bluuebunny/crossref_metadata_2025_split huggingface dataset:
Decoupling direct payments: potential impacts of the 2014 farm bill on farm debt
Authors: Bekkerman Anton , Belasco Eric , Watson Amy 
Published: October 2015

Purpose
– For over 20 years, decoupled agricultural support programs have played a large role in farm policy. The purpose of this paper is to investigate the effects of decoupled agricultural support payments on farm-level debt.

Design/methodology/approach
– A two-stage least squares model is used to estimate the impact of decoupled payments, farm production characteristics, demographics, regional risk factors, as well as cross-sectional and temporal fixed effects on farm debt, along with weather-related instrumental variables.

Findings
– Results indicate that a negative and statistically significant relationship exists between decoupled payments and farm debt. This study also provides evidence that research results not accounting for the endogenous relationship between acres operated and farm-level debt should be interpreted with caution.

Research limitations/implications
– The constantly changing sets of policy options provide a challenge in identifying the impact of a single policy, ceteris paribus. Therefore, one notable limitation is in extrapolating the results in this study to make implications on the elimination of the direct payments program, as part of the 2014 Farm Bill.

Practical implications
– This implies that farmers likely use annual decoupled payments to reduce their debt, potentially influencing their exposure to financial risks, capacity to withstand financial instability, and access to credit. The methodology used may establish a foundation for continued research that seeks to empirically identify and measure the complex interrelationships among agricultural public policies and farm-level financial measures.

Social implications
– Decoupled payment programs may indirectly influence debt decisions, which can influence production decisions in the long run.

Originality/value
– In order to accurately identify the impact of direct payment programs on farm debt levels, this study is the first of its kind to account for the endogenous relationship between production decisions and debt and use a large unbalanced panel of data available from the Agricultural Resource Management Survey.
Consistent IRR and NPV rankings
Authors: Robison Lindon J. , Barry Peter J. , Myers Robert J. 
Published: October 2015

Purpose
– It is well known that internal rate of return (IRR) and net present value (NPV) rankings of mutually exclusive investments are sometimes inconsistent. This inconsistency, when it occurs, requires decision makers to choose between the two ranking methods. The purpose of this paper is to deduce sufficient conditions for consistent IRR and NPV investment rankings of mutually exclusive investments.

Design/methodology/approach
– Deductive reasoning is used to obtain the sufficient conditions required for consistent rankings of mutually exclusive investments.

Findings
– There are different sufficient conditions (methods) that can be used to resolve inconsistent IRR and NPV rankings. However, the different methods do not necessarily produce the same consistent rankings. In particular, different size adjustment methods and reinvestment rate assumptions can produce different IRR and NPV consistent rankings. This paper suggests the appropriate criteria for selecting a particular method for ranking mutually exclusive investments.

Research limitations/implications
– Like all deduced models, the results apply only to the set of assumptions and preconditions adopted in the model. Furthermore, the application is to ranking mutually exclusive investments.

Practical implications
– There is probably no other issue in the capital budgeting literature that has generated more attention and debate than the consistency (or lack thereof) between IRR and NPV rankings. This paper summarizes conditions that can be followed to resolve the conflict which should have near universal interest to those working in the capital budging area. This paper offers alternative methods for obtaining consistent IRR and NPV rankings which can be used to improve investment ranking decisions. The particular method used should depend on the decision environment. Guides for choosing the appropriate ranking method are described in the paper.

Social implications
– Significant decisions, projects, and investments are evaluated using either IRR or NPV methods. This paper shows that existing evaluation methods can lead to sub-optimal investment choices and provides an improved framework that facilitates better investment choices. Lacking an understanding of the sufficient conditions for IRR and NPV consistency – means that resource allocations have been made to investments and projects that are not optimal.

Originality/value
– To the best of the authors’ knowledge, the results are this paper have not been published nor are they available elsewhere. That said, this paper builds on important earlier work which is carefully cited and credited.
Outsourcing maintenance in complex process industries
Authors: Maley Jane F. , Kowalkowski Christian , Brege Staffan , Biggemann Sergio 
Published: October 2015

Purpose– The purpose of this paper is to analyze the rationale for choice of suppliers and the influence these decisions have on the firm’s capabilities.Design/methodology/approach– The authors examine the choice of in-house operations vs buying maintenance in the Swedish mining industry through a qualitative case study approach.Findings– The findings reveal a strong tendency to outsource maintenance.Research limitations/implications– This in turn has a strong influence on the firm’s capabilities and long-term competitive advantage and sustainability.Practical implications– Based on the empirical findings, the authors comment on the strength and weaknesses of the different outsourcing and attempt to find practical solutions that assist the firm in creating competitive advantage.Originality/value– The unique contribution of this study is that it extends prior firm capabilities studies by investigating the impact of capability loss specifically in complex, intricate maintenance processes in a dynamic industry.
A clinical decisions unit improves emergency general surgery care delivery
Authors: Johnstone Chris , Harwood Rachel , Gilliam Andrew , Mitchell Andrew 
Published: October 2015

Purpose
– Early access to senior decision makers and investigations has improved outcomes for many conditions. A surgical clinical decisions unit (CDU) was created to allow rapid assessment and investigation by on-call senior surgical team members to facilitate decision making and, if appropriate, discharge within a set time frame (less than four hours). The purpose of this paper is to compare outcomes for unscheduled general surgery admissions to the hospital before and after commissioning this unit.

Design/methodology/approach
– Prospectively collected hospital episode statistics data were compared for all general surgical admissions for one year prior to (July 2010-June 2011) and two years after (July 2011-June 2013) the introduction of the CDU. Statistical analysis using the Mann Whitney U-test was performed.

Findings
– More patients were discharged within 24 hours (12 per cent vs 20 per cent, p
<
0.001) and total hospital stay decreased (4.6 days vs 3.2 days, p
<
0.001) following introduction of CDU. Admission via A
&
E (273 vs 212, p
<
0.01) was also decreased. Overall there was a 25.3 per cent reduction in emergency surgical admissions. No difference was noted in 30-day readmission rates (47 vs 49, p=0.29).

Originality/value
– The introduction of a CDU in has increased early discharge rates and facilitated safe early discharge, reducing overall hospital stay for unscheduled general surgical admissions. This has decreased fixed bed costs and improved patient flow by decreasing surgical care episodes routed through the emergency department (ED). In all, 30-day readmission rates have not been influenced by shorter hospital stay. Service redesign involving early senior decision making and patient investigation increases efficiency and patient satisfaction within unscheduled general surgical care. Not original but significant in that the model has not been widely implemented and this is a useful addition to the literature.
Inadvertent returns to theatre within 30 days (IRT30) of surgery
Authors: Sukha Anisha , Li Elizabeth , Sykes Tim , Fox Anthony , Schofield Andrew , Houghton Andrew 
Published: October 2015

Purpose
– When a patient unexpectedly has to go back to the operating theatre, there is often a perceived problem with the primary operation. An IRT30 is defined as any patient returning to the operating theatre within 30 days of the index procedure. IRT30 has been suggested to be a useful quality indicator of surgical standards and surgeon performance. The purpose of this paper is to evaluate the usefulness of this validated tool, by assessing all IRT30 over a 12-month period. Learning points for individual surgeons, surgical subspecialty units and the clinical governance leads were reviewed.

Design/methodology/approach
– Consecutive series of general and vascular surgical patients undergoing elective and emergency procedures between July 2012 and 2013. Prospective data collection of all IRT30s classified as Types 1-5 by a single-rater and in-depth discussion of Types 3-5 cases at the clinical governance meetings. The individual case learning points were recorded and the collective data monitored monthly.

Findings
– There were 134 IRT30s. In total 84 cases were discussed: Type 3 (n=80), Type 4 (n=4) and Type 5 (n=0). In total 50 cases were not discussed: Type 1 (n=27), Type 2 (n=23).

Originality/value
– It is crucial that surgeons continue to learn throughout their surgical career by reflecting on their own and their colleague’s results, complications and surgical performance. Analysing Types 3 and 4 IRT30s within the governance meetings has identified learning points related to both surgical technique and surgical decision making. By embracing these learning points, surgical technique and individual as well as group surgeon performance can be modified and opportunities for training and focused supervision created.
Social information landscapes
Authors: Ch'ng Eugene 
Published: October 2015

Purpose
– The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

Design/methodology/approach
– The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

Findings
– The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

Research limitations/implications
– The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

Practical implications
– The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

Originality/value
– The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.
Using Twitter data to predict the performance of Bollywood movies
Authors: Gaikar Dipak Damodar , Marakarkandy Bijith , Dasgupta Chandan 
Published: October 2015

Purpose– The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India.Design/methodology/approach– This paper uses sentiment analysis and prediction algorithms to analyze the performance of Indian movies based on data obtained from social media sites. The authors used Twitter4j Java API for extracting the tweets through authenticating connection with Twitter web sites and stored the extracted data in MySQL database and used the data for sentiment analysis. To perform sentiment analysis of Twitter data, the Probabilistic Latent Semantic Analysis classification model is used to find the sentiment score in the form of positive, negative and neutral. The data mining algorithm Fuzzy Inference System is used to implement sentiment analysis and predict movie performance that is classified into three categories: hit, flop and average.Findings– In this study the authors found results of movie performance at the box office, which had been based on fuzzy interface system algorithm for prediction. The fuzzy interface system contains two factors, namely, sentiment score and actor rating to get the accurate result. By calculation of opening weekend collection, the authors found that that the predicted values were approximately same as the actual values. For the movieSingham Returnsover method of prediction gave a box office collection as 84 crores and the actual collection turned out to be 88 crores.Research limitations/implications– The current study suffers from the limitation of not having enough computing resources to crawl the data. For predicting box office collection, there is no correct availability of ticket price information, total number of seats per screen and total number of shows per day on all screens. In the future work the authors can add several other inputs like budget of movie, Central Board of Film Certification rating, movie genre, target audience that will improve the accuracy and quality of the prediction.Originality/value– The authors used different factors for predicting box office movie performance which had not been used in previous literature. This work is valuable for promoting of product and services of the firms.
Safety or no safety in numbers? Governments, big data and public policy formulation
Authors: Amankwah-Amoah Joseph 
Published: October 2015

Purpose
– Although big data have emerged at the cornerstone of business and management research, past studies have failed to offer explanations and classifications of different levels of capacity and expertise possessed by different countries in utilising big data. The purpose of this paper is to examine the different capacities of governments in utilising big data.

Design/methodology/approach
– The paper is based on a comprehensive synopsis of the literature on big data and the role of governments in utilising and harnessing big data.

Findings
– The study provides an array of explanations to account for why some countries are adept at using big data to solve social problems, while others often faltered.

Research limitations/implications
– The study offers a range of explanations and suggestions, which include skills upgrading, to help countries improve their capabilities in data collection and data analysis.

Originality/value
– In this paper, data collection-data analysis matrix was developed to characterise the role of governments in data collection and analysis.
Modeling and quantifying uncertainty in the product design phase for effects of user preference changes
Authors: Afshari Hamid , Peng Qingjin 
Published: October 2015

Purpose– The purpose of this paper is to quantify external and internal uncertainties in product design process. The research addresses the measure of product future changes.Design/methodology/approach– Two methods are proposed to model and quantify uncertainty in the product life cycle. Changes of user preferences are considered as the external uncertainty. Changes stemming from dependencies between components are addressed as the internal uncertainty. Both methods use developed mechanisms to capture and treat changes of user preferences. An agent-based model is developed to simulate sociotechnical events in the product life cycle for the external uncertainty. An innovative application of Big Data Analytics (BDA) is proposed to evaluate the external and internal uncertainties in product design. The methods can identify the most affected product components under uncertainty.Findings– The results show that the proposed method could identify product changes during its life cycle, particularly using the proposed BDA method.Practical implications– It is essential for manufacturers in the competitive market to know their product changes under uncertainty. Proposed methods have potential to optimize design parameters in complex environments.Originality/value– This research bridges the gap of literature in the accurate estimation of uncertainty. The research integrates the change prediction and change transferring, applies data management methods innovatively, and utilizes the proposed methods practically.
Perishable food supply chain quality (PFSCQ)
Authors: Siddh Man Mohan , Soni Gunjan , Jain Rakesh 
Published: October 2015

Purpose
– The purpose of this paper is to provide a literature review of the perishable food supply chain quality (PFSCQ). It includes all the products/process from the farm to folk of perishable food (milk, meat, vegetable, grains and butter).

Design/methodology/approach
– The methodology of a structured literature review involves selection of a representative sample of articles followed by classification of articles on the basis of research methodology and content of PFSCQ in the paper.

Findings
– Research toward PFSCQ has risen in last five years. Fewer articles are addressing issues of developing countries then developed countries. Majority of the articles involved multiple as entity of analysis (combination of supplier, distributor, manufacturer, retailer and consumer), information as element of exchange and chain as the level of analysis. A large number of articles involved “agri-food,” “dairy” and “pork” as perishable products. Majority of articles used “case study” methodology. Statistical analysis as a tool for problem solving was used in majority of articles. Performance measurement aspect is also on growth in PFSCQ literature. Information sharing, logistic management, strategic management, demand forecasting and integration among the various stakeholders of PFSCQ are some of the critical issues.

Originality/value
– Many literature reviews that aim at critical examination of supply chain literature are reported but none of them focussed exclusively on content of PFSCQ. Another unique feature of this paper is that the sample size of articles with respect to number of papers (481 papers) as well as number of journals (141 journals) is quite large. The paper spans a longer time span of 20 years (1994-2013) as well.
Stakeholders’ perceptions and reputational antecedents
Authors: Shams S. M. Riad 
Published: October 2015

Purpose
– It is recognised that reputation is a relational construct; however the impact of stakeholders’ various relational dimensions on their perceptions to influence reputation is not widely understood. The purpose of this paper is to add to the current understanding of stakeholders’ relationships, interactions, their subsequent relational dimensions and its impact on stakeholders’ perceptions to further influence relational reputation.

Design/methodology/approach
– This paper takes a case study approach.

Findings
– The findings of this study recognise the impact of relationship marketing (RM) on the influence of stakeholders’ perceptions. It discusses how RM substantiate the pertinent authenticity (symbolises reputation), relevance and differentiation (represent brand positioning) of an organisation’s profile and/or their market offerings, in relation to the interest of the target market through the cause and consequence of stakeholder relationships and interactions to influence their perceptions. The findings acknowledge 11 RM dimensions that have relational implications to nurture stakeholders’ perceptions and subsequent relational reputation, which appear viable across industries and markets.

Originality/value
– Underlying the cause and consequence of stakeholder relationships and interactions; these 11 RM dimensions emerge as antecedents to form/reform relational reputation. Further academic and professional implications of the findings are briefly discussed.
Evolution of computer game developer organizations
Authors: Vanhala Erno , Kasurinen Jussi , Smolander Kari 
Published: October 2015

Purpose
– The purpose of this paper is to identify the peculiarity of computer game organizations and their human resources. It presents a stage model including four phases covering the growth from demo group to full business. This study extends the research on how computer game organizations are formed and what it takes them to grow to financially self-sufficient. The study also broadens the understanding of the beginning phase of an organization.

Design/methodology/approach
– The paper utilizes the grounded theory research method with 34 interview sessions among 11 computer game organizations. The interviewed persons include chief executive officers, designers and developers.

Findings
– This paper presents empirical findings on what a computer game organization go through when they evolve from demo group phase, which is not discussed in existing literature, to full business. The authors observed that the core team is formed over a game designer and one or more developers. The team fortifies as the organization moves onwards to next phases. At the same time its reliance on partners and outsourcing changes to need based.

Research limitations/implications
– As this is a qualitative study the observations are directly applicable only in the context of observed organizations. In the other context they are merely suggestions.

Practical implications
– The study presents concrete growth model that can be utilized when building a computer game organization.

Originality/value
– This paper illustrates the specialty of computer game organizations and their growth process. It also presents discussion of the beginning phase of organizations.