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In every industry there are resources. Some are moving, others more fixed; some are technical, others social. People working with the resources, for example, as buyers or sellers, or users or producers, may not make much notice of them. A product sells. A facility functions. The business relationship in which we make our money has “always” been there. However, some times this picture of order is disturbed. A user having purchased a product for decades may “suddenly” say to the producer that s/he does not appreciate the product. And a producer having received an order of a product that s/he thought was well known, may find it impossible to sell it. Such disturbances may be ignored. Or they can be used as a platform for development. In this study we investigate the latter option, theoretically and through real world data. Concerning theory we draw on the industrial network approach. We see industrial actors as part of (industrial) networks. In their activities actors use and produce resources. Moreover, the actors interact − bilaterally and multilaterally. This leads to development of resources and networks. Through “thick” descriptions of two cases we illustrate and try to understand the interactive character of resource development and how actors do business on features of resources. The cases are about a certain type of resource, a product − goat milk. The main message to industrial actors is that they should pay attention to that products can be co-created. Successful co-creation of products, moreover, may require development also of business relationships and their connections (“networking”).

The global research evidences indicate that the technology adoption in case of agribusiness has a potential to enhance the performance and bring operational efficiency. India is the world’s largest producer as well as consumer of milk but struggles with yield per cattle, overall productivity, low rate of technology acceptance and adoption, health detection of milching units, animal data recording and presence of dairy products in the global market. The purpose of this study is to focus on identifying the challenges of technology adoption in dairy farms and constructing a hierarchical model using soft systems methodology.

This study uses nominal group technique-based discussion with domain experts and personal interviews with dairy farm owners/managers for the identification of challenges, fuzzy interpretative structural modeling as well as FMICMAC to develop a hierarchical model of challenging elements and to divide the identified elements into four categories based on the dominance of driving-dependence power.

This research has developed a list of 12 challenges affecting the technology adoption in a dairy farm business unit, identified through the personal interviews with 60 dairy farms across three highest milk-producing states of India in terms of annual milk output – Haryana, Punjab and Uttar Pradesh. Lack of government support followed by lack of educational opportunities in dairy-based education were found as the most crucial and high driving challenges, whereas high cost, huge investment and low acceptance of decision-maker were found as the most dependent challenges of technology adoption.

This research is one step ahead of interpretive structural modeling that considers the fuzzy-based dominance in the model to showcase the degree of relationship along with its existence, but it lacks to statistically validate the findings using techniques like SEM.

This paper has developed a list of challenges in adoption of technology along with their inter-relationships to highlight the required focus challenge that drives or is dependent on the other challenges. The goal is to bring performance improvement and assist Indian dairy farm business stakeholders or decision-makers in formulating strategic and action plans and help policy planners to make favorable policies based on the understanding of contextual relationship between challenges.

In Indian context, dairy is an important part of agriculture sector, and milk is an essential item that facilitates income generation to small and rural households and a source item for several other businesses and activities. The results of this research suggested the policy planners and government to ensure subsidized and insured technologies, training support and facilities, educational opportunities and efforts for promotion of technology adoption among dairy farmers. The suggestions are purely on the basis of the relevance of challenges in the hierarchy and can play a significant role in improving the level of technology adoption and can ultimately uplift the social and economic well-being from micro-level of farmers to macro-stage concerning economic development of India.

To the best of the authors’ knowledge, this study is purely original and outcome of the research conducted by authors.

Murrah Dairy Company Limited (Murrah Dairy) is a strategy and management case related to entrepreneurship, with a focus on marketing, expansion, strategy and management of a family-run small and medium enterprise (SME).

The case is suitable for senior undergraduate and/or graduate MBA strategic management, entrepreneurship and marketing courses.

The case is about Murrah Dairy, Thailand's first and only buffalo dairy producer. The company combines the concepts of regular SMEs together with community enterprises to build a business that can be used to achieve community benefits as well as private gains. With 11 years of experience, Murrah Dairy remains the first and only extensive dairy buffalo farm in Thailand. The market is growing, the brand is catching on and the company keeps expanding. Beginning with Murrah Farm in 2003, now Murrah Dairy now operates Murrah Farm, Murrah House and Mini Murrah Farm. The question now is where to go from here and what will it take to grow?

The expected learning outcomes are the increases in understanding on environment assessment (such as SWOT analysis, Porter's Five Forces, success factors), marketing strategy (product portfolio analysis, market-product analysis) and SME management, as well as abilities to propose growth strategies and marketing strategies for the firm.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

This paper provides one of the first and most detailed accounts of the large modern dairy farms that are emerging in the dairy sector in India. Qualitative interviews are used to understand how these farms differ from their traditional smallholder counterparts and how well integrated they are into the value chains.

Snowball sampling was used to identify large farmers. In total, 49 in-depth interviews were conducted with large commercial modern farms in Punjab. A detailed description of the main characteristics of these modern dairy farms is provided. Data from previous studies conducted in Punjab is used to compare the new farms with traditional smallholder farms.

The modern dairy farms are much more advanced in their use of technology compared to their traditional counterparts. These large commercial modern farms are very well integrated into the value chains. They often, but not exclusively, sell milk to formal supply chains, sometimes on a contractual basis.

Most of the literature on the Indian dairy sector focuses on smallholders. However, understanding and acknowledging the emergence of modern dairy farms is very important in understanding the development of value chains not only in the dairy sector in India, but in domestic food sectors in developing countries in general. This qualitative data analysis is a necessary first step if more large-scale representative information is to be collected in the future.

The purpose of this paper is to evaluate dairy farm financial performance over time utilizing farm financial ratios from three university business analysis programs. The evaluation includes measures of profitability, solvency, and liquidity by herd size.

Financial ratios to reflect profitability (rate of return on assets), solvency (debt to asset ratio), and liquidity (current ratio) were collected from Cornell University, Michigan State University, and the University of Wisconsin for dairy farms from 2000 to 2012. The distribution of farm financial performance using these ratios was examined over time and by herd size. Variance component methods are used to examine the percent of variation due to individual firm and industry aspects. A simple credit risk score is calculated to examine relative farm risk.

Dairy farm profitability performance is similar across herd sizes in poor years but larger herds realized significantly more profitability in good years. Findings were similar with respect to liquidity. Large herds consistently carried relatively more debt. Large herds’ financial performance was more uniform than across smaller herds. Larger herds had more financial risk as measured by credit risk scoring but recovered quickly to industry averages in profitable years.

The variation of dairy farm financial performance in an era of volatile milk and feed price is assessed. The results have important implications for farm financial management and benchmarking farm financial performance. In addition to helping to evaluate the efficacy of various price and income risk management tools, these results have important implications for understanding the benefits of the new federal Margin Protection Program for Dairy that is available to all US dairy farmers.

The purpose of this paper is to analyse the determinants of technical efficiency (TE) in dairy farms located in the South of Brazil, aiming for a better understanding of the topic for academics, dairy farmers and policymakers to improve the productivity and competitiveness of dairy farms.

This study was developed using a two-stage approach. Data envelopment analysis was used to estimate the TE level and regression models to understand the factors affecting TE in dairy farms. The sample size is 253 dairy farms in the South of Brazil.

The variation in the mean TE indexes reported in the literature can be explained by the attributes of the analysed studies, including the education of the farm operator, farm size (number of cows and milk), feed and labour costs, and use of services. Additionally, the results suggest that dairy farmers in the sample could increase milk output by 50.1 per cent (level of inefficiency) on average if they improve their TE.

This study makes three important contributions: first, it formulates hypotheses from the previous literature’s propositions on the estimation of TE in dairy farms; second, it tests the hypotheses in an empirical study to understand the main factors affecting the TE in dairy farms of the selected municipalities in the South of Brazil; and third, it compares previous findings on the determinants of TE in dairy farms serving different stakeholders, such as researchers, farmers and government representatives, to improve the productivity and competitiveness of dairy farms.

This study applies asymmetric rather than conventional symmetric analysis to advance theory in occupational psychology. The study applies systematic case-based analyses to model complex relations among conditions (i.e., configurations of high and low scores for variables) in terms of set memberships of managers. The study uses Boolean algebra to identify configurations (i.e., recipes) reflecting complex conditions sufficient for the occurrence of outcomes of interest (e.g., high versus low financial job stress, job strain, and job satisfaction). The study applies complexity theory tenets to offer a nuanced perspective concerning the occurrence of contrarian cases – for example, in identifying different cases (e.g., managers) with high membership scores in a variable (e.g., core self-evaluation) who have low job satisfaction scores and when different cases with low membership scores in the same variable have high job satisfaction. In a large-scale empirical study of managers (n = 928) in four (contextual) segments of the farm industry in New Zealand, this study tests the fit and predictive validities of set membership configurations for simple and complex antecedent conditions that indicate high/low core self-evaluations, job stress, and high/low job satisfaction. The findings support the conclusion that complexity theory in combination with configural analysis offers useful insights for explaining nuances in the causes and outcomes to high stress as well as low stress among farm managers. Some findings support and some are contrary to symmetric relationship findings (i.e., highly significant correlations that support main effect hypotheses).

Purpose – This study compares the arrival of large-scale dairy farming in two New Zealand regions since 1984 with a particular focus on the competition between sheep farming and dairy farming.

Design/methodology/approach – The case study draws on qualitative interviews with 58 farmers in two regions.

Findings – We identify and compare the changing economic, social and cultural hierarchies in and between the two regions.

Originality/value – This study extends a typical political economic comparison by emphasising the changes in social and cultural capital as a result of the changing economic conditions in the country and the regions.

Changing conditions for farming force farmers to search for new ways to organize agricultural production. In dairy farming, households experience long working hours, inconvenient working conditions, and low incomes. Dairy markets are beleaguered by overproduction, low prices on staple dairy products, and low return to labor and capital. This structural squeeze, which is aggravated by quick technological changes and the globalization of markets, is negotiated in various ways by dairy farmers in different agricultural regimes. A recent coping strategy for dairy farmers in Norway has been joint farming, a process whereby two, three, or even more farmers establish a joint company to merge their resources and work together. These joint farmers enjoy more leisure time, greater security in case of illness, and improved work environments. Why is joint farming so successful in Norway? One main explanation is the difference between agricultural regimes, which places the Norwegian dairy farmer in a privileged position when it comes to building coping capacity.

In the past decade, there has been a dramatic decline in agricultural employment in Bulgaria and several reports have pointed at supply chain modernisation and poor milk quality as the main reasons for the dramatic decline in the number of farms. However, to date the policy debate is been based on ad hoc claims, while there is relatively little micro-level evidence. The purpose of this paper is to analyse the determinants of structural change in the Bulgarian dairy sector in the period 2003-2009.

This paper analyses the determinants of structural change in the Bulgarian dairy sector in the period 2003-2009, using a unique panel survey of 296 farm households in the North and South Central Region of Bulgaria. In order to control for sample attrition bias, the authors use a two-step Heckman model of farm survival and growth model.

The data confirms the rapid outflow of agricultural labour from dairy farming activities: 55 per cent of the farm households supplying milk to a dairy company in 2003 stopped supplying in 2009. The main reasons for quitting are ageing of the household, health problems and an increase in off-farm employment alternatives and not supply chain modernisation and milk quality standards. The institutional innovations which are associated with integration in modern supply chains, such as the provision of farm assistance programmes, have a positive impact on small farms’ growth.

The study is one of the first to use panel data to analyse the impact of standards on the survival and growth of small farms in value chains. The authors analyse the determinants of farm survival and growth in the Bulgarian dairy sector in the period 2003-2009, using panel surveys of 296 dairy farm households in the North and South Central Region of Bulgaria and panel data from interviews with dairy companies. The findings are relevant beyond the Bulgarian dairy sector as supply chain modernisation and changes in quality regulations are taken place in many other transition and developing countries.