Jayson Beckman and David Schimmelpfennig
The recent fluctuations in farm income remind us of the boom-bust nature of the agricultural sector. To better understand these fluctuations in farm income, the purpose of this…
Abstract
Purpose
The recent fluctuations in farm income remind us of the boom-bust nature of the agricultural sector. To better understand these fluctuations in farm income, the purpose of this paper is to examine the relationship between farm income and influential factors from 1964 to 2010 allowing for structural breaks in the data.
Design/methodology/approach
The authors estimate error-correction models for an overarching model and several sub-models at different scales based on their relationship with farm income: micro, meso, and macro. The authors then provide a series of impulse response functions (IRFs) that combine short- and long-run impacts in a rigorous framework indicating the response of farm income to shocks from any of the explanatory variables.
Findings
Results indicate that prices paid (PP) and received by farmers, technological change, interest and exchange rates (ERs), gross domestic product (GDP) and land prices all influence farm income. Results using IRFs show how increases in farm income arise from shocks to prices received and GDP; while PP, interest rates, and land prices have a negative impact on farm income. Technological progress and ERs switch from having a negative short-run impact, to a positive long-run impact.
Originality/value
This paper takes a fresh look at the single, overarching model for farm income determinants. The authors break this model into three separate levels, with results indicating that these sub-groups perform better than the one overarching model of all variables.
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Edgar Nave, Paulo Duarte, Ricardo Gouveia Rodrigues, Arminda Paço, Helena Alves and Tiago Oliveira
In recent years, the craft beer (CB) industry has gained impetus and has experienced significant growth in scientific publications. This study aims to present a systematic review…
Abstract
Purpose
In recent years, the craft beer (CB) industry has gained impetus and has experienced significant growth in scientific publications. This study aims to present a systematic review of the literature on CB in areas related to economic and business sciences.
Design/methodology/approach
Based on the data from Scopus, Web of Science and a set of articles not indexed to these databases until June 2021, a total of 132 articles were included for analysis, using bibliometric and content analysis techniques.
Findings
The study allowed us to identify that CB has four main clusters/themes of research, namely, CB industry and market, marketing and branding, consumer behavior and sustainability. Detailed information on the clusters is provided. In addition, the results showed that publications addressing CB have grown significantly from 2015 onwards and are dispersed across many journals, with none assuming a clear leadership. Quantitative approaches account for more than half of publications.
Research limitations/implications
This study is a useful guide for academics intending to develop studies with CB. It provides a framework to structure future research by identifying existing literature clusters and proposes several research propositions.
Practical implications
The findings from this study are useful for CB companies to get an overview of the main issues affecting the CB industry and market to be able to adapt their strategies and stay aligned with market tendencies in the four main clusters identified.
Originality/value
This is the first systematic review of CB. Therefore, it provides a significant contribution to frame and strengthening the literature on CB and serves as a reference for future research. Based on the content analysis and cluster identification, the findings portray the status of current research. Accordingly, a set of research opportunities are offered.
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The fast pace of innovation and disruption in business processes and technology today requires employees of organizations to be continuously up-skilled and be able to adapt to…
Abstract
Purpose
The fast pace of innovation and disruption in business processes and technology today requires employees of organizations to be continuously up-skilled and be able to adapt to changing practices. Training needs are becoming more personalized. Micro-learning and byte-sized training modules, easily accessible to employees, as and when required, are some of the major organizational needs. Training and development programs should be designed keeping in mind factors of employee engagement, involvement and extent of training transfer. The purpose of this paper is to explore whether artificial intelligence (AI) can lead training and development processes in organizations in the years to come.
Design/methodology/approach
The author has interviewed 27 HR and training professionals, in person, from across eight organizations in the FMCG, oil and natural gas and clothing and apparel industries. All these organizations have an annual turnover of greater than US$14.5m. A formal questionnaire was not followed since this research explores a new field in academia. Open-ended questions were used in the interviews, of which eight were common across all interviews. The mean interview duration was 25 min 33 s. The objective being to capture ideas and identify future trends, the analysis was done on a percentage basis and served as the foundation for a new training and development needs model for organizations.
Findings
Among the 27 HR/training professionals interviewed, 92.6 percent respondents believed that their organization/department requires knowledge management practices while 40.7 percent require the training content delivered to a fixed category of employees, to be updated continuously. Personalized learning was mentioned as a requirement by 63 percent of the respondents. In total, 92.6 percent HR/training professionals believed training programs should involve high employee engagement. In total, 51.9 percent would prefer on-the-go learning tools for their employees, while 33.33 percent respondents believed an intuitive e-learning interface would be useful for their organization/department. The findings also led to the foundation of an SIP model, which shall be useful in providing direction to AI systems in training and development practices.
Research limitations/implications
The paper opens up avenues for further research to be conducted in identifying the areas of impact of AI in training and development. It paves the way for researchers to quantify training effectiveness and measure it with the help of AI.
Practical implications
The objective of the paper is to explore the opportunities for AI in training and development practices. Having identified the opportunities, it shall drive the practice of using AI across industries.
Originality/value
The thoughts in the paper have been ideated by the authors organically. Relevant data points from referred sources have been cited to back up those thoughts.
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Among the top management issues covered in this section are: leadership to promote change; issues of corporate culture; effective international strategy; environmental leadership;…
Abstract
Among the top management issues covered in this section are: leadership to promote change; issues of corporate culture; effective international strategy; environmental leadership; investment in Eastern Europe; and developing “world‐class” manufacturing strategy.