency and flexibility and the indicators related to profitability in one factor, and confirmatory factor analysis (CFA) was conducted accordingly. The CFA results showed that all four factors are significantly associated with profitability, and flexibility has the greatest impact on profitability, and speed has minimal impact on profitability.

As this study has been done in the context of Iran, cautious should be taken to generalize the results.

Other studies have examined the effect of agility on business performance and the relationship between them but this study, by providing a comprehensive set of supply chain agility evaluation criteria and indicators and considering all its dimensions, intends to identify the factors affecting supply chain agility and evaluate and determine the effects of these factors on profitability and examine if supply chain agility affects profitability.

The purpose of this study is to develop a reliable and valid measurement of employee learning agility for use in human resources development.

This paper analyzed a total of 365 cases collected from Korean organizations. This paper conducted exploratory factor analysis, confirmatory factor analysis and Rasch analysis to validate the measurement. This paper conducted a measurement invariance (MI) test to confirm the generalizability of the measurement and used Harman’s single factor test to assess the common method variance (CMV).

This paper derived a learning agility measurement with six subfactors (Cronbach’s α = 0.893). This paper verified that it could be applied equally to supervisors and subordinates using an MI test. Factor analysis confirmed the feasibility of CMV. Based on I-ADAPT theory, the learning agility measurement can be applied to assess not only leader competency but also general employee competency.

Practitioners may use this model of learning agility for developing competency-based training programs and evaluations. This study is meaningful because it extends the concept of learning agility. In particular, the MI test indicated that there are no differences between supervisors and employees regarding the utility of the concept.

This study is meaningful because it extends the concept of learning agility. In particular, the MI test indicated that there are no differences between supervisors and employees regarding the utility of the concept. The concept in this paper is distinguished from other studies by applying the Rasch model from an item response theory perspective.

This paper aims to present a dynamic model based on casual relationships among the most important effective factors on business–IT alignment in Agile businesses by using system dynamics modeling approach.

To study the most important factors on agility and alignment, the data were collected by questionnaires filled by 201 experts and were analyzed by SPSS and PLS. Casual relationships among studied factors and efficiency coefficients of each factor were identified by fuzzy DEMATEL technique and analyzed by MATLAB and EXCELL. Finally, the dynamic model was plotted by VENSIM.

According to the results, only “learning IT capabilities” are the most important casual factor that has the highest influence on the other factors. “Business responding capabilities” take the highest effect from the system, and “business sensing capabilities” are in the next rank.

This study underpins effective IT deployment toward developing efficient IT capabilities to gain greater agility.

The dynamic capabilities view (DCV) has emerged as an influential theoretical and management framework in modern IS and agility researches. In this regard, we propose a conceptualization of dynamic capabilities in the form of an alignment model. Based on the dynamic capabilities, and on the alignment perspectives found in Henderson and Venkatraman’s seminal model, IT alignment is modeled as a process of reconfiguration of the firm’s IT and organizational resources, competencies and capabilities.

This paper takes a holistic and process-based view on agility from a Middle Management (MM) perspective. Its purpose is to identify subjective factors of agility emerging from people's interpretations and perceptions and to integrate them into the process of agile strategy-making.

To provide a theoretical foundation, literature was reviewed in the area of agile diversity and strategy-making. A qualitative study based on interviews was conducted to uncover the hidden subjective factors from the personal experiences and perceptions of the participants.

The study has revealed that individual opinions in terms of agility can originate from three sources: individual understandings, contextual drivers and personal beliefs.

The research contributes to dynamic capability theory by providing a better understanding of agile diversity at the MM level. The findings can help mid-level executives to cope with the complexities and ambiguities in managing agility by aligning the different understandings and people's perceptions. This is crucial, as missing alignment of team members can lead to poor dynamic capabilities of business entities and thus threatens overall organisational agility.

A model was developed to align the subjective factors of agility during the process of agile strategy-making at the MM level. The framework allows a flexible adaptation to the individual demands of organisational units, as well as concentrated measures for effective agile management. It contributes to organisational agility and business success by scaling the dynamic capabilities of MM.