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Moscow Rules: A Quantitative Exposé

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• Eduardo Miranda   ORCID: orcid.org/0000-0001-8195-7506 10  

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This article analyzes the performance of the MoSCoW method to deliver all features in each of its categories: Must Have, Should Have and Could Have using Monte Carlo simulation. The analysis shows that under MoSCoW rules, a team ought to be able to deliver all Must Have features for underestimations of up to 100% with very high probability. The conclusions reached are important for developers as well as for project sponsors to know how much faith to put on any commitments made.

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Toward Impact Generation and Future Research

Friedenspädagogik

• Agile planning
• Release planning
• Requirements prioritization
• Feature buffers
• MosCoW method

1 Introduction

MoSCoW rules [ 1 ], also known as feature buffers [ 2 ], is a popular method to give predictability to projects with incremental deliveries. The method does this by establishing four categories of features: M ust Have, S hould Have, C ould Have and W on’t Have, from where the MoSCoW acronym is coined. Each of the first three categories is allocated a fraction of the development budget, typically 60, 20 and 20 percent, and features assigned to them according to the preferences Footnote 1 of the product owner until the allocated budgets are exhausted by subtracting from them, the development effort estimated for each feature assigned to the category. By not starting work in a lower preference category until all the work in the more preferred ones have been completed, the method effectively creates a buffer or management reserve of 40% for the Must Have features, and of 20% for those in the Should Have category. These buffers increase the confidence that all features in those categories will be delivered by the project completion date. As all the development budget is allocated by the method, there are no white spaces in the plan, which together with incentive contracts, makes the method palatable to sponsors and management.

Knowing how much confidence to place in the delivery of features in a given category is an important concern for developers and sponsors alike. For developers it helps in formulating plans consistent with the organization’s risk appetite, making promises they can keep, and in calculating the price of incentives in contracts as well as the risk of incurring penalties, should these exist. For sponsors, it informs them the likelihood the features promised will be delivered, so they, in turn, can make realistic plans based on it. To this purpose, the article will explore:

The probabilities of delivering all the features in each of the categories: Must Have, Should Have and Could Have, under varying levels of under and overestimation of the features’ development efforts

The impact of features’ sizes, dominance, number of features, and correlation between development efforts in said probabilities

The effect of budget allocations other than the customary 60/20/20 on them.

To calculate the probabilities of delivery (PoDs) we need to make suitable assumptions about the distribution of the efforts required to develop each feature since the single point estimate used in the MoSCoW method are insufficient to characterize them.

In this article, those assumptions are derived from two scenarios: a low confidence estimates scenario used to establish worst case Footnote 2 PoDs and a typical estimates scenario used to calculate less conservative PoDs.

The potential efforts required and the corresponding PoDs, are calculated using Monte Carlo simulations [ 3 , 4 ] to stochastically add the efforts consumed by each feature to be developed.

The rest of the paper is organized as follows: Sect.  2 provides an introduction to the MoSCoW method, Sect.  3 introduces the Monte Carlo simulation technique and describes the calculations used for the interested reader, Sect.  4 discusses the two scenarios used in the calculations, Sect.  5 analyzes the main factors affecting the method’s performance, Sect.  6 discuss the method’s effectiveness in each of the scenarios and Sect.  7 summarizes the results obtained.

2 The MoSCoW Method

The MoSCoW acronym was coined by D. Clegg and R. Baker [ 5 ], who in 1994 proposed the classification of requirements into Must Have, Should Have, Could Have and Won’t Have. The classification was made on the basis of the requirements’ own value and was unconstrained, i.e. all the requirements meeting the criteria for “Must Have” could be classified as such. In 2002, the SPID method [ 6 ] used a probabilistic backcasting approach to define the scope of three software increments roughly corresponding to the Must Have, Should Have and Could Have categories, but constraining the number of Must Have to those that could be completed within budget at a level of certainty chosen by the organization. In 2006, the DSDM Consortium, now the Agile Business Consortium, published the DSDM Public Version 4.2 [ 7 ] establishing the 60/20/20% recommendation although this, was probably used before by Consortium’s members on their own practices. The current formulation of the MoSCoW prioritization rules is documented in the DSDM Agile Project Framework [ 1 ].

During the project planning phase, see Fig.  1 .a, features are allocated to one of four sets: Must Have, Should Have, Could Have, and Won’t Have on the basis of customer preferences and dependencies until the respective budgets are exhausted.

MoSCoW rules at play: a) During planning, b) in execution

During execution, Fig.  1 .b, features in the Must Have category are developed first, those in the Should Have second, and those in the Could Have, in third place. If at any time the work in any category requires more effort than planned, work on them will continue at the expense of those in the lower preference categories which will be pushed out of scope in the same amount as the extra effort required. The advantage for the project sponsor is that, whatever happens, he or she can rest assured of getting a working product with an agreed subset of the total functionality by the end of the project.

For the MoSCoW method to be accepted by the developer as well as by the sponsor of a project, the risk of partial deliveries must be shared between both of them through incentive contracts since approaches like firm fixed price or time and materials, that offloads most of the risk on only one of the parties could be either, prohibitive or unacceptable to the other. Contractually, the concept of agreed partial deliveries might adopt different forms. For example, the contract could establish a base price for the Must Have set, with increasingly higher bonuses or rewards for the Should Have and Could Have releases. Conversely the contract could propose a price for all deliverables and include penalties or discounts if the lower priority releases are not delivered. This way the incentives and disincentives will prevent the developer from charging a premium price to protect itself from not delivering all features while the sponsor, is assured the developer will do its best, in order to win the rewards.

3 The Monte Carlo Simulation

The Monte Carlo method is a random sampling technique used to calculate probability distributions for aggregated random variables from elementary distributions. The technique is best applied to problems not amenable to closed form solutions derived by algebraic methods.

The Monte Carlo method involves the generation of random samples from known or assumed elementary probability distributions, the aggregation or combination of the sample values according to the logic of the model been simulated and the recording of the calculated values for the purpose of conducting an ex-post statistical analysis.

The technique is widely used [ 3 , 4 ] in probabilistic cost, schedule and risk assessments and numerous tools Footnote 3 exist to support the computations needed.

The results presented in the paper were calculated using @Risk 7.5. As these are the product of simulation runs, they might slightly differ from one run to another, or when using a different number of iterations or platforms.

The rest of the section explains the model used to generate the cumulative probability curves and calculate the PoD for each MoSCoW category: Must Have (MH), Should Have (SH) and Could Have (CH), with the purpose of allowing interested readers replicate the studies or develop their own simulations. Those not so inclined might skip it, with little or no loss in understanding the paper. The name of the parameters should make them self-explanatory however, conceptual definitions about its meaning and usage will be provided throughout the paper.

The probability of completing all features in a given category in, or under, an \(x\) amount of effort is defined as:

The cumulative distribution functions: \(F_{MH} \left( x \right), \,F_{SH} \left( x \right)\, {\text{and}}\, F_{CH} \left( x \right)\) , are built by repeatedly sampling and aggregating the effort required by the features included in each category.

similarly, for features j and k, and:

subject to the maximum allocation of effort for each category:

The Probability of Delivery (PoD) of each category is defined as:

All quantities are normalized for presentation purposes by dividing them by the \(DevelopmentBudget\) .

4 Low and Typical Confidence Scenarios

Figure  2 contrasts the two scenarios mentioned in the introduction. The low confidence scenario is characterized by the uniform distribution of the potential efforts required to realize each feature, with the lower limit of each distribution corresponding to the team’s estimated effort for the feature and their upper to increments of 50, 100 and 200% above them, to express increasing levels of uncertainty. Since all values in the interval have equal probability, this scenario corresponds to a maximum uncertainty state [ 8 ]. This situation, however unrealistic it might seem, is useful to calculate a worst case for the PoD of each category. In the typical confidence scenario, the potential efforts are characterized by a right skewed triangular distributions, in which the team’s estimates correspond to the most likely value of the distribution, meaning the realization of many features will take about what was estimated, some will take some more and a few could take less.

Probability distributions for the effort required by each feature in the low (uniform distributions) and typical (triangular distributions) confidence scenarios

The right skewness of the typical estimate distributions is predicated on our tendency to estimate based on imagining success [ 9 ], behaviors like Parkinson’s Law Footnote 4 and the Student Syndrome Footnote 5 , which limit the potential for completing development with less effort usage than estimated, and the fact that the number of things that can go wrong is practically unlimited [ 10 , 11 ]. Although many distributions fit this pattern, e.g. PERT, lognormal, etc., the triangular one was chosen for its simplicity and because its mass is not concentrated around the most likely point [ 12 ], thus yielding a more conservative estimate than the other distributions mentioned.

As before, the right extreme of the distribution takes values corresponding to 50, 100 and 200 percent underestimation levels. For the lower limit however, the 80 percent of the most likely value was chosen for the reasons explained above.

Considering this second scenario is important, because although having a worst case for the PoDs is valuable as they tell the lowest the probabilities could be, relying on them for decision making may lead to lost opportunities because of overcautious behaviors.

5 Level of Underestimation, Correlation, Number of Features in a Category, Feature Dominance and Non-traditional Budget Allocations

Before calculating the PoDs for each MoSCoW category under the two scenarios, the impact of different factors on the PoD is explored with the purpose of developing an appreciation for how they affect the results shown, i.e. what makes the PoDs go up or down. Understanding this is important for those wanting to translate the conclusions drawn here to other contexts.

Although the analysis will be conducted only for the low confidence estimates for reasons of space, the same conclusions applies to the typical estimates scenario, with the curves slightly shifted to the left.

Figure  3 shows the impact of underestimation levels of up to 50, 100 and 200% of the features’ individual estimates on the PoD of a Must Have category comprising 15 equal sized features, whose development efforts are independent from each other.

Independent, as used here, means the efforts required by any two features will not deviate from its estimates conjointly due to a common factor such as the maturity of the technology, the capability of the individual developing it or the consistent over optimism of an estimator. When this occurs, the efforts are correlated rather than independent. Having a common factor does not automatically mean the actual efforts are correlated. For example, a feature could take longer because it includes setting up a new technology, but once this is done, it doesn’t mean other features using the same technology would take longer since the it is already deployed. On the other hand, the use of an immature open source library could affect the testing and debugging of all the features in which it is included.

The higher the number of correlated features and the stronger the correlation between them, the more individual features’ efforts would tend to vary in the same direction, either requiring less or more of it, which would translate into higher variability at the total development effort level. This is shown by curves “r = 0.2”, “r = 0.6” and “r = 0.8” in Fig.  4 , becoming flatter as the correlation (r) increases.

Correlation brings good and bad news. If things go well, the good auspices will apply to many features, increasing the probability of completing all of them on budget. Conversely, if things do not go as well as envisioned, all affected features will require more effort, and the buffers would not provide enough slack to complete all of them.

Estimating the level of correlation between estimates is not an easy task, it requires assessing the influence one or more common factors could have on the items affected by them, a task harder than producing the effort estimates themselves. So while correlation cannot be ignored at risk of under or over estimating the safety provided by the method, the cost of estimating it, would be prohibitive for most projects. Based on simulation studies, Garvey et al. [ 13 ] recommend using a coefficient of correlation of 0.2 across all the estimated elements to solve the dilemma, while Kujawski et al. [ 14 ], propose to use a coefficient of 0.6 for elements belonging to the same subsystem, as these would tend to exhibit high commonality since in general, the technology used and the people building it would be the same, and 0.3 for elements on different subsystems, because of the lower commonality.

Cumulative completion probabilities under increasing levels of underestimation. The simulation shows a PoD for the Must Have features of 100% for an underestimation level of up to 50%, of 98.9% at up to 100%, and of 1.3% for an underestimation in which each feature can require up to 200% of the estimated budget.

The PoDs are also affected by the number of features in the category as well as by the existence of dominant features, which are features whose realization requires a significative part of the budget allocated to the category. See Figs.  5 and 6 .

As in the case of correlation, a small number of features and the presence of dominant features result in an increase in the variability of the estimates. Dominant features, contribute to this increase because it is very unlikely that deviations on their effort requirements could be counterbalanced by the independent deviations of the remaining features in the category. As for the increase of variability with a diminishing number of features, the reason is that with a fewer independent features, the probability of them going all in one direction, is higher than with many features.

The model in Fig.  7 challenges the premise of allocating 60% of the development budget to the Must Have category and explores alternative assignments of 50, 70 and 80% of the total budget. Reducing the budget allocation from 60 to 50% increases the protection the method affords at the expense of reducing the number of features a team can commit to. Increasing the budget allocation for the Must Have allows developers to promise more, but as will be shown, this is done at the expense of reducing the certainty of delivering it. For the 50% allocation level, there is a 100% chance of delivering the Must Have for underestimations of up to 100%, and of 68.2% for underestimations of up to 200%. At the 70% allocation level, the simulation shows that the PoD for the Must Have, when the possibility of underestimation is up to 50% still is 100%, but that it drops sharply to 34% when the underestimation level rises to up to 100%. For the 80% allocation level, the PoD for the Must Have falls to 49.7% for the up to 50% underestimation level and to 0 for the other two. The rest of the paper will then use the customary 60, 20 & 20% allocation scheme.

Probability of completing all features in the Must Have category under a given percent of the budget when the underestimation level is up to 100% and the efforts are correlated (r > 0)

Influence of the number of features on the PoD for a Must Have set containing the number of equally sized independent features indicated by the legend on the chart, with an underestimation level of up to 100%. The PoD offered by the method drops sharply when the set contains less than 5 features

Influence of a dominant feature on the PoD. Each set, with the exception of the dominant at 100%, contained 15 features, with the dominant feature assigned the bulk of the effort as per the legend in the chart with the remaining budget equally distributed among the other 14 features. The safety offered by the method drops sharply when a feature takes more than 25% of the budgeted effort for the category. Underestimation of up to 100% and independent efforts

Probability of delivering all Must Have features for Must Have budget allocations of 50, 60, 70 and 80% under different underestimation conditions. The respective number of Must Have features for each budget allocation were 12, 15, 17, and 20.

6 Probabilities of Delivery for Each MoSCoW Category

This section discusses the PoDs for each MoSCoW category: Must Have, Should Have and Could Have under the following conditions:

Low confidence estimation, independent efforts

Low confidence estimation, correlated efforts

Typical estimation, independent efforts

Typical estimation, correlated efforts

In all cases, the underestimations considered are of up to 50, 100 and 200% of the estimated effort, a 60/20/20 effort allocation scheme and a Must Have category comprising 15 equal sized features with Should and Could Have categories comprising 5 equal sized features each. These assumptions are consistent with the precedent analysis and with the small criteria in the INVEST [ 15 ] list of desirable properties for user stories. For the correlated efforts cases, the article follows Kujaswki’s recommendation, of using an r = 0.6, as many of the attributes of an agile development project: dedicated small teams, exploratory work and refactoring, tend to affect all features equally.

6.1 Low Confidence, Independent Efforts

Figure  8 shows the PoDs for all MoSCoW categories for the low confidence, uncorrelated features, r = 0, model. At up to 50% underestimation, the probability of delivering all Must Have is 100%, as expected, and the probability of delivering all Should Have is 50.2%. At up to 100% underestimation, the probability of delivering all the Must Have still high, 98.9% but the probability of completing all the Should Have drops to 0. At up to 200% the probability of delivering all the Must Haves is pretty low, at 1.3%. In no case it was possible to complete the Could Have within budget.

6.2 Low Confidence, Correlated Efforts

As shown by Fig.  9 , in this case the variability of the aggregated efforts increases, with the outermost points of the distribution becoming more extreme as all the efforts tend to move in unison in one or another direction. Comparing the PoDs for this case with those of the previous one, it seems paradoxical, that while the PoD for the Must Have at 100% underestimation level goes down from 98.9 to 74.0, the PoD for the same category at 200% underestimation level goes up from 1.3 to 26.9%! This is what was meant when it was said that correlation brought good and bad news.

Probability of delivering all features in a category in the case of low confidence estimates under different levels of underestimation when the efforts required by each feature are independent (r = 0)

To understand what is happening, it suffices to look at Fig.  10 . Figure  10 .a shows histograms of the Must Have aggregated independent efforts for uncertainty levels of 50, 100 and 200%. Because of the relatively lower upper limit and the tightness of the distribution spread afforded by the sum of independent efforts, the 100% uncertainty distribution fits almost entirely to the left of the total budget, scoring this way a high PoD. A similar argument could be made for the 200% uncertainty level, except that this time, the distribution is almost entirely to the right of the total budget, thus yielding a very low PoD. As could be seen in Fig.  10 .b, when the efforts are correlated, the distributions spread more widely, making part of the 100% distribution fall to the right of the total budget line, reducing its PoD, and conversely, part of the 200% distribution might fall to the left of the line, thus increasing its PoD, which is what happened with this particular choice of parameter values.

Probability of delivering all features in a category in the case of low confidence estimates under different levels of underestimation when the efforts required by each feature are highly correlated (r = 0.6)

Histograms for Must Have features’ effort (a) left – independent efforts, (b) right – correlated efforts

6.3 Typical Estimates

Figures  11 and 12 show the typical estimates’ PoDs for uncorrelated and correlated efforts respectively. As expected, all the PoDs in this scenario are higher than in the case of the low confidence estimates. In the case of independent efforts, at up to 50% underestimation, the PoDs for the Must Have and the Should Have are 100%. At up to 100% underestimation, the PoD for the Must Have is 100% with the PoD for Should Have dropping to 39.7%. At up to 200% the probability of delivering all the Must Haves still high, at 70.5%, but there is no chance of delivering the Should Have. In no case, any Could Have were completed. For the correlated efforts case, the respective probabilities at 50% underestimation are: 100% for the Must Have, 88.7% for the Should Have and 20.6% for the Could Have. At 100% underestimation: 96.4, 50.3 and 8.6% respectively and at 200% underestimation: 59.8, 20.5 and 3%.

Probability of delivering all features in a category in the case of typical estimates under different levels of underestimation when the efforts required by each feature are independent (r = 0)

Probability of delivering all features in a category in the case of typical estimates under different levels of underestimation when the efforts required by each feature are highly correlated (r = 0.6).

This article sought to quantitatively answer the following questions:

What are the probabilities of delivering all the features in each of the categories: Must Have, Should Have and Could Have, under varying levels of under and overestimation of the features’ development efforts?

What is the influence of features’ sizes, feature dominance, number of features, and correlation between development efforts in said probabilities?

What is the effect of budget allocations other than the customary 60/20/20 on them?

To answer question 1, it is necessary to look at Table 1 which summarizes the results for the low confidence and typical estimates scenarios, for the three levels of underestimation studied: 50, 100 and 200%.

Not surprisingly, the results indicate that the method consistently yields a high PoD for the Must Have features. What is noteworthy, is its resilience in face of up to 100% underestimation of individual features in the category. For the Should Have, the results are robust for up to 50% of underestimation and with regards to the Could Have, they should only be expected if destiny is smiling upon the project.

Question 2 is important for practitioners preparing release plans. For the method to offer these levels of certainty, the number of features included in each category should be at least 5 with none of them requiring more than 25% of the effort allocated to the category. If these conditions are not met, the safety offered by the method drops sharply. Correlation, as mentioned before, is a mixed blessing. Depending on which direction things go, it can bring the only possibility of completing all the features in the project. Notice that in Table 1 , all the Could Have can only be completed when the efforts are highly correlated since all of them must be low. Under the independence assumption, when some could be low and others high, there is no chance of completing them on or under budget.

With regards to question 3, the 60, 20, 20% allocation seems to be the “Goldilocks” solution, balancing predictability with level of ambition. As shown by Fig.  7 , changing the allocation from 60 to 70%, has a dramatic impact on the safety margin which, at the up to 100% underestimation level, drops from 98.5 to 34%.

Finally, it is worth making clear, that the analysis refers to variations in execution times of planned work and not changes in project scope, which should be addressed differently.

The author gratefully acknowledges the helpful comments of Hakan Erdogmus. Diego Fontdevila and Alejandro Bianchi on earlier versions of this paper.

These preferences might induce dependencies that need to be addressed by the team, either by incorporating lower preference features in the higher categories or by doing additional work to mock the missing capabilities.

Worst case, means that if some of the assumptions associated with the scenario were to change, the probability of delivering within budget would increase.

@Risk by Palisade, Crystal Ball by Oracle, ModelRisk by Vose and Argo by Booz Allen among others.

Parkinson’s Law, the 1955 assertion by British economist Cyril Northcote Parkinson, that “Work expands so as to fill the time available for its completion”, regardless of what was strictly necessary.

Student Syndrome, a term introduced by Eliyahu M. Goldratt in his 1997 novel Critical Chain to describe the planned procrastination of tasks by analogy with a student leaving working in an assignment until the last day before its due date.

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Miranda, E. (2022). Moscow Rules: A Quantitative Exposé. In: Stray, V., Stol, KJ., Paasivaara, M., Kruchten, P. (eds) Agile Processes in Software Engineering and Extreme Programming. XP 2022. Lecture Notes in Business Information Processing, vol 445. Springer, Cham. https://doi.org/10.1007/978-3-031-08169-9_2

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Sustainability: is it a strategic management research fashion.

1. Introduction

2. literature review, 3. materials and methods, 4. results and discussion, 4.1. identification of sustainability as a strategic management research fashion, 4.2. identification of areas contributing to the advancement of sustainability as a strategic management research fashion.

• Red cluster No. 1: strategic and innovation-driven sustainability. This cluster highlights the importance of sustainability in SM, with key themes such as sustainability ( n = 220), SM ( n = 65) and innovation ( n = 45). This emphasises how sustainability shapes strategy research. It influences leadership, climate change, project management, knowledge management, human resource management (HRM), industry 4.0, etc.
• Green cluster No. 2: operational corporate sustainability. This emphasises sustainability in regulatory, operational and competitive terms, focusing on SD ( n = 229), CSR ( n = 51) and environmental management ( n = 37). This cluster thus emphasises how academic research motivated by CSR initiatives and environmental compliance applies to sustainability concepts. The attention paid to corporate sustainability, economic and social effects, competition and SD suggests a wide involvement with the SDGs from different organisational perspectives.
• Blue cluster No. 3: crisis management and environmental economics. This focuses on strategic development in response to global disruptions and market dynamics, including strategic approach ( n = 63), pandemic ( n = 33) and stakeholder engagement ( n = 19). Thus, the keyword “pandemic”, along with the strategic approach, emphasises the need to take disruptions and resilience into account in academic research, together with stakeholders and business strategy.
• Yellow cluster No. 4: sustainable supply chain and resource management. This cluster focuses on sustainable supply chain management and resource efficiency, and emphasises the need to enhance supply chain sustainability and resource optimisation in SM. Among the topics covered are supply chain management ( n = 58), SDGs ( n = 21), circular economy ( n = 37) and waste management ( n = 21), highlighting academic research focused on waste reduction, circular economy, resource reuse and achieving the SDGs.
• Motor themes (upper-right quadrant): This quadrant covers CE, industry 4.0, environmental sustainability, sustainable supply chain management, digitalisation, project management and decision-making, COVID-19, SDGs, stakeholder theory, competitiveness, sustainability reporting and sustainable tourism, etc. themes. These themes are well developed and central to ongoing sustainability and SM research, reflecting their essential role in fostering future achievements and innovation. The high centrality and density of these themes thus indicate their importance and strong development in this area [ 106 ].
• Niche themes (upper-left quadrant): This includes themes such as competitive advantage, dynamic capabilities, resource-based theory (view) and firm performance. These themes are highly specialised and internally strong, but they need to be integrated into the more comprehensive research network. They are essential research areas but have a different broad impact and direct relevance to sustainability than motor themes. Accordingly, these themes show high density but low centrality, being well developed but relatively isolated [ 106 ].
• Emerging or declining themes (lower-left quadrant): This contains talent management, knowledge management and sustainability performance themes. They may represent new research areas or those that are losing popularity in academia. The future direction of these themes depends on their ability to gain prominence and integration into the broader research discourse. Consequently, they have a low centrality and density, suggesting they are either developing or becoming less important [ 106 ].
• Basic themes (lower-right quadrant): These themes can be described as a basis for research on sustainability and SM. Thus, they also serve as a foundation for further study in many areas. This quadrant includes themes such as sustainability, corporate sustainability and SD, SM, strategies, CSR, innovation and entrepreneurship. As a result, these themes are characterised by high centrality but low density [ 106 ].

5. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, appendix a. overview of the most relevant analysed articles in the fields of sustainability and strategic management based on query 2.

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Share and Cite

Sedovs, E.; Volkova, T. Sustainability: Is It a Strategic Management Research Fashion? Sustainability 2024 , 16 , 7434. https://doi.org/10.3390/su16177434

Sedovs E, Volkova T. Sustainability: Is It a Strategic Management Research Fashion? Sustainability . 2024; 16(17):7434. https://doi.org/10.3390/su16177434

Sedovs, Edgars, and Tatjana Volkova. 2024. "Sustainability: Is It a Strategic Management Research Fashion?" Sustainability 16, no. 17: 7434. https://doi.org/10.3390/su16177434

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LBS Journal of Management & Research

• Submit your paper
• Author guidelines
• Editorial team
• Indexing & metrics
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Before you start

For queries relating to the status of your paper pre decision, please contact the Editor or Journal Editorial Office. For queries post acceptance, please contact the Supplier Project Manager. These details can be found in the Editorial Team section.

Author responsibilities

Our goal is to provide you with a professional and courteous experience at each stage of the review and publication process. There are also some responsibilities that sit with you as the author. Our expectation is that you will:

• Respond swiftly to any queries during the publication process.
• Be accountable for all aspects of your work. This includes investigating and resolving any questions about accuracy or research integrity .
• Treat communications between you and the journal editor as confidential until an editorial decision has been made.
• Include anyone who has made a substantial and meaningful contribution to the submission (anyone else involved in the paper should be listed in the acknowledgements).
• Exclude anyone who hasn’t contributed to the paper, or who has chosen not to be associated with the research.
• In accordance with COPE’s position statement on AI tools , Large Language Models cannot be credited with authorship as they are incapable of conceptualising a research design without human direction and cannot be accountable for the integrity, originality, and validity of the published work. The author(s) must describe the content created or modified as well as appropriately cite the name and version of the AI tool used; any additional works drawn on by the AI tool should also be appropriately cited and referenced. Standard tools that are used to improve spelling and grammar are not included within the parameters of this guidance. The Editor and Publisher reserve the right to determine whether the use of an AI tool is permissible.
• If your article involves human participants, you must ensure you have considered whether or not you require ethical approval for your research, and include this information as part of your submission. Find out more about informed consent .

Generative AI usage key principles

• Copywriting any part of an article using a generative AI tool/LLM would not be permissible, including the generation of the abstract or the literature review, for as per Emerald’s authorship criteria, the author(s) must be responsible for the work and accountable for its accuracy, integrity, and validity.
• The generation or reporting of results using a generative AI tool/LLM is not permissible, for as per Emerald’s authorship criteria, the author(s) must be responsible for the creation and interpretation of their work and accountable for its accuracy, integrity, and validity.
• The in-text reporting of statistics using a generative AI tool/LLM is not permissible due to concerns over the authenticity, integrity, and validity of the data produced, although the use of such a tool to aid in the analysis of the work would be permissible.
• Copy-editing an article using a generative AI tool/LLM in order to improve its language and readability would be permissible as this mirrors standard tools already employed to improve spelling and grammar, and uses existing author-created material, rather than generating wholly new content, while the author(s) remains responsible for the original work.
• The submission and publication of images created by AI tools or large-scale generative models is not permitted.

Research and publishing ethics

Our editors and employees work hard to ensure the content we publish is ethically sound. To help us achieve that goal, we closely follow the advice laid out in the guidelines and flowcharts on the COPE (Committee on Publication Ethics) website .

We have also developed our research and publishing ethics guidelines . If you haven’t already read these, we urge you to do so – they will help you avoid the most common publishing ethics issues.

A few key points:

• Any manuscript you submit to this journal should be original. That means it should not have been published before in its current, or similar, form. Exceptions to this rule are outlined in our pre-print and conference paper policies .  If any substantial element of your paper has been previously published, you need to declare this to the journal editor upon submission. Please note, the journal editor may use  Crossref Similarity Check  to check on the originality of submissions received. This service compares submissions against a database of 49 million works from 800 scholarly publishers.
• Your work should not have been submitted elsewhere and should not be under consideration by any other publication.
• If you have a conflict of interest, you must declare it upon submission; this allows the editor to decide how they would like to proceed. Read about conflict of interest in our research and publishing ethics guidelines .
• By submitting your work to Emerald, you are guaranteeing that the work is not in infringement of any existing copyright.
• If you have written about a company/individual/organisation in detail using information that is not publicly available, have spent time within that company/organisation, or the work features named/interviewed employees, you will need to clear permission by using the  consent to publish form ; please also see our permissions guidance for full details. If you have to clear permission with the company/individual/organisation, consent must be given either by the named individual in question or their representative, a board member of the company/organisation, or a HR department representative of the company/organisation.
• You have an ethical obligation and responsibility to conduct your research in adherence to national and international research ethics guidelines, as well as the ethical principles outlined by your discipline and any relevant authorities, and to be transparent about your research methods in such a way that all involved in the publication process may fairly and appropriately evaluate your work. For all research involving human participants, you must ensure that you have obtained informed consent, meaning that you must inform all participants in your work (or their legal representative) as to why the research is being conducted, whether their anonymity is protected, how their data will be stored and used, and whether there are any associated risks from participation in the study; the submitted work must confirm that informed consent was obtained and detail how this was addressed in accordance with our policy on informed consent .  
• Where appropriate, you must provide an ethical statement within the submitted work confirming that your research received institutional and national (or international) ethical approval, and that it complies with all relevant guidelines and regulations for studies involving humans, whether that be data, individuals, or samples. Specifically, the statement should contain the name and location of the institutional ethics reviewing committee or review board, the approval number, the date of approval, and the details of the national or international guidelines that were followed, as well as any other relevant information. You should also include details of how the work adheres to relevant consent guidelines along with confirming that informed consent was secured for all participants. The details of these statements should ensure that author and participant anonymity is not compromised. Any work submitted without a suitable ethical statement and details of informed consent for all participants, where required, will be returned to the authors and will not be considered further until appropriate and clear documentation is provided. Emerald reserves the right to reject work without sufficient evidence of informed consent from human participants and ethical approval where required.

Third party copyright permissions

Prior to article submission, you need to ensure you’ve applied for, and received, written permission to use any material in your manuscript that has been created by a third party. Please note, we are unable to publish any article that still has permissions pending. The rights we require are:

• Non-exclusive rights to reproduce the material in the article or book chapter.
• Print and electronic rights.
• Worldwide English-language rights.
• To use the material for the life of the work. That means there should be no time restrictions on its re-use e.g. a one-year licence.

We are a member of the International Association of Scientific, Technical, and Medical Publishers (STM) and participate in the STM permissions guidelines , a reciprocal free exchange of material with other STM publishers.  In some cases, this may mean that you don’t need permission to re-use content. If so, please highlight this at the submission stage.

Please take a few moments to read our guide to publishing permissions  to ensure you have met all the requirements, so that we can process your submission without delay.

Open access information

This is a sponsored open access journal, also referred to as platinum open access. Because it is published in partnership with an organisation, your article will be published open access, but you will not have to pay an APC (article processing charge) - publication is free. Your article will be published with a Creative Commons CC BY 4.0 user licence , which outlines how readers can reuse your work.

You can find out more about our open access routes and read our FAQs on our open research page. 

Find out about open

Transparency and Openness Promotion (TOP) Guidelines

We are a signatory of the Transparency and Openness Promotion (TOP) Guidelines , a framework that supports the reproducibility of research through the adoption of transparent research practices. That means we encourage you to:

• Cite and fully reference all data, program code, and other methods in your article.
• Include persistent identifiers, such as a Digital Object Identifier (DOI), in references for datasets and program codes. Persistent identifiers ensure future access to unique published digital objects, such as a piece of text or datasets. Persistent identifiers are assigned to datasets by digital archives, such as institutional repositories and partners in the Data Preservation Alliance for the Social Sciences (Data-PASS).
• Follow appropriate international and national procedures with respect to data protection, rights to privacy and other ethical considerations, whenever you cite data. For further guidance please refer to our  research and publishing ethics guidelines . For an example on how to cite datasets, please refer to the references section below.

Prepare your submission

Manuscript support services.

We are pleased to partner with Editage, a platform that connects you with relevant experts in language support, translation, editing, visuals, consulting, and more. After you’ve agreed a fee, they will work with you to enhance your manuscript and get it submission-ready.

This is an optional service for authors who feel they need a little extra support. It does not guarantee your work will be accepted for review or publication.

Visit Editage

Manuscript requirements

Before you submit your manuscript, it’s important you read and follow the guidelines below. You will also find some useful tips in our structure your journal submission how-to guide.

Submit your manuscript

There are a number of key steps you should follow to ensure a smooth and trouble-free submission.

Double check your manuscript

Before submitting your work, it is your responsibility to check that the manuscript is complete, grammatically correct, and without spelling or typographical errors. A few other important points:

• Give the journal aims and scope a final read. Is your manuscript definitely a good fit? If it isn’t, the editor may decline it without peer review.
• Does your manuscript comply with our research and publishing ethics guidelines ?
• Have you cleared any necessary publishing permissions ?
• Have you followed all the formatting requirements laid out in these author guidelines?
• If you need to refer to your own work, use wording such as ‘previous research has demonstrated’ not ‘our previous research has demonstrated’.
• If you need to refer to your own, currently unpublished work, don’t include this work in the reference list.
• Any acknowledgments or author biographies should be uploaded as separate files.
• Carry out a final check to ensure that no author names appear anywhere in the manuscript. This includes in figures or captions.

You will find a helpful submission checklist on the website Think.Check.Submit .

The submission process

All manuscripts should be submitted through our editorial system by the corresponding author.

The only way to submit to the journal is through the journal’s ScholarOne site as accessed via the Emerald website, and not by email or through any third-party agent/company, journal representative, or website. Submissions should be done directly by the author(s) through the ScholarOne site and not via a third-party proxy on their behalf.

A separate author account is required for each journal you submit to. If this is your first time submitting to this journal, please choose the Create an account or Register now option in the editorial system. If you already have an Emerald login, you are welcome to reuse the existing username and password here.

Please note, the next time you log into the system, you will be asked for your username. This will be the email address you entered when you set up your account.

Don't forget to add your  ORCiD ID during the submission process. It will be embedded in your published article, along with a link to the ORCiD registry allowing others to easily match you with your work.

Don’t have one yet? It only takes a few moments to register for a free ORCiD identifier .

Visit the ScholarOne support centre  for further help and guidance.

What you can expect next

You will receive an automated email from the journal editor, confirming your successful submission. It will provide you with a manuscript number, which will be used in all future correspondence about your submission. If you have any reason to suspect the confirmation email you receive might be fraudulent, please contact the journal editor in the first instance.

Post submission

Review and decision process.

Each submission is checked by the editor. At this stage, they may choose to decline or unsubmit your manuscript if it doesn’t fit the journal aims and scope, or they feel the language/manuscript quality is too low.

While all journals work to different timescales, the goal is that the editor will inform you of their first decision within 60 days.

During this period, we will send you automated updates on the progress of your manuscript via our submission system, or you can log in to check on the current status of your paper.  Each time we contact you, we will quote the manuscript number you were given at the point of submission. If you receive an email that does not match these criteria, it could be fraudulent and we recommend you contact the journal editor in the first instance.

Manuscript transfer service

Emerald’s manuscript transfer service takes the pain out of the submission process if your manuscript doesn’t fit your initial journal choice. Our team of expert Editors from participating journals work together to identify alternative journals that better align with your research, ensuring your work finds the ideal publication home it deserves. Our dedicated team is committed to supporting authors like you in finding the right home for your research.

If a journal is participating in the manuscript transfer program, the Editor has the option to recommend your paper for transfer. If a transfer decision is made by the Editor, you will receive an email with the details of the recommended journal and the option to accept or reject the transfer. It’s always down to you as the author to decide if you’d like to accept. If you do accept, your paper and any reviewer reports will automatically be transferred to the recommended journals. Authors will then confirm resubmissions in the new journal’s ScholarOne system.

Our Manuscript Transfer Service page has more information on the process.

If your submission is accepted

All accepted authors are sent an email with a link to a licence form.  This should be checked for accuracy, for example whether contact and affiliation details are up to date and your name is spelled correctly, and then returned to us electronically. If there is a reason why you can’t assign copyright to us, you should discuss this with your journal content editor. You will find their contact details on the editorial team section above.

Proofing and typesetting

Two to three months before the scheduled print publication of an issue, we carry out editorial checks on your paper and a pre-typeset version appears in the Accepted Articles section of the journal’s online content. Your paper is then copyedited, typeset, and proofs are sent to you (if you are the corresponding author) for your review. You receive advance notification of this.  Please note, this is your opportunity to correct any typographical errors, grammatical errors or incorrect author details. We can’t accept requests to rewrite texts at this stage.

How to share your paper

Visit our author rights page  to find out how you can reuse and share your work.

To find tips on increasing the visibility of your published paper, read about  how to promote your work .

Correcting inaccuracies in your published paper

Sometimes errors are made during the research, writing and publishing processes. When these issues arise, we have the option of withdrawing the paper or introducing a correction notice. Find out more about our  article withdrawal and correction policies .

Need to make a change to the author list? See our frequently asked questions (FAQs) below.

Frequently asked questions

Editor-in-Chief

• Dr. Praveen Gupta Lal Bahadur Shastri Institute of Management - India

Managing Editor

• Dr. Gaurav Joshi Lal Bahadur Shastri Institute of Management - India [email protected]

Deputy Editor

• Dr. Anchal Gupta Lal Bahadur Shastri Institute of Management - India

Editorial Assistant

• Dr. Neha Bhatnagar Lal Bahadur Shastri Institute of Management - India

Editorial Advisory Board

• Dr. Amit Bardhan Faculty of Management Studies, Delhi - India
• Dr. Arpan Kar Indian Institute of Technology Delhi - India
• Dr. Neeraj Kaushik National Institute of Technology Kurukshetra - India
• Dr. Atul Shiva Chandigarh University - India
• Prof. O.P. Wali Indian Institute of Foreign Trade, New Delhi - India
• Dr. Abhishek Borah Institut Européen d'Administration des Affaires - France
• Prof. Kanika T. Bhal Indian Institute of Technology Delhi - India
• Prof. Harsh V. Verma Faculty of Management Studies, Delhi - India
• Prof. Shailaja Karve KJ Somaiya Institute of Management - India
• Dr. Smita Kashiramka Indian Institute of Technology Delhi - India
• Dr. Selim Ahmed World University of Bangladesh - Bangladesh
• Dr. Muhammad Ali UCSI University - Malaysia
• Prof. Pratheepkanth Puwanenthiren University of Jaffna - Sri Lanka
• Dr. Praveen Ranjan Srivastava Indian Institute of Management, Rohtak (IIM-R) - India
• Dr. Neeraj Pandey Indian Institute of Management, Mumbai - India
• Serena Tsai Emerald Publishing [email protected]

Journal Editorial Office (For queries related to pre-acceptance)

• Shivani Verma Emerald Publishing [email protected]

Supplier Project Manager (For queries related to post-acceptance)

• Subha Arounkumar Emerald Publishing [email protected]

Citation metrics

Publication timeline.

Time to first decision

Further information

Time to first decision , expressed in days, the "first decision" occurs when the journal’s editorial team reviews the peer reviewers’ comments and recommendations. Based on this feedback, they decide whether to accept, reject, or request revisions for the manuscript.

Data is taken from submissions between 1st June 2023 and 31st May 2024

Acceptance to publication

Acceptance to publication , expressed in days, is the average time between when the journal’s editorial team decide whether to accept, reject, or request revisions for the manuscript and the date of publication in the journal. 

Data is taken from the previous 12 months (Last updated July 2024)

Acceptance rate

The acceptance rate is a measurement of how many manuscripts a journal accepts for publication compared to the total number of manuscripts submitted expressed as a percentage %

Data is taken from submissions between 1st June 2023 and 31st May 2024 .

This figure is the total amount of downloads for all articles published early cite in the last 12 months

(Last updated: July 2024)

This journal is included in the following abstract and indexing services:

• CNKI Scholar
• Directory of Open Access Journals (DOAJ)
• EBSCO Discovery Service
• Google Scholar
• Indian Citation Index
• Indian Science
• Summons (ProQuest)

Reviewer information

Peer review process.

This journal engages in a double-anonymous peer review process, which strives to match the expertise of a reviewer with the submitted manuscript. Reviews are completed with evidence of thoughtful engagement with the manuscript, provide constructive feedback, and add value to the overall knowledge and information presented in the manuscript.

The mission of the peer review process is to achieve excellence and rigour in scholarly publications and research.

Our vision is to give voice to professionals in the subject area who contribute unique and diverse scholarly perspectives to the field.

The journal values diverse perspectives from the field and reviewers who provide critical, constructive, and respectful feedback to authors. Reviewers come from a variety of organizations, careers, and backgrounds from around the world.

All invitations to review, abstracts, manuscripts, and reviews should be kept confidential. Reviewers must not share their review or information about the review process with anyone without the agreement of the editors and authors involved, even after publication. This also applies to other reviewers’ “comments to author” which are shared with you on decision.

Resources to guide you through the review process

Discover practical tips and guidance on all aspects of peer review in our reviewers' section. See how being a reviewer could benefit your career, and discover what's involved in shaping a review.

More reviewer information

Calls for papers

Lbs journal of management & research special issue guide.

A special issue allows a journal to focus on a topic – often in a new or emerging area – and explore it in depth or provide alternative perspectives. Special issues can also collate the best papers presented at a conference. They can even take an ...

Open Call for Papers

LBS Journal of Management Research (LBSJMR) is a double-blind peer-reviewed bi-annual publication hosted on Emerald Insight, accessed by a global network, and published under CC 4.0 to maximize dissemination. Each article published in LBSJMR is as...

Emerald Publishing partners with the Lal Bahadur Shastri Institute of Management, India to publish LBS Journal of Management and Research

Emerald Publishing House, UK announces its partnership with the Lal Bahadur Shastri Institute of Management (LBSIM), Delhi, India to publish their LBS Journal of Management and Research (LBSJMR). The LBSJMR ...

LBS Journal of Management & Research is bi-annual peer reviewed journal welcoming high-quality original contributions on different functional areas of management.

Aims and scope

When submitting a manuscript, authors will be taken to a service called  Paperpal Preflight , an AI-driven tool that checks manuscripts against the journal's author guidelines. Authors are free to use or bypass this step and submit directly to ScholarOne .

LBS Journal of Management & Research seeks to strike a balance between theoretical paradigms and business practices by focusing on current and future trends in the area of management. It provides a forum for researchers and business professionals worldwide to make significant contributions on key issues and challenges emerging in management and its related fields in today’s VUCA world.

It welcomes the publication of research paper/ critical review articles/ case studies/ book reviews with an emphasis on various aspects of management and its related issues.

The title covers but is not limited to:

• General Management
• Finance and Banking
• Operations and Supply Chain Management
• Corporate Governance and Entrepreneurship
• Organizational theory
• Information and Knowledge Management
• Business Ethics & Sustainable Development
• International Business
• Data Science and analytics

Latest articles

These are the latest articles published in this journal (Last updated: July 2024 )

Unravelling market access opportunities through one stop border posts: traders' perceptions on the influence of administrative practices and cross-cultural competences

Procurement of good governance as a strategic tool for achieving value for money in public construction projects, terrorism and its impact on the stock market: broad results from tunisia, top downloaded articles.

These are the most downloaded articles over the last 12 months for this journal (Last updated: July 2024 )

Do social media marketing activities promote customer loyalty? A study on the e-commerce industry

The influence of sustainable supply chain management practices on financial sustainability of food processing smes, work home interaction: a challenge to human resource management.

These are the top cited articles for this journal, from the last 12 months according to Crossref (Last updated: July 2024 )

Exploring the linkage between Business Incubation and Entrepreneurship: Understanding, Trends, Themes, and Future research agenda

Behavioural uncertainty and environmental sustainability of restaurant businesses: the moderating role of purchasing technical knowledge, transparency statement.

Transparency Statement for LBS Journal of Management & Research

1. Journal Ownership: LBS Journal of Management & Research  is published by Emerald Publishing on behalf of the  Lal Bahadur Shastri Institute of Management .

2. Governing Body:  The editorial team is appointed and managed by the  Lal Bahadur Shastri Institute of Management .  The journal is governed by the editorial team in collaboration with Emerald Publishing.

3. Peer Review Process:  The journal operates a double blind peer review model. All articles undergo an initial assessment by the journal editor. If they are considered suitable for consideration, articles will then be a reviewed by a minimum of two external reviewers to assess suitability for publication. Final responsibility for editorial decisions rests with the Editor-in-Chief of the journal.

4. Editorial team/contact information:  Contact details for the editorial team can be found on the journal homepage  here . Queries may also be directed to Emerald Publishing Services team as follows:

Serena Tsai: Publishing Services Manager –  [email protected]

5. Copyright : All articles in the journal are published Open Access under a Creative Commons Attribution license (CC BY-4.0). This allows authors to retain copyright of their work whilst others can share, use and build upon this work created as long as appropriate attribution is given.

6. Author Fees : The journal is published under a Platinum Open Access arrangement, in that all costs associated with publishing an Open Access article in the journal are funded by the  Lal Bahadur Shastri Institute of Management .  There are currently no Article Processing Charges to the author(s).

7. Allegations of Misconduct:  All journals published by Emerald are members of and subscribe to the principles of the  Committee on Publication Ethics . In the event of any allegation of research or publication misconduct the publisher and editor will adhere to COPE guidelines in dealing with such allegations.

8. Conflicts of interest:  Authors are asked to declare any financial or ethical conflicts of interest upon submitting their work to the journal. Difficult cases will be referred to the Committee on Publishing Ethics (COPE) for advice.

9. Frequency:  The journal publishes two issues per annum

10. Access:  All journal articles are published Open Access on EmeraldInsight.com -  LBS Journal of Management & Research | Emerald Insight  under a CCBY 4.0 licence (please see section 5).

11. Revenue sources:  The journal is published under a platinum Open Access arrangement, in that all costs associated with publishing an Open Access article in the journal are funded by the  Lal Bahadur Shastri Institute of Management .

12. Advertising:  The journal does not accept direct advertising

13. Archiving:  Emerald provides perpetual access for all e-journal content by working with digital preservation schemes Portico, LOCKSS and CLOCKSS.

14. Originality:  Any manuscript you submit to this journal should be original. That means it should not have been published before in its current, or similar, form. Exceptions to this rule are outlined in our  pre-print and conference paper policies .  If any substantial element of your paper has been previously published, you need to declare this to the journal editor upon submission. Please note, the journal editor may use  Crossref Similarity Check  to check on the originality of submissions received. For further information on originality, please see our  editorial policy & originality guidelines .

15. Direct marketing:  On occasion the journal will use direct marketing activities (primarily email campaigns) to raise awareness of the journal and to invite authors to submit articles.   Marketing activities are conducted by the  Lal Bahadur Shastri Institute of Management   unless otherwise agreed with Emerald.

This statement was prepared by Serena Tsai (Emerald Publishing Services) on 26 October 2023.

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More Articles in this Issue

Student research showdown: a research communication competition.

Student researchers are rarely trained to explain their work to a general audience but must do so throughout their careers. To assist undergraduate researchers in building this skill, the Student Research Showdown—a research video and presentation competition—was created at the University of Texas at Austin. Students create brief videos on which their peers vote, and the top video creators face off with presentations and are awarded prizes by a panel of judges. Students reflect on their experiential learning as they construct a narrative that disseminates their findings, communicates impact, and serves as a sharable testament to their success. Indirect measures indicate that students improve their research communication skills by participating in this event.

Social and Human Capital Influences on Undergraduate Researchers’ Disciplinary Identity: The Case of Social and Natural Scientists

Disciplinary identity, or connection to a particular academic discipline, is constructed through a developmental process across a scholar’s academic life course. Using unique data from an online survey of students at four different types of colleges and universities, this study investigates the extent to which disciplinary identity among undergraduate researchers is connected to their human and social capital and differs between social and natural scientists. Disciplinary identity for natural scientists is correlated with mentoring, being a first-generation student, and having a disability, whereas disciplinary identity for social scientists is correlated with only one factor: grit. Implications for institutions and undergraduate programs desiring to enhance the connection between students and their fields of study are discussed.

Leveraging the Role of Library Partnerships to Understand Undergraduate Research Contributions to Humanities Scholarship: A Case Study

The library and the Undergraduate Research Opportunity Program at the University of Michigan partnered in an investigation of student-faculty research collaboration in the humanities. The authors found that providing early opportunities for undergraduates to collaborate on such projects was highly beneficial for both students and faculty mentors. Students contributed and gained invaluable skills in an experience they stated was more meaningful than that of a conventional classroom, whereas faculty mentors could juggle multiple projects, benefit from students’ technological skills, and articulate the salience of their research processes and their work. The authors also discuss the role of the library as a crucial catalyst in changing the perception of the humanities at higher education institutions, particularly as it exposes students to research projects and professions within the library.

Knowledge Makers: Indigenous Undergraduate Researchers and Research

As higher education institutions seek to provide further scope for diversity in their practices, there is space opening up for Indigenous undergraduate research. This article reflects on the Knowledge Makers program, an Indigenous undergraduate research initiative based in British Columbia, Canada. The Knowledge Makers program shows what is possible when an Indigenous approach is used to mentor Indigenous undergraduate students and offers promising practices for increasing Indigenous researchers and research, such as drawing on the knowledge of Indigenous ancestors, valuing Indigenous research methodologies, using a strengths-based approach, performing research as a type of service, and committing to legacy.

Undergraduate Research Highlights – Spring 2019

Establishing a statewide celebration of undergraduate research: history and lessons learned.

The Florida Undergraduate Research Conference (FURC) is an annual multidisciplinary conference that enables student scholars to present their research, network with other students, and attend professional development seminars. FURC has been held since 2011 and has featured more than 2,100 student presentations with participation from a broad array of institutions within the state. Survey data indicate that FURC is the first conference presentation for the majority of participants and that participation in the conference is associated with several positive outcomes. This article describes the history, structure, and planning of the conference and as well provides survey and outcome data that may assist other states and geographic areas as they consider forming their own conferences.

Learning “Scholarship as Conversation” by Writing Book Reviews

The ability of undergraduate students to write for scholarly audiences is contingent upon their capacity to recognize that scholarship is a kind of conversation. For a student, writing an academic book review is a near ideal yet generally underutilized opportunity to learn this lesson. Through analysis of previously published book reviews coproduced with students, the authors identify actionable practices to transform the process of writing book reviews from an undervalued, lone activity into a viable form of undergraduate research. Publishing coauthored book reviews may aid students seeking admission to graduate school and faculty seeking promotion. In the end, writing book reviews with students is an opportunity for faculty and librarians to pass along the important lesson that scholarship is an important, inclusive conversation.

A Peer Research Consultant Program: Feasibility and Outcomes

The authors discuss their study of the Peer Research Consultant (PRC) program at California State University, San Bernardino. During the 2016–2017 academic year, 13 courses, with 853 students enrolled, participated in the PRC program. Program participants completed pre and post-measures assessing demographic information, perceptions of skill level, and perceived gains following program participation. Students who participated in the PRC program showed an increase in overall course grades (M = 3.11) compared to those who did not (M = 2.82; p < 0.05). Similar findings were observed among under-represented minority (URM) students who participated (M = 3.05) and those who did not (M = 2.73, p < 0.05). Program participants reported high satisfaction with the program and improved confidence in skills

Introduction – Spring 2019

Table of contents – spring 2019.

SUBSCRIPTION

SPUR advances knowledge and understanding of novel and effective approaches to mentored undergraduate research, scholarship, and creative inquiry by publishing high-quality, rigorously peer reviewed studies written by scholars and practitioners of undergraduate research, scholarship, and creative inquiry. The SPUR Journal is a leading CUR member benefit. Gain access to all electronic articles by joining CUR.