Enhancing Decision-Making Efficiency Through M-BI Use
(2017)- Abstract
- Mobile business intelligence (m-BI) denotes the delivery of business information on mobile devices, such as tablets and smartphones. m-BI promises to support the mobile workforce in making decisions just in time. Yet, a lack of knowledge exists on how m-BI use can actually lead to improved decisionmaking performance at the individual level. This void of understanding has implications for both practitioners and academics, with the former feeling insecure about the benefits of investing in yet another technology, and the latter lacking empirical studies on technologies that challenge traditional decision support system (DSS) platforms. To this end, drawing inspiration from the information system (IS) success models and information search... (More)
- Mobile business intelligence (m-BI) denotes the delivery of business information on mobile devices, such as tablets and smartphones. m-BI promises to support the mobile workforce in making decisions just in time. Yet, a lack of knowledge exists on how m-BI use can actually lead to improved decisionmaking performance at the individual level. This void of understanding has implications for both practitioners and academics, with the former feeling insecure about the benefits of investing in yet another technology, and the latter lacking empirical studies on technologies that challenge traditional decision support system (DSS) platforms. To this end, drawing inspiration from the information system (IS) success models and information search literature, a research model is designed and tested through a survey-based study. Data are collected from 357 m-BI users employed in different organisations. The study findings support our research model. Reactive search and proactive search, both of which use modes of m-BI, significantly influence decision-making efficiency, albeit with different prediction-size effects. Furthermore, mobility and time criticality (i.e. mobile work characteristics) influence proactive search, and time criticality is a predictor of reactive search. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/02e2ce4a-d8f8-4307-a6c1-af3b0f89ce6b
- author
- Tona, Olgerta LU and Carlsson, Sven LU
- organization
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 25th European Conference on Information Systems (ECIS)
- publisher
- European Conference on Information Systems (ECIS) at AIS Electronic Library (AISeL)
- external identifiers
-
- scopus:85035339324
- ISBN
- 978-989-20-7655-3
- language
- English
- LU publication?
- yes
- id
- 02e2ce4a-d8f8-4307-a6c1-af3b0f89ce6b
- alternative location
- http://aisel.aisnet.org/ecis2017_rp/30/
- date added to LUP
- 2017-12-18 14:28:02
- date last changed
- 2022-04-25 04:34:59
@inproceedings{02e2ce4a-d8f8-4307-a6c1-af3b0f89ce6b,
abstract = {{Mobile business intelligence (m-BI) denotes the delivery of business information on mobile devices, such as tablets and smartphones. m-BI promises to support the mobile workforce in making decisions just in time. Yet, a lack of knowledge exists on how m-BI use can actually lead to improved decisionmaking performance at the individual level. This void of understanding has implications for both practitioners and academics, with the former feeling insecure about the benefits of investing in yet another technology, and the latter lacking empirical studies on technologies that challenge traditional decision support system (DSS) platforms. To this end, drawing inspiration from the information system (IS) success models and information search literature, a research model is designed and tested through a survey-based study. Data are collected from 357 m-BI users employed in different organisations. The study findings support our research model. Reactive search and proactive search, both of which use modes of m-BI, significantly influence decision-making efficiency, albeit with different prediction-size effects. Furthermore, mobility and time criticality (i.e. mobile work characteristics) influence proactive search, and time criticality is a predictor of reactive search.}},
author = {{Tona, Olgerta and Carlsson, Sven}},
booktitle = {{Proceedings of the 25th European Conference on Information Systems (ECIS)}},
isbn = {{978-989-20-7655-3}},
language = {{eng}},
publisher = {{European Conference on Information Systems (ECIS) at AIS Electronic Library (AISeL)}},
title = {{Enhancing Decision-Making Efficiency Through M-BI Use}},
url = {{http://aisel.aisnet.org/ecis2017_rp/30/}},
year = {{2017}},
}