Pub Date : 2022-10-27DOI: 10.1108/jamr-01-2022-0010
G. Prakash, Kumar Ambedkar
PurposeThis paper explores the relationships between Industry 4.0-driven technologies and the circular economy-driven business model (CEDBM) components of value creation, delivery and capture along manufacturing processes.Design/methodology/approachBased on the literature, a research model is developed in which the three CEBDM components are represented by five components: product service system (PSS), product design, industrial symbiosis (IS), consumer interaction and pay-per-use/rental. For each of these five components, enabling Industry 4.0 technologies are identified and vague interdependence relationships were assessed using a fuzzy decision-making trial and evaluation laboratory (DEMATEL) method.FindingsThis paper contributes to the literature by exploring the relationships of the CEDBM components of value creation, value delivery and value capture with Industry 4.0-driven technological enablers. In addition, causal relationships between Industry 4.0 technologies and their relevance for facilitating CE-enabled manufacturing processes are identified, and finally, Industry 4.0-driven technological enablers of CE are categorized as base and front-end technologies.Research limitations/implicationsThe findings suggest that value delivery-based differentiation provides new avenues for value creation and innovative forms of value capture in CEDBMs.Practical implicationsPractitioners can use the findings to develop a roadmap for Industry 4.0-driven technological solutions for CE.Social implicationsCE-driven processes of manufacturing provide not only opportunities for value capture, creation and delivery but also avenues for customer-centric product and service development and effective resource utilization.Originality/valueThis paper is the first to identify value creation, delivery and capture processes along with Industry 4.0-enabled manufacturing processes.
{"title":"Digitalization of manufacturing for implanting value, configuring circularity and achieving sustainability","authors":"G. Prakash, Kumar Ambedkar","doi":"10.1108/jamr-01-2022-0010","DOIUrl":"https://doi.org/10.1108/jamr-01-2022-0010","url":null,"abstract":"PurposeThis paper explores the relationships between Industry 4.0-driven technologies and the circular economy-driven business model (CEDBM) components of value creation, delivery and capture along manufacturing processes.Design/methodology/approachBased on the literature, a research model is developed in which the three CEBDM components are represented by five components: product service system (PSS), product design, industrial symbiosis (IS), consumer interaction and pay-per-use/rental. For each of these five components, enabling Industry 4.0 technologies are identified and vague interdependence relationships were assessed using a fuzzy decision-making trial and evaluation laboratory (DEMATEL) method.FindingsThis paper contributes to the literature by exploring the relationships of the CEDBM components of value creation, value delivery and value capture with Industry 4.0-driven technological enablers. In addition, causal relationships between Industry 4.0 technologies and their relevance for facilitating CE-enabled manufacturing processes are identified, and finally, Industry 4.0-driven technological enablers of CE are categorized as base and front-end technologies.Research limitations/implicationsThe findings suggest that value delivery-based differentiation provides new avenues for value creation and innovative forms of value capture in CEDBMs.Practical implicationsPractitioners can use the findings to develop a roadmap for Industry 4.0-driven technological solutions for CE.Social implicationsCE-driven processes of manufacturing provide not only opportunities for value capture, creation and delivery but also avenues for customer-centric product and service development and effective resource utilization.Originality/valueThis paper is the first to identify value creation, delivery and capture processes along with Industry 4.0-enabled manufacturing processes.","PeriodicalId":46158,"journal":{"name":"Journal of Advances in Management Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46377940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-13DOI: 10.1108/jamr-04-2022-0074
Maman Alimansyah, Yoshi Takahashi
PurposeThis study examines how perceived organizational justice mediates the relationship between talent management (TM) and non-high potential employees (NHPE) outcomes (i.e. affective commitment, job satisfaction, and the intention to leave) in the public sector, thereby clarifying the underlying mechanisms.Design/methodology/approachThe authors conducted a causal mediation analysis of the findings of a scenario-based survey with 748 public-sector NHPEs by adopting a post-test experimental design.FindingsPerceived distributive justice and perceived procedural justice mediated the relationships among equal resource distribution/TM procedures and NHPE outcomes, respectively.Originality/valueThis study extends and clarifies the argument for fairness judgments based on the gap in resource allocation and the presence or absence of the six rules of procedural justice that affect the attitudes and behaviors of NHPEs, who are generally more affected by TM but underexplored, in the public sector in which NHPEs are considered to be more sensitive to TM due to the egalitarian culture of public sector.
{"title":"How does perceived organizational justice mediate talent management of non-high potential employees and their outcomes?","authors":"Maman Alimansyah, Yoshi Takahashi","doi":"10.1108/jamr-04-2022-0074","DOIUrl":"https://doi.org/10.1108/jamr-04-2022-0074","url":null,"abstract":"PurposeThis study examines how perceived organizational justice mediates the relationship between talent management (TM) and non-high potential employees (NHPE) outcomes (i.e. affective commitment, job satisfaction, and the intention to leave) in the public sector, thereby clarifying the underlying mechanisms.Design/methodology/approachThe authors conducted a causal mediation analysis of the findings of a scenario-based survey with 748 public-sector NHPEs by adopting a post-test experimental design.FindingsPerceived distributive justice and perceived procedural justice mediated the relationships among equal resource distribution/TM procedures and NHPE outcomes, respectively.Originality/valueThis study extends and clarifies the argument for fairness judgments based on the gap in resource allocation and the presence or absence of the six rules of procedural justice that affect the attitudes and behaviors of NHPEs, who are generally more affected by TM but underexplored, in the public sector in which NHPEs are considered to be more sensitive to TM due to the egalitarian culture of public sector.","PeriodicalId":46158,"journal":{"name":"Journal of Advances in Management Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44426977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-06DOI: 10.1108/jamr-03-2022-0054
Ami A. Kumar, Anupriya Kaur
PurposeThe current study aims to predict consumer complaint status (complainers or non-complainers) based on socio-demographic and psychographic factors and further to discern the differences in behavior disposition of consumer groups concerning determinants of consumer's tendency to exit (TE).Design/methodology/approachThe research used survey-based data of 600 Indian consumers of three service sectors (hotel and hospitality, automobile service centers and organized retail stores). Chi-square automatic interaction detector (CHAID) decision tree analysis was used to profile consumers.FindingsThe results indicated that occupation; income; education; industry and attitude toward complaining were significant factors in profiling consumers as complainers or non-complainers. Further, determinants of TE (discouraging subjective norms, perceived likelihood of successful complaint, lower perceived switching cost, poor employee response, negative past experience and ease of complaint process) vary significantly across the groups of complainers and non-complainers.Research limitations/implicationsThe research questions in this study were tested with three service sectors consumers in India, so due care should be exercised in generalizing these findings to other sectors and countries. Study replication across other service sectors and countries is recommended to improve the generalizability of these findings with wider socio-demographic samples.Practical implicationsFirms striving for consumer retention and aim to extend their consumer life cycle can greatly benefit from the results of this study to understand the customer complaint behavior (CCB) specific to non-complaining (exit) behavior. The future researcher may benefit from replicating and extending the model in different industries for further contribution to the CCB literature.Originality/valueTo the best of the author's knowledge, there is no evidence of consumer segmentation based on their complaining behavior or socio-demographic and psychographic factors by employing CHAID decision tree analysis. In addition to illustrating the use of data mining techniques such as CHAID in the field of CCB, it also contributes to the extant literature by researching in a non-Western setting like India.
{"title":"Predicting complaint voicing or exit amidst Indian consumers: a CHAID analysis","authors":"Ami A. Kumar, Anupriya Kaur","doi":"10.1108/jamr-03-2022-0054","DOIUrl":"https://doi.org/10.1108/jamr-03-2022-0054","url":null,"abstract":"PurposeThe current study aims to predict consumer complaint status (complainers or non-complainers) based on socio-demographic and psychographic factors and further to discern the differences in behavior disposition of consumer groups concerning determinants of consumer's tendency to exit (TE).Design/methodology/approachThe research used survey-based data of 600 Indian consumers of three service sectors (hotel and hospitality, automobile service centers and organized retail stores). Chi-square automatic interaction detector (CHAID) decision tree analysis was used to profile consumers.FindingsThe results indicated that occupation; income; education; industry and attitude toward complaining were significant factors in profiling consumers as complainers or non-complainers. Further, determinants of TE (discouraging subjective norms, perceived likelihood of successful complaint, lower perceived switching cost, poor employee response, negative past experience and ease of complaint process) vary significantly across the groups of complainers and non-complainers.Research limitations/implicationsThe research questions in this study were tested with three service sectors consumers in India, so due care should be exercised in generalizing these findings to other sectors and countries. Study replication across other service sectors and countries is recommended to improve the generalizability of these findings with wider socio-demographic samples.Practical implicationsFirms striving for consumer retention and aim to extend their consumer life cycle can greatly benefit from the results of this study to understand the customer complaint behavior (CCB) specific to non-complaining (exit) behavior. The future researcher may benefit from replicating and extending the model in different industries for further contribution to the CCB literature.Originality/valueTo the best of the author's knowledge, there is no evidence of consumer segmentation based on their complaining behavior or socio-demographic and psychographic factors by employing CHAID decision tree analysis. In addition to illustrating the use of data mining techniques such as CHAID in the field of CCB, it also contributes to the extant literature by researching in a non-Western setting like India.","PeriodicalId":46158,"journal":{"name":"Journal of Advances in Management Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49249197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-23DOI: 10.1108/jamr-07-2021-0242
K. Pandey, D. Shukla
PurposeThe K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.Design/methodology/approachThis study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.FindingsThe performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.Originality/valueThe KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized emplo
{"title":"NDPD: an improved initial centroid method of partitional clustering for big data mining","authors":"K. Pandey, D. Shukla","doi":"10.1108/jamr-07-2021-0242","DOIUrl":"https://doi.org/10.1108/jamr-07-2021-0242","url":null,"abstract":"PurposeThe K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.Design/methodology/approachThis study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.FindingsThe performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.Originality/valueThe KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized emplo","PeriodicalId":46158,"journal":{"name":"Journal of Advances in Management Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44799712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-23DOI: 10.1108/jamr-04-2022-0079
Junghun Han
PurposePrevious studies on employee turnover did not explore the contextual differences of emerging markets such as Vietnam. As Vietnam is a fast-growing new tiger economy with a high inflow of foreign direct investment, contextual analysis needs to be conducted to handle rising HR issues in the region. The current study aims to analyze paths to employee turnover intention through an integrated model covering factors on individual, team, and organizational levels to understand the contextual difference in the Vietnam F&B service industry.Design/methodology/approachA mixed method was used based on quantitative and qualitative data from three organizations. For the quantitative analysis, a path model was developed and analyzed by SEM-PLS (Smart PLS) based on a sample size of 354. For the qualitative analysis, 32 semi-structured interviews were conducted to explore the contextual understanding in the regional context.FindingsAlthough the current study confirms that the paths among the three levels show the turnover factors developed in the previous study still applicable to the Vietnam context, the strengths and relationships among the team and individual levels imply that the Vietnamese context created a unique HRM environment forming different paths to reach employee turnover decisions.Originality/valueThe findings contributed to the literature on employee turnover by developing an integrated model of employee turnover encompassing the three levels, suggesting the different local contexts formed unique paths to employee turnover decisions.
{"title":"A contextual study of employee turnover intention in Vietnam F&B service sector: an integrative perspective","authors":"Junghun Han","doi":"10.1108/jamr-04-2022-0079","DOIUrl":"https://doi.org/10.1108/jamr-04-2022-0079","url":null,"abstract":"PurposePrevious studies on employee turnover did not explore the contextual differences of emerging markets such as Vietnam. As Vietnam is a fast-growing new tiger economy with a high inflow of foreign direct investment, contextual analysis needs to be conducted to handle rising HR issues in the region. The current study aims to analyze paths to employee turnover intention through an integrated model covering factors on individual, team, and organizational levels to understand the contextual difference in the Vietnam F&B service industry.Design/methodology/approachA mixed method was used based on quantitative and qualitative data from three organizations. For the quantitative analysis, a path model was developed and analyzed by SEM-PLS (Smart PLS) based on a sample size of 354. For the qualitative analysis, 32 semi-structured interviews were conducted to explore the contextual understanding in the regional context.FindingsAlthough the current study confirms that the paths among the three levels show the turnover factors developed in the previous study still applicable to the Vietnam context, the strengths and relationships among the team and individual levels imply that the Vietnamese context created a unique HRM environment forming different paths to reach employee turnover decisions.Originality/valueThe findings contributed to the literature on employee turnover by developing an integrated model of employee turnover encompassing the three levels, suggesting the different local contexts formed unique paths to employee turnover decisions.","PeriodicalId":46158,"journal":{"name":"Journal of Advances in Management Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42644292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-19DOI: 10.1108/jamr-09-2021-0298
D. Cavazos, Nathan Heller
PurposeThe current study seeks to contribute to current self-regulation research by first exploring the association between the cost of self-regulation and firm self-regulation. The mediating role of association membership and firm slack is additionally explored.Design/methodology/approachLongitudinal analysis of firm-initiated product recalls for 15 manufacturers in the USA automobile industry from 1966 to 2012 has several important findings regarding the motivations for firm self-regulation.FindingsThe influence of industry associations and firm absorbed slack both contribute to firm self-regulation.Originality/valueThe current study begins to address the importance of firm characteristics in predicting self-regulation activities. The bulk of existing research has examined self-regulation at the industry level as an activity performed as a result of the adoption of formalized industry sanctioned standards of practice. This research contributes to such work by examining firm proactivity in the absence of such formal standards.
{"title":"Examining firm self-regulation in the automobile industry: the role of situational factors, firm characteristics and association influence","authors":"D. Cavazos, Nathan Heller","doi":"10.1108/jamr-09-2021-0298","DOIUrl":"https://doi.org/10.1108/jamr-09-2021-0298","url":null,"abstract":"PurposeThe current study seeks to contribute to current self-regulation research by first exploring the association between the cost of self-regulation and firm self-regulation. The mediating role of association membership and firm slack is additionally explored.Design/methodology/approachLongitudinal analysis of firm-initiated product recalls for 15 manufacturers in the USA automobile industry from 1966 to 2012 has several important findings regarding the motivations for firm self-regulation.FindingsThe influence of industry associations and firm absorbed slack both contribute to firm self-regulation.Originality/valueThe current study begins to address the importance of firm characteristics in predicting self-regulation activities. The bulk of existing research has examined self-regulation at the industry level as an activity performed as a result of the adoption of formalized industry sanctioned standards of practice. This research contributes to such work by examining firm proactivity in the absence of such formal standards.","PeriodicalId":46158,"journal":{"name":"Journal of Advances in Management Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45452384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1108/jamr-01-2022-0022
Poulami Saha, K. B. Kiran
PurposeThe unified payment interface (UPI) is in its early stages of adoption for baby boomers. This study explores the factors affecting the behavioral intention of baby boomers to adopt UPI. UTAUT was adopted as theoretical lens of the study and extended with ubiquity, privacy risk and perceived security. The impact of an external factor – effect of COVID-19 was also examined in this study.Design/methodology/approachA consumer intercept survey was used to collect data from baby boomers via a self-administered structured questionnaire. Structural equation modeling was used to establish the relationships among latent variables. Further, using bootstrap re-sampling technique, the role of perceived security as a mediator between risk, ubiquity and behavioral intention was examined.FindingsThe study confirmed that COVID-19 was the most influential external factor for baby boomers to adopt UPI, followed by performance expectancy, social influence, ubiquity, effort expectancy and perceived security. Apropos of UPI adoption by baby boomers, privacy risk negatively influenced perceived security, whereas perceived security fully mediated the relationship between risk, ubiquity and behavioral intention.Research limitations/implicationsThe study focused only on baby boomers and their intention to adopt UPI. Hence the results cannot be generalized to all age groups and are specific to the cohort.Originality/valueThe present study aims to establish research findings on predicting antecedents of adopting a newly introduced payment mechanism and an exemplary Indian digital innovation, UPI, by baby boomers. This study is first to empirically explore intention of baby boomers toward adoption of UPI.
{"title":"What insisted baby boomers adopt unified payment interface as a payment mechanism?: an exploration of drivers of behavioral intention","authors":"Poulami Saha, K. B. Kiran","doi":"10.1108/jamr-01-2022-0022","DOIUrl":"https://doi.org/10.1108/jamr-01-2022-0022","url":null,"abstract":"PurposeThe unified payment interface (UPI) is in its early stages of adoption for baby boomers. This study explores the factors affecting the behavioral intention of baby boomers to adopt UPI. UTAUT was adopted as theoretical lens of the study and extended with ubiquity, privacy risk and perceived security. The impact of an external factor – effect of COVID-19 was also examined in this study.Design/methodology/approachA consumer intercept survey was used to collect data from baby boomers via a self-administered structured questionnaire. Structural equation modeling was used to establish the relationships among latent variables. Further, using bootstrap re-sampling technique, the role of perceived security as a mediator between risk, ubiquity and behavioral intention was examined.FindingsThe study confirmed that COVID-19 was the most influential external factor for baby boomers to adopt UPI, followed by performance expectancy, social influence, ubiquity, effort expectancy and perceived security. Apropos of UPI adoption by baby boomers, privacy risk negatively influenced perceived security, whereas perceived security fully mediated the relationship between risk, ubiquity and behavioral intention.Research limitations/implicationsThe study focused only on baby boomers and their intention to adopt UPI. Hence the results cannot be generalized to all age groups and are specific to the cohort.Originality/valueThe present study aims to establish research findings on predicting antecedents of adopting a newly introduced payment mechanism and an exemplary Indian digital innovation, UPI, by baby boomers. This study is first to empirically explore intention of baby boomers toward adoption of UPI.","PeriodicalId":46158,"journal":{"name":"Journal of Advances in Management Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49170909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1108/jamr-12-2021-0370
Surbhi Gupta, Surendra S. Yadav, P. Jain
PurposeThe purpose of the study is to examine the moderating impact of absorptive capacity on the foreign direct investment (FDI)–growth link using the data for the period 1995–2019.Design/methodology/approachThe authors apply the autoregressive distributed lag (ARDL) model and threshold analysis for empirical analysis.FindingsThe findings indicate that the link between FDI and economic growth is influenced indirectly by absorptive capacities, such as financial development, institutional quality, technological capability, and trade openness. However, while examining the linear FDI–growth nexus, the authors noticed that human capital and infrastructure did not affect the relationship; when the non-linearity in the link is considered, the authors noted that all absorptive capacities (including human capital and infrastructure), when interacted with FDI, have a positive effect on growth. Furthermore, FDI stimulates growth if the absorptive capacities have exceeded a certain threshold level.Research limitations/implicationsFrom a practical standpoint, it is reasonable to conclude that improving absorptive capacities is critical in order to perceive FDI as a growth driver.Originality/valueIndia has been able to position itself as a preferred destination for FDI (when the major economies are facing a sharp decline in FDI inflows) despite the Covid-19 pandemic. However, it still suffers from low growth. Although much of the literature admits that absorptive capacity is crucial for FDI to promote growth, no study in the case of India examines FDI–growth nexus conditioned upon absorptive capacity. Moreover, the authors have used threshold analysis for assessing the non-linearities in FDI–growth nexus contingent on absorptive capacity.
{"title":"Absorptive capacities, FDI and economic growth in a developing economy: a study in the Indian context","authors":"Surbhi Gupta, Surendra S. Yadav, P. Jain","doi":"10.1108/jamr-12-2021-0370","DOIUrl":"https://doi.org/10.1108/jamr-12-2021-0370","url":null,"abstract":"PurposeThe purpose of the study is to examine the moderating impact of absorptive capacity on the foreign direct investment (FDI)–growth link using the data for the period 1995–2019.Design/methodology/approachThe authors apply the autoregressive distributed lag (ARDL) model and threshold analysis for empirical analysis.FindingsThe findings indicate that the link between FDI and economic growth is influenced indirectly by absorptive capacities, such as financial development, institutional quality, technological capability, and trade openness. However, while examining the linear FDI–growth nexus, the authors noticed that human capital and infrastructure did not affect the relationship; when the non-linearity in the link is considered, the authors noted that all absorptive capacities (including human capital and infrastructure), when interacted with FDI, have a positive effect on growth. Furthermore, FDI stimulates growth if the absorptive capacities have exceeded a certain threshold level.Research limitations/implicationsFrom a practical standpoint, it is reasonable to conclude that improving absorptive capacities is critical in order to perceive FDI as a growth driver.Originality/valueIndia has been able to position itself as a preferred destination for FDI (when the major economies are facing a sharp decline in FDI inflows) despite the Covid-19 pandemic. However, it still suffers from low growth. Although much of the literature admits that absorptive capacity is crucial for FDI to promote growth, no study in the case of India examines FDI–growth nexus conditioned upon absorptive capacity. Moreover, the authors have used threshold analysis for assessing the non-linearities in FDI–growth nexus contingent on absorptive capacity.","PeriodicalId":46158,"journal":{"name":"Journal of Advances in Management Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43141182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-28DOI: 10.1108/jamr-02-2022-0033
Ana María García‐Pérez, Vanessa Yanes-Estévez
PurposeThis work develops a longitudinal analysis of perceived environmental uncertainty applying the Rasch methodology (1960). The environmental uncertainty is defined as an individual's perceived inability to predict the environment accurately (Milliken, 1987). The study focuses on analysing the state uncertainty from the perspective of the information and under the cognitive approach to the business reality.Design/methodology/approachRasch measurement theory (1960) is applied, specifically the differential item functioning analysis based on the responses to a survey of SMEs.FindingsThe main sources of uncertainty for all the SMEs in the sample are two sectors in their general environment: economic and political-legal ones. These segments are the only ones in the environment that generate uncertainty that in 2016 is significantly different from that in 2019, being lower in the latter year.Originality/valueThis is a pioneering analysis of uncertainty both for its longitudinal nature and the methodology applied.
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Pub Date : 2022-06-22DOI: 10.1108/jamr-02-2022-0036
A. Loomba
PurposeThe main purpose of this paper is to identify and rank various barriers to pharmacovigilance (PV) in context of emerging economies and examine their interrelationships using the interpretive structural modeling (ISM) approach. The result is a model that offers insights about how to achieve rational and safe use of medicines and ensure patient safety as realized through robust national PV systems.Design/methodology/approachThe paper develops a model to analyze the interactions among PV barriers using the ISM approach. Based on input from clinical and medical product development experts, PV barriers in emerging economies were identified and reviewed. The hierarchical interrelationships among these PV barriers were analyzed in context of their driving/dependence powers.FindingsFindings of the study identify key PV barriers—lack of resources/infrastructure, weak legislation, unfair burden of disease, lack of PV capacity, training, and enforcement authority—that drive, or strongly influence, other barriers and thwart implementation of robust national PV systems in emerging economies. Pharmaceutical industry factors were PV barriers that were identified as autonomous, implying their relative disconnection from other barriers, and patient PV practices barrier was strongly dependent on other barriers.Research limitations/implicationsThe paper offers policy- and decision-makers alike with a framework to support further research into interdependencies among key PV barriers in emerging economies. It can serve as an impetus for further research with potential to broadening the understanding of how and why PV systems may be rendered ineffective. Future studies can be planned to apply the ISM approach to study PV barriers in the context of developed economies and draw lessons and implications for policy- and decision-makers by contrasting results from these studies.Practical implicationsThis paper contributes to the understanding of the multifaceted nature of PV and its barriers. The proposed approach gives public health decision-makers a better comprehension of driver PV barriers that have most influence on others versus dependent PV barriers, which are most influenced by others. Also, knowledge, attitude and practices of patients and caregivers can also be critical PV barriers in emerging economies. This information can be instrumental for public health policymakers, government entities, and health/PV practitioners to identify the PV barriers that they should prioritize for improvement and how to manage trade-offs between these barriers.Social implicationsPV barriers in emerging economies, as compared to developed economies, are inherently different and need to be examined in their specific context. The hierarchical ISM model suggests that resources and regulation initiatives by governments in emerging economies lead to through informed/enabled pharmaceutical supply chain players and eventually drive PV-specific knowledge, attitude, and practice outco
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