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Article
Publication date: 3 June 2011

Professor S.P. Raj

896

Abstract

Details

Journal of Agribusiness in Developing and Emerging Economies, vol. 1 no. 1
Type: Research Article
ISSN: 2044-0839

Content available
Book part
Publication date: 20 August 1996

Abstract

Details

The Peace Dividend
Type: Book
ISBN: 978-0-44482-482-0

Content available
Book part
Publication date: 20 August 1996

Abstract

Details

The Peace Dividend
Type: Book
ISBN: 978-0-44482-482-0

Open Access
Article
Publication date: 23 March 2020

Cao Van Hon and Le Khuong Ninh

The purpose of this paper is to estimate the impact of credit rationing on the amount of capital allocated to inputs used by rice farmers in the Mekong River Delta (MRD).

1528

Abstract

Purpose

The purpose of this paper is to estimate the impact of credit rationing on the amount of capital allocated to inputs used by rice farmers in the Mekong River Delta (MRD).

Design/methodology/approach

Based on the literature review, the authors propose nine hypotheses on the determinants of access of rice farmers to credit and four hypotheses on the impact of credit rationing on the amount of capital allocated to inputs used by rice farmers in the MRD. Data were collected from 1,168 farmer households randomly selected out of 10 provinces (city) in the MRD.

Findings

Step 1 of propensity score matching (PSM) with probit regression shows that land value, income, education, gender of household head and geographical distance to the nearest credit institution affect the degree of credit rationing facing rice farmers. Step 2 of PSM estimator identifies that the amount of capital allocated to inputs such as fertilizer and hired labour increases when credit rationing decreases while that allocated to seed and pesticide is not influenced by credit rationing because rice farmers use these inputs adamantly regardless of effectiveness.

Originality/value

This paper sheds light on the impact of credit rationing on the amount of capital allocated to inputs used by rice farmers, which is largely different from the main focus of the extant literature just on the determinants of credit rationing facing farmers in general and rice farmers in particular.

Details

Journal of Economics and Development, vol. 22 no. 1
Type: Research Article
ISSN: 1859-0020

Keywords

Open Access
Article
Publication date: 29 December 2023

Dean Neu and Gregory D. Saxton

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social…

Abstract

Purpose

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

Design/methodology/approach

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

Findings

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

Research limitations/implications

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

Originality/value

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

Details

Accounting, Auditing & Accountability Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0951-3574

Keywords

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