Editorial: Navigating excellence: understanding and overcoming common causes of manuscript rejections in logistics and supply chain management research

International Journal of Physical Distribution & Logistics Management

ISSN: 0960-0035

Article publication date: 7 May 2024

Issue publication date: 7 May 2024

866

Citation

Russo, I. and Wong, C.Y. (2024), "Editorial: Navigating excellence: understanding and overcoming common causes of manuscript rejections in logistics and supply chain management research", International Journal of Physical Distribution & Logistics Management, Vol. 54 No. 2, pp. 211-228. https://doi.org/10.1108/IJPDLM-03-2024-554

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited


In the dynamic realm of logistics, supply chain management and operations, the International Journal of Physical Distribution and Logistics Management (IJPDLM) stands as a beacon for scholarly excellence, seeking to advance the understanding of strategic issues in these crucial domains.

Since its inception in 1970, IJPDLM has consistently emphasized the intersection of rigor, novelty, theory and relevance. Since early 1990, in Volume 20, Issue 1, with the first online Issue, the journal not only explored the central issues of theory-practice discourse but also advanced scholarly contributions by delving into rigorous approaches, novel perspectives, and foundational theoretical frameworks in the realms of strategy, decision-making, supply chain alignment with customers and in-depth corporate and country case-studies.

At the heart of IJPDLM’s mission is a commitment to publish original research studies that are strategically focused, theoretically grounded and contribute significantly to the body of knowledge in business logistics, physical and retail distribution, purchasing, operations and supply chain management.

As the custodian of rigorous empirical methodology and a stronghold for papers with a strong theoretical basis, IJPDLM places a premium on the quality, relevance and impact of the research it disseminates. The journal aims not merely to provide a platform for publication but to foster a community of scholars who engage in thoughtful and influential research, pushing the boundaries of our understanding of supply chain and logistics management (LSCM).

In aligning with the broader goals of IJPDLM, the editorial team recognizes the importance of a judicious and thorough review process. While essential, a commitment to faster review cycles and an increased volume of published manuscripts must not compromise the journal’s commitment to maintaining high-quality standards. The decision-making process, exemplified by the “reject and resubmit” option, reflects the journal’s dedication to supporting authors in refining and enhancing their research work and offering details about weaknesses.

However, as with any rigorous editorial process, certain manuscripts may face rejection due to specific issues that impede their alignment with the journal’s standards.

In academic life, rejection, as depicted in this editorial, is a well-known experience for every scholar. As Editors, around four out of five decisions we make involve rejections, a norm shared by business management and premier LSCM journals. Our commitment to offering constructive, supportive feedback underscores the importance of the tone in alleviating the disappointment associated with these outcomes.

In this editorial, we shed light on the five primary reasons for rejections encountered in the review process, each representing a facet crucial to the integrity and scholarly impact of the published work:

  1. Superficial/inappropriate use of theory

  2. Lack of novelty.

  3. Data, rigor and measurement issues

  4. Descriptive analysis lacking theoretical insights

  5. Descriptive analysis of structure literature review

By understanding and addressing these key points, authors can navigate the submission process more effectively, ensuring that their contributions meet the high-quality standards set by IJPDLM. This proactive approach not only streamlines the review process but also positions the journal as a catalyst for advancing the field of LSCM.

Regardless of the decision or stage in the process, we intend to provide all authors with comprehensive and constructive feedback to assist them in developing their papers.

Upon receiving a manuscript at the Editorial Office, the initial scrutiny is conducted by the Editorial Assistant, who meticulously assesses the submission against the editorial guidelines and ensures alignment with the focused aims and scope of IJPDLM. Following this preliminary evaluation, the manuscript is then forwarded to the Editors-in-Chief (EICs), who assume the crucial responsibility of deciding whether to advance the paper for further formal review.

At IJPDLM, we prioritize delivering our authors a valuable and efficient review process. Out of the 1,313 papers submitted to the Journal during the three years from 2021 to 2023, approximately 14% underwent the rigorous review process. Reflecting our commitment to efficiency, the average duration for the EIC to determine whether a paper warrants review or not was 20 days over these three years. The first revision decisions, on average, take 72.0 days (Major revision) and 50.75 days (Minor revision). Moreover, there has been a substantial shift for papers that move under a formal review process, with the average acceptance rate increasing to about 78% from 2021 to 2023.

Our commitment to authors remains unwavering for the remaining 86% of papers that did not proceed to review. Upon submission of your paper, desk-rejected manuscripts will receive structured and comprehensive feedback from the editors or senior associate editors. While these extra efforts take time, you can also expect to receive this feedback within less than three weeks, which has improved from 31–58 days in 2019–2020.

We strive to offer comprehensive and constructive feedback, elucidating the rationale behind our decision and suggesting viable pathways for improvement. We are deeply committed to sharing our knowledge and contributing valuable insights to enhance the quality of submissions to IJPDLM as best as possible.

In this editorial focus, we focus on the prevailing reasons for desk rejection by the EICs. Understanding these common grounds for rejection is paramount, as they not only guide authors in refining their submissions before submitting but also contribute to maintaining the high standards and thematic coherence of IJPDLM.

We concur that fostering epistemic respect is vital in safeguarding individuals while concurrently promoting accelerated, innovative and inclusive scientific progress. A comprehensive review process exemplifies epistemic respect by meticulously evaluating arguments based on their validity, coherence and originality (Krlev and Spicer, 2023). This approach not only upholds the dignity of authors but also contributes to a scholarly environment that is both rigorous and supportive.

In the following, we provide readers with specific examples of reasons for rejection and suggest possible ways to overcome these challenges.

1. Superficial/inappropriate use of theory

Most academic journals expect a strong theoretical basis and contribution. One of the most common EICs’ comments is, “… it suffers from a lack of theoretical contributions.” However, many authors misunderstood the theoretical contribution by symbolically referring to a theory but providing a limited explanation of the underlying mechanisms of the phenomenon under study. Superficially referring to a known (grand) theory is a common mistake. For example, referring to resource-based view or dynamic capability theories is convenient when the study involves any form of capability. Naming capabilities as tangible, intangible or dynamic does not mean the resource-based view or dynamic capability is applied; this does not add meaningful theoretical explanations. Likewise, transaction cost economy or relational view theories are mentioned whenever buyer–supplier relationships are involved without applying their theoretical premises to explain what happens in the relationships.

Theoretical basis means an attempt to explain a phenomenon using assumptions, appropriate concepts and logical explanations. Authors often put little effort into clarifying assumptions. For example, one of the EICs’ comments highlights that a theory (e.g. Organizational Information Processing Theory – OIPT-) is applied “in an abstract, superficial and vague manner.” It is not that useful to claim OIPT is “applied” whenever the study involves some form of information processing activities (e.g. data analytics, information sharing, etc.). OIPT concerns the design of structure (or capabilities) to meet information processing needs, which may change when uncertainty increases. So, a study uses OIPT in an abstract, superficial and vague manner when assumptions about the types and levels of uncertainty are not clarified, meaning information processing needs (the main concept in OPIT) are not understood. Skipping these fundamental steps means ignoring the conceptualization and theorization steps, which are key to developing novel theoretical insights the journal expects.

The lack of efforts to lay down a theoretical basis (assumptions, conceptualization) often leads to speculative claims instead of logical explanations. For example, authors label any capability as dynamic capability and jump to the hypothesis that the capability leads to specific performance outcomes. This is a baseless and misleading claim. Likewise, any capability that has some form of information processing capacity is argued to produce better performance. This is another speculative claim.

The EICs have put much effort into educating authors who have superficially used theories. For example, the EICs pointed out that “the authors failed to use this theory to fully explain the entire model” to help the authors understand the theory cannot explain a part of the model. The EICs also explained to the authors the need to clarify important assumptions, e.g. “uncertainty is mentioned, but there is a need to clarify the specific types of uncertainty the model considers … so what uncertainties should be addressed/reduced for …” The submitted manuscript will receive content-based feedback on claims and assumptions to improve the ability to conceptualize and theorize.

While this space does not allow for an exhaustive exploration of the profound meaning of theory, it is widely recognized that theory forms a foundational element in business management, organizational studies, marketing and the broader social sciences. Its influence extends into Business LSCM within the domain of IJPDLM. In various social disciplines like LSCM, “theory” is often synonymous with explanatory theory, encompassing structural elements such as purpose, phenomenon, conceptual ordering mechanism, relevance criteria, intellectual insight, empirical support and boundary conditions (Sandberg and Alvesson, 2021). In essence, theory elucidates variables by addressing fundamental questions about individuals and elements. The domain delineates the circumstances under which the theory is anticipated to be applicable, addressing queries related to timing and location (who and what). During the relationship-building stage, the rationale is specified, elucidating the mechanisms and reasons behind the interconnectedness of variables by exploring the how and the why (Whetten, 1989; Van de Ven, 1989; Sandberg and Alvesson, 2021).

The development of theory design in LSCM becomes imperative for scholars to effectively align their research problems with practice and the goal of generating new knowledge. However, scholars often overlook or struggle to understand how to use theory, resorting to “post hoc theorizing,” adding theories after data collection based on what seems to align best with their findings. The manuscripts we rejected appear to begin with empirical work (picked some known constructs and collected data), and then the hypothesis development section was written by symbolically naming a theory. This tendency, coupled with the dominance of net-effects theorizing, constrains alternative styles of theorizing, leading to a homogeneous and impoverished comprehension of research phenomena (Delbridge and Fiss, 2013), hindering creative exploration beyond explanations and variable effects (Ketchen et al., 2022).

Despite the daunting perception of formal theories and frameworks, mid-range theories (Stank et al., 2017; Pellathy et al., 2018; Russo et al., 2021; Wowak et al., 2022; Stank et al., 2022), abductive theory elaboration approach (Kovács and Spens, 2005; DuHadway et al., 2022; Ahlqvist et al., 2023; Norrman and Prataviera, 2023; Russo et al., 2023; Bals et al., 2023; Sawyerr and Harrison, 2023) offer viable entry points for scholars who may otherwise be hesitant to engage in theorizing. Design science shapes and refines solutions, emphasizing pragmatic validity and long-term outcomes. Authors are encouraged to adopt explorative design science for methodological advancement, aiming to contribute actionable knowledge with pragmatic validity and practical relevance (Holmström et al., 2009; Oliva, 2019; Svanberg, 2020; Öhman et al., 2021; Bagni et al., 2022; Pfaff, 2023; Stark et al., 2023). There, scholars have more space to offer a nuanced description of complex and more profound mechanisms and contexts that focus on expanding the knowledge of specific supply chain management problems. Even though grand theories might offer some relevant ideas, contextualizing (based on the specific contexts facing the industry or country under study) and conceptualization offer a more nuanced understanding as opposed to efforts to chase generalization through de-contextualizing an explanation.

A recurring recommendation we offer to authors is the acknowledgment that rejection often stems from the inappropriate or superficial use of theory, notably evident in the absence of a robust theoretical foundation for examining and explaining conceptualizations at the unit of analysis level. It is important to underscore that merely selecting some constructs from a theory does not automatically equate to the effective application of that theory. Despite recognizing the relevance of specific research topics from an LSCM perspective, the editorial team encounters challenges in comprehending the theoretical contributions of models and their foundations within the current LSCM literature. This lack of clarity in articulating and applying theory constitutes a significant factor in the decision to reject manuscripts.

2. Lack of novelty

A lack of novelty sometimes comes from the superficial use of theory or data analysis at a superficial level. Sometimes, it comes from using known concepts, theories and models with minor, not-so-meaning changes. The journal expects significant advances in theoretical knowledge or new information that have significant research and/or practice implications. Combining constructs from several articles or disciplines does not add new knowledge if it does not offer novel theoretical explanations. On one occasion, EICs commented, “… a combination of the work of X and Y (2012), Z and Y (2013), and B et al. (2015). There is limited novelty here.” There is a difference between an arbitrary combination of ideas and using theoretical reasoning to combine ideas. It is the latter that potentially leads to new knowledge.

There is also a tendency to list topics that have been studied to justify a less studied topic. Studying a less studied or new topic (e.g. war in Ukraine or Gaza, circular economy, modern slavery) is not a contribution. Offering new explanations or new insights is. There is a tendency to treat new evidence as novelty. Adding another (quite similar) finding does not necessarily offer novelty. New evidence that comes to similar conclusions as past evidence may not offer sufficient novel knowledge. There is also a tendency to call mixed findings or lack of study as gaps and repeat this later as a novel contribution. Repeating phrases like “this study offers novel contributions” does not explain any contribution in a meaningful way. It is the explanation of how the new evidence changes current (theoretical) understanding that should be the focus.

Sometimes, authors are not aware of valuable novelty in their work. For example, one comment from EIC highlights, “… perhaps the novelty comes from modifying the coordination and cooperation constructs from X and Y (2012).” This often comes from a tendency to use existing ideas and ignore the significance of conceptual or construct clarification. Conceptual clarification can be a powerful contribution, as it contributes to theory elaboration. Sometimes, nuances and differences between behaviors or strategies (e.g. typology) can offer new understanding. Nuanced differences could potentially lead to a deeper insight. We often find manuscripts that ignore conceptual clarification or efforts to conceptualize because they do not attempt to clarify nuanced differences between concepts, they do not contextualize the meaning of the concepts, and/or they simply leave the discussion to the methodology section with the simple remark “we use X’s measurement items.” Clarity of the concepts helps clarify the main underlying mechanisms behind a phenomenon. Ignoring conceptual clarity means giving up the chance to offer novel theoretical explanations of what happens to the phenomenon.

Another common mistake in this area is the lack of conceptual clarity in construct definitions. While drawing on constructs from interrelated disciplines such as management, organizations, marketing and psychology is valuable, it is crucial to have a deep understanding of their conceptual core.

We encourage authors to challenge themselves by introducing new constructs to capture novel phenomena. However, a common error is not following the correct construct development process (i.e. MacKenzie et al., 2011). It is imperative to give much attention to providing precise definitions for all constructs. Specific tables should be included to demonstrate the origin of the constructs, whether they are adopted as-is or adapted, and the process used in the adaptation process. Ambiguity in this process can create serious problems in developing theoretical arguments.

The lack of novelty also comes from asking very broad (vague) questions, e.g. “what”, “what impact,” etc. These questions often lead to descriptive information. More meaningful research questions come from critically assessing existing theoretical understanding or problems. Jumping to research questions or empirical work before completing a critical assessment of existing theoretical knowledge means it is extremely hard to elaborate on what novel theoretical insights the study offers. A superficial understanding of the literature (existing theoretical understanding) means a superficial description of novelty.

While we acknowledge a surge in submissions detailing the impact of Generative AI within LSCM, these studies, while empirically sound, often do not transcend the boundaries of existing theoretical paradigms. We are highly receptive to manuscripts that showcase advancements in decision-making, productivity, supply chain structure reconfiguration, innovative processes and practices, sustainability initiatives, worker well-being considerations, regulatory policy considerations and ethical considerations (i.e. Richey et al., 2023; Fosso Wamba et al., 2023). However, pursuing these advancements should be accompanied by a heightened level of theoretical explanation closely aligned with the research design, advanced methodologies and meticulous data collection. Failure to adhere to these guidelines renders manuscripts theoretically deficient. For instance, the deployment of established models, such as the Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technology (UTAUT) in the context of AI, without substantial adaptation, contributes little to the theoretical discourse. These frameworks, though robust for initial technological adoption studies, are insufficient to capture the nuanced and evolving complexities of AI’s role in LSCM. Theoretical innovation is as crucial as technological innovation; hence, manuscripts that merely retrofit AI into these well-worn models fail to meet the novelty threshold we seek. Instead, we encourage authors to challenge extant theories or propose novel theoretical constructs that better account for the transformative potential of AI in redefining the dynamics of LSCM. This theoretical advancement should also be reflected in the research design, with methodological rigor and comprehensive data analysis supporting the proposed theoretical contributions.

The previous one is a typical pitfall leading to rejection based on the lack of novelty, particularly in the context of complex LSCM issues. It is imperative to embark on timely and captivating research endeavors to address and circumvent this challenge. Elevating the likelihood of a manuscript moving under review and having a chance to be accepted hinges on the recognition that editors increasingly scrutinize originality and relevance, with the potential for being influential as a pivotal factor.

Before initiating any research project in the realm of LSCM, it is crucial to pose fundamental questions:

  1. How does the proposed research introduce a new and compelling perspective?

  2. Where does the existing literature fall short of providing necessary explanations?

  3. How does it pose challenges and contribute to advancing the field of LSCM?

  4. Is the work directly aligned with current, significant industry or society topics?

  5. How does this work position itself concerning the business/society problem in the field of LSCM?

  6. Do exploratory studies identify phenomena for further testing that existing theory struggles to explain?

  7. Do confirmatory studies test and define the applicability of propositions, aiming to refine or confirm them as new pieces of the puzzle for a theory and describe their practical application?

  8. How should this research be conducted to create value, solve problems or face new challenges?

Scholars should explicitly delineate the purpose and type of contribution in their articles, enhancing the manuscript’s appeal and aligning it with criteria crucial for acceptance in IJPDLM. The assertion of novelty or contribution relies on effectively arguing for knowledge gaps, omissions or a fresh perspective within the existing knowledge base. Consequently, strategic positioning becomes essential to justify the manuscript’s relevance, elucidating why it merits the reader’s attention and providing the interpretative framework. For example, problematization serves as a methodology to pinpoint and question the underlying assumptions of existing theories, adding novelty (Alvesson and Sandberg, 2011; Sandberg and Alvesson, 2021). Moreover, in the context of LSCM, leveraging the three dominant manifestations of the temporal lens – time as a resource, time as structure, and time as a process – can significantly enhance novelty and advance the field with insightful findings (Blagoev et al., 2023).

These perspectives facilitate the generation of research questions that, in turn, contribute to developing more compelling and influential papers within the realm of LSCM.

Essentially, positioning is pivotal in constructing and rationalizing the research purpose, detailing how the author plans to investigate or fulfill specific objectives. When evaluating a research project, consider how it introduces a new and compelling perspective, poses challenges and contributes to advancing the field. Assess whether the work is directly aligned with current, significant industry or societal topics and how it positions itself concerning the business problem and previous studies in the field of LSCM. Finally, we recommend incorporating attention-grabbing elements at the beginning of the paper, such as startling statistics, anecdotes, quotes or vignettes related to the industry or social problems the research is addressing. These elements will captivate our readers and reinforce the logical justification for the research and its positioning. This approach aligns with the expectations of our readership and enhances the overall impact of the research within the LSCM field.

3. Data, rigor and measurement issues

IJPDLM stands as a high-quality platform within the academic landscape, distinct from traditional Operations Research/Management Science (OR/MS) journals. Its focus revolves around the exploration of real-world applications, emphasizing empirical insights and theoretical foundations related to LSCM. In line with the journal’s aim and scope, quantitative mathematical modeling research is outside the scope of IJPDLM. Instead, the journal invites comprehensive (in-depth) case studies and the use of authentic and empirical data, fostering robust discussions on model functionality, performance and generalizability in the context of existing literature.

A hallmark of IJPDLM is its commitment to transparency and rigor in research. Authors utilizing data from real cases are expected to thoroughly describe the case, justifying its validity while engaging in a discourse on its generalizability. This aligns with the principled standards inherent in academic publications. While IJPDLM welcomes a spectrum of research methodologies, ranging from conceptual studies to quantitative and qualitative approaches, observations reveal common pitfalls in study design. As we navigate through these challenges, it becomes imperative to underscore the critical role of precision in data collection and measurement. This discussion aims to shed light on the common lapses identified in submissions to IJPDLM, offering insights and recommendations to enhance the overall quality and impact of research in the realm of LSCM. Research methods evolve to address important problems; otherwise, questions can remain off-limits for researchers. This development follows a common trend of iteratively expanding hypotheses in response to results and reviewer feedback. However, we concur with Connelly et al. (2023), that more hypotheses may not always enhance the quality, and introducing a new moderator does not necessarily contribute novelty to the narrative. The same applies to their recommendation, “More Supplementary Tests Are Not Always Better”; consequently, adding a test that does not contribute value makes a paper worse and difficult to read, not better. Sometimes, there is a lack of theoretical motivation for studying certain variables within the same model. From a data analysis perspective, it is unclear why the authors opted for one methodological approach over another to test the model. While a mediation and moderation model should add value, it requires theoretical justifications.

Furthermore, many authors assume rigor is a question of method and data, especially post-data collection. This is only half of the story. There is also rigor in conceptualization and measurements. In the realm of deductive research, authors should construct hypotheses that are both logical and well-supported, grounded in theory, to effectively address the research questions. The main constructs and their measurements should reflect theoretical foundations (assumptions, conceptualization). It is the measurement items that reflect the underlying mechanisms behind the phenomenon. However, many authors simply ignore the need to use measurement items to explain the hypotheses or the underlying mechanisms. Frequently, measurement items do not reflect the arguments for the hypotheses or the theory itself. For example, EICs often commented, “… the construct doesn’t adequately reflect the concept of X” (certainly not the main ideas in the theory the authors used, e.g. dynamic capability). Many have forgotten the need to ensure measurement properties are consistent with the theory (theoretical explanation) and the use of formal and information definitions of concepts (Wacker, 2004). There is also a tendency to use past evidence to justify hypotheses but ignore that the evidence comes from different measurement scales, conceptualizations, theoretical bases and contexts. Such de-contextualized baseless claims can lead to serious flaws in conceptualization and theoretical arguments. All the post-data-collection techniques to measure validity and reliability become useless when the conceptual and measurement scales are flawed.

In a recent Special Issue on Partial Least Squares Structural Equation Modeling (PLS-SEM) for predictive modeling, IJPDLM delved into the intricacies of employing PLS-SEM and offered valuable insights into its application in LSCM (Wang et al., 2023; Cheah et al., 2023). These studies show serious lapses in skipping important steps to ensure data and method rigor. Advances in techniques developed by other fields are not used, while flawed old techniques are applied without questions. As we embark on the review process for new submissions, both authors and reviewers must draw upon these references for guidance.

In the examined manuscript, a notable fatal flaw concerns the hypothesis of relationships between the variables under consideration. While the model suggests connections among these variables, a critical deficiency arises from the absence of detailed explanations regarding the underlying mechanisms or processes driving these relationships. Essentially, the manuscript falls short in addressing the “why” and “how” aspects of the stated connections, thereby compromising the strength of its arguments for mediation effects. To improve the manuscript’s persuasiveness and robustness in establishing mediation effects, it becomes imperative to delve into the specific mechanisms or pathways through which one variable influences another. More than merely asserting the existence of a relationship is required; the manuscript must construct a coherent narrative elucidating the causal steps or processes involved. Doing so will strengthen the overall argument, and the analysis will also achieve heightened clarity and depth.

Then, another prevalent issue observed in submitted manuscripts pertains to measurement. At times, the measurement items within a manuscript appear broad and abstract, lacking a direct reflection of the specific topic, unit of analysis and constructs under consideration. Attention should be put into clarifying the unit of analysis rather than using, e.g. “we” without clarifying who they are. There is also a tendency to claim “we use X’s measurement items” without showing the details. Manuscripts offering low transparency regarding the questionnaire design, data collection, and/or measure development will be returned to the authors for further clarification or desk-rejected.

For example, one of the EICs commented, “The journal emphasizes data and method transparency, especially when it comes to rigor in informant selection and sampling. We expect you to assess the knowledge of the experts to measure whether they have knowledge about relationships between the concepts. You provided limited information about this sampling process. The study cannot be considered rigorous without this information.

Thus, a crucial piece of advice to authors is to enhance methodological transparency by refining measurement scales. This not only fortifies the empirical foundations but also bolsters the theoretical underpinnings of the work. Without a clear understanding of the measurement scales' rigor, evaluating how well they align with the proposed theories becomes challenging.

To address this, we encourage authors to align their measurements more closely with the specific business context relevant to their study. A meticulous examination of measurement items ensures that they accurately capture the nuances of the phenomena under investigation. This alignment is pivotal for meeting the journal’s clarity requirement regarding what is being measured concerning a specific phenomenon.

In instances where ambiguity exists regarding the precise nature of the measurement, we emphasize that such clarity is a key expectation from the journal. A clear articulation of what is being measured about a specific phenomenon is indispensable for establishing a solid foundation for scholarly contributions. As authors prepare their research project and the relative design, meticulous attention to these measurement considerations will undoubtedly contribute to their work’s overall strength and impact within the IJPDLM community. This is a key requirement from the journal.

Some studies put limited attention to sampling procedures that ensure valid and reliable data from informants. Very often, authors ignore steps to improve knowledge of the studied topics fit with the purpose of the data collection. Also, there is often a need for more efforts to test the data collection instrument, even though similar instruments have been applied in previous studies. For example, studying online ordering behaviors from individuals with and without experience in activities specifically linked to the business problem under investigation can yield very different responses. Ignoring these subtle differences means collecting data that may reflect diverse populations, leading to results that can mislead.

For example, an SAE commented: “However, participants, limited to online ordering experience, may lack face validity in responding to specific research questions.”

Additionally, in scrutinizing the research methodologies applied to emerging technologies (such as AI) within the context of LSCM, we recurrently encounter a fundamental flaw: the reliance on data from single respondents to infer organizational behaviors and outcomes. Such studies frequently employ surveys capturing individual perceptions of specific technologies and attempt to extrapolate these findings to broader organizational decisions, like the adoption of technology or the consequent performance impact. This is a comment provided on a recent, desk-rejected submission: “The model has integrated personal-level constructs (attitudes towards Gen AI and technology readiness) with constructs that are inherently organizational or systemic in nature (circular economy practices and supply chain efficiency/performance). This represents a methodological incongruence as it infers that an individual’s perspective is reflective of complex, multi-layered organizational systems (…). This methodological design raises significant concerns that challenge the validity of the findings. The core issue lies in the conflation of data levels, mixing individual-level perceptions with organizational and supply chain-level outcomes. This approach presupposes that a single individual’s attitudes and perceptions can be indicative of, or exert influence on, the broader organizational and supply chain dynamics. Such an assumption is particularly tenuous unless the respondents hold positions of substantial decision-making power, such as CEOs or presidents, which does not appear to be the case in your sample.” This approach is inherently problematic. An individual’s viewpoint is shaped by a multitude of subjective factors and may not accurately reflect the strategic direction, capabilities or collective wisdom of the organization. Moreover, the nuances of technology implementation and its integration into the multifaceted processes of supply chain activities necessitate a comprehensive evaluation involving multiple stakeholders. Decisions of technology adoption and its benefits are invariably complex, shaped by interdepartmental dynamics, and should not be distilled to a singular perspective. Therefore, to derive more meaningful insights into the influence of technologies on global supply chain decisions, research must either embrace a multi-respondent, multi-level approach that accounts for the diverse and intersecting influences within an organization or keep the analysis at the individual level.

These points connect with the concern about face validity in the experimental and survey designs, which are indeed noteworthy. Face validity refers to the extent to which a measure or assessment appears, on the surface, to accurately capture the construct it is intended to measure. In this context, the apprehension is centered around the participants' potential lack of familiarity with the specific nuances of the research questions. This raises questions about the face validity of the results generated from such participants, as the survey instrument may not align seamlessly with their practical experiences. Ensuring the validity of experimental results is paramount for drawing accurate and meaningful conclusions. In light of this concern, exploring strategies for enhancing face validity within the experimental design is imperative. This might involve refining the survey questions, providing additional context or considering alternative methodologies that align more closely with participants' experiences. By addressing these validity concerns, the research can bolster the credibility of its findings and contribute to a more robust understanding of the relationships under investigation. Enhancing research credibility and reinforcing model-based analysis results can be achieved by incorporating model-free evidence for statistical rigor (Scott and Davis-Sramek, 2022).

In the case of inductive qualitative research, the process culminates in formulating insightful propositions intricately connected to a theoretical framework and future empirical research. By addressing these issues head-on, we hope to guide authors toward elevating the rigor of their data and measurement practices, ultimately contributing to advancing knowledge in our dynamic field. See an example of rejection based on one of the EIC’s comments: “The data show more nuances than how they are broadly categorized, so the opportunity to conceptualize and theorize is missed. The data show efforts from various stakeholders, e.g. international buyers, suppliers, and government. The data also show reasoning for specific actions. I suggest re-do all the coding (using grounded theory, e.g. open coding, axial coding, selective coding) from a stakeholder theory perspective and forgetting all three theories (…) The most intriguing aspect lies in understanding how to address the barriers and drivers rather than merely describing what they are.”

The above example shows a tendency to broadly categorize data (into broad topics like barriers and drivers). Some studies collected data from different stakeholders, who may have different perspectives and cognitive processes behind them. Aggregating perspectives from different stakeholders mask some of the nuanced differences between them, missing the opportunities to reveal subtle differences as the basis for theorizing them for a deeper understanding. From labeling high-level categories (using topics), there is a need to learn to label thinking and actions (verbs) using nuanced descriptions (using adjectives or adverbs). This will help move towards a theorizing typology and underlying processes instead of broad answers (to what questions). Without this process, authors often receive the “lack of theoretical insights” comment.

In the realm of inductive research, where complexities abound and concepts continually evolve, embracing an exploratory approach becomes not just beneficial but essential. Authors engaging in this form of research are encouraged to seek out novel perspectives that distinguish their work from existing literature. Instead of solely reiterating established knowledge, the emphasis should be on uncovering new mechanisms and boundaries, thereby contributing fresh insights to the field.

At IJPDLM, a pivotal focus lies on methodological transparency, particularly concerning data collection and analysis. It is imperative for authors to rigorously select informants and sampling strategies that align with best practices. The method section should adhere closely to the robust methodologies, ensuring a comprehensive understanding of the research process. Within qualitative studies, clarity is paramount. Authors must explicitly address various aspects:

  1. How was the interview protocol developed?

  2. What steps were taken to achieve data saturation?

  3. How clear and straightforward is the coding process description in explaining the authors' decision-making steps?

  4. Did you consider providing examples of codes and themes to enhance clarity and convince readers about the absence of alternative explanations in your research? If yes, how was this made?

  5. How have the authors avoided socially desirable answers? How have the authors dealt with these validity problems?

  6. Why was a specific context or industry sector chosen for examination?

  7. How do you elucidate the procedures employed for inter-rater reliability, specifically addressing whether multiple readers interpreted and analyzed transcripts? If disagreements arose, what issues were involved, and how frequently did discrepancies occur?

  8. How do you align the research propositions more closely with the research questions to enhance clarity? Additionally, were there any observed mismatches between the propositions and research questions?

  9. How surprised should I be by these findings, and more importantly, what is their significance in theory elaboration or theory building?

Within quantitative studies, authors need to clarify, for example:

  1. What units of analysis and population are being studied, and how are samples drawn from the population?

  2. How do the researchers ensure informants are knowledgeable and capable of answering the questions?

  3. What detailed survey instrument was utilized (including the exact wording of the questions asked)? Were any of the questions dropped along the process?

  4. What other variables were included in the survey instrument but not in the manuscript, and why?

  5. How were the survey instrument and every question developed, validated and tested?

  6. What are the results for all the statistical tests that reflect the assumptions behind the statistical analysis methods applied?

  7. Are there any missing values or data, and how were these issues addressed?

  8. Are there anomalies coming in the various statistical tests that could mislead the results? How were these anomalies handled?

  9. Were modifications to the structured models needed? If yes, what were they? How can we ensure that model modifications are guided by substantive considerations and what choices were adopted when dealing with constraints that necessitated significant modifications?

These questions are essential in elucidating the depth and breadth of the research undertaken.

Furthermore, the transparency and reliability of qualitative findings hinge on trustworthiness criteria. Authors are encouraged to openly discuss the strategies employed to ensure validity, credibility, dependability, confirmability, integrity, transferability and fit, at least in their research and following good examples in the literature. This discussion not only solidifies the validity of the study but also elevates the overall contribution to the field of LSCM, fostering a deeper understanding of the complexities inherent in the realm of supply chain management (new) challenges.

In maintaining a consistent positioning of the paper and employing precise measurements, a commitment to methodological rigor becomes paramount. Acknowledging the link between methodological diligence and the validity of conclusions, this commitment ensures that research outcomes remain robust and logically sound, guarding against the pitfalls of theorizing lapses or methodological mistakes. The key methodological criterion currently centers around whether the methodology is adequately elucidated to facilitate future research replication.

4. Descriptive analysis lacking theoretical insights

Some manuscripts fall short of achieving adequate theoretical contribution. A common misconception arises when descriptive concepts or frameworks are misconstrued as theoretical. Despite their significant potential, many studies present descriptive information rather than developing genuine theoretical concepts. There is a tendency to aggregate data into broad categories (e.g. driver, barrier, capability) erroneously labeled as theoretical categories. Whether in literature reviews or qualitative studies, there is a prevalent inclination to claim a “theoretical” framework has been developed by merely organizing literature or data into broad topics. It is crucial to note that topics themselves are not theoretical concepts. Sometimes, the data lack the nuance required to justify conceptual labels, and authors may halt analysis prematurely upon achieving broad categorization. Some may lack familiarity with techniques like axial and selective coding (theoretical coding) or grounded theory (Mello et al., 2021), or they may mistakenly believe that broad categorization alone constitutes a theoretical contribution (Magnani and Gioia, 2023).

In certain qualitative studies, we observe a persistent lack of substantial improvements in establishing connections between data collection, interviews and research findings. The data analysis often fails to consistently bridge emerging data, potential gaps and the foundational literature supporting research propositions. Concerns may arise regarding the limited number of interviewees, necessitating more details or a follow-up plan for additional interviews. It is imperative to incorporate codes and themes/areas derived from interviews, and clarity can be enhanced by connecting exemplar quotes with the main themes/areas through a table. While IJPDLM offers supplementary material for extensive appendices, it is crucial to maintain consistency with the main manuscript. This involves adhering to guidance aimed at enhancing the logical progression of the study, from design and data gathering to coding, analysis and the presentation of findings, all while following practical tools (Rockmann and Vough, 2023).

For submissions, an emphasis should be placed on theoretical development, requiring a robust discussion section that intricately links findings with existing literature. If theories are retained, developing research propositions at the end, tied to the identified gaps and the research question, is expected.

In deductive research, incorporating diverse theories for testing is not seamlessly woven into constructing the paper’s theoretical foundation and hypothesis argumentation. The authors would benefit from focusing on a specific set of theories and ensuring that the mechanisms scrutinized in the analysis align cohesively with these chosen theories. Such concentration would fortify the paper’s arguments on a robust theoretical foundation. We should encourage the authors to go beyond the surface-level description of significant relationships and provide insightful interpretations and contextualization of the results.

Good research should be both interesting and influential, aiding decision-makers in better understanding and decision-making. Consistently, the analysis needs to pursue this ambitious goal to achieve a meaningful intersection of interesting and influential (Bartunek et al., 2006; Fawcett et al., 2014; Wong, 2021a; Tsang, 2022; Richey and Davis-Sramek, 2022).

5. Descriptive literature review lacking new knowledge generation and theoretical advancements

At IJPDLM, we are open to publishing literature reviews that not only focus on specific LSCM topics but also transcend mere description to contribute significantly to advancing knowledge in the field.

Consequently, it normally does not publish pure bibliometric papers because they focus on telling who or what topics got cited and describing past studies; they do not add new knowledge. Regrettably, we find that recent submissions do not align with these editorial criteria.

The overarching approach is predominantly descriptive and lacks clear motivations for why a literature review on a specific topic is indispensable. When we say a literature review is descriptive, we refer to the use of descriptive statistics and descriptions of what past studies did, as opposed to critically assessing theoretical concepts and empirical findings in the literature. The journal expects a deeper analysis of how and why certain concepts are linked together instead of just graphics showing they are somehow linked.

In the LSCM field, the last few years have witnessed a proliferation of literature reviews on several research topics, addressing the pivotal question of “Where are we at?” The majority of the papers, unfortunately, do not make a distinct contribution or advance knowledge in this area. The proposal of further conceptualization is particularly crucial, and regrettably, this element is absent in several works. This effort serves as a cornerstone in establishing the foundation for impactful scholarship. To understand what we expect from a literature review paper, we suggest referring to Durach et al. (2021), Wong (2021b), Ketchen and Craighead (2023) and Kunisch et al. (2023).

In summary, the literature review underscores a dual-fold objective. Firstly, it endeavors to explore extensive research at the intersection of relatively unexplored thematic areas. This exploration not only delves into the advancements within a specific research stream but also assesses their profound impact on the intricate understanding of supply chain dynamics and change.

Additionally, the review aims to unravel the underlying interactions among theories, themes, and contextualized mechanisms. Going beyond mere theoretical frameworks, it involves a nuanced examination of supply chain actors engaged in a particular research domain. This scrutiny focuses on the “for whom” aspect, elucidating the actors involved and affected by the exploration of a specific area of research.

Furthermore, the examination extends to specific LSCM processes, shedding light on the “in what circumstances” dimension. It seeks to clarify the circumstances under which certain lenses prove most impactful in influencing these processes.

Then, temporal aspects come under scrutiny, addressing the “when” factor involved in the implementation of practices. Understanding the timing and sequencing of the phenomenon under investigation is deemed crucial in ensuring the efficacy of these advancements within the SCM landscape.

Finally, authors are encouraged to establish connections with respected practitioners, drawing on widely-read press articles and incorporating managerial quotes or personal observations of industry challenges. This methodological approach seamlessly extends to aligning future research concepts with industry perspectives.

In essence, IJPDLM advocates for a comprehensive review that explores topics within LSCM and elucidates multifaceted interactions among various elements. By adopting a contextualized and explanatory approach, researchers are positioned to make significant contributions to the ongoing debate within the discipline and across different disciplines, thereby paving the way for innovative solutions and advancements in the field.

As stewards of rigorous and impactful scholarly discourse, we encourage authors to delve deeper, offering comprehensive insights and conceptual advancements in their literature reviews.

5.1 Final thoughts: the path towards a successful publication

Our aspiration is that by leveraging our experiences as authors, reviewers, and editors, we offer valuable insights and ideas. These contributions aim to assist in refining their research design, increase the likelihood of their work being accepted in a top-tier journal such as IJPDLM, and foster greater consistency and objectivity in developing and evaluating manuscripts during the review process. Table 1 provides a comprehensive overview of the main reasons for rejection and possible ways to avoid these fatal flaws, as discussed in the previous sections. We encourage authors to use this table as a checklist before submitting a manuscript to IJPDLM.

It is crucial to recognize that every submitted manuscript requires a minimum of 3–4 reviewers with availability, reliability and high-quality skills. These reviewers play a pivotal role in the Editor’s puzzle, ensuring the review process is rigorous, valuable and timely. As we continue to strive for excellence in scholarly publishing, we extend our gratitude for the continued support and expertise provided by the editorial team, including the Editorial Manager, the Senior Associate Editors, the Editorial Reviewer Board and ad hoc reviewers. Their contribution is crucial to maintain our reputation as trusted gatekeepers. Authors can actively contribute to this co-creation of value by carefully reading editorials and acquiring sufficient knowledge about the journal to which they plan to submit their work. This proactive approach helps the entire community derive value from the review process, preventing unnecessary strain on the time and capacity of the editorial team and steering clear of common pitfalls.

Editorial recommendations to avoid desk rejection in submitted manuscripts

Reasons of rejectionsEditors’ tip
1. Superficial/inappropriate use of theory
  • Put more effort into clarifying assumptions, conceptualizing and theorizing (explaining the logic) a phenomenon or problem

  • Be aware of and address the limitations of a known theory if you choose to apply it. Put more effort into problematization and move away from superficial mention of a theory

  • Align research problems to generate new knowledge

  • With novel and complex phenomena, middle-range theory, abductive reasoning approach, design science and intervention-based approach should be used as emerging theoretical designs with practical relevance

2. Lack of novelty
  • The novelty here means new knowledge and understanding, starting from a deep understanding of a stream of research, normally created by new concepts, perspectives, theoretical explanations and/or evidence/analysis, but also adding new knowledge to an existing stream of literature or theoretical perspective

  • Novelty also comes from asking new questions, explaining a phenomenon from a different perspective and integrating new ideas into old ones

  • Interdisciplinary approaches that blend insights from different fields can lead to the creation of novel insights, enriching the existing stream of literature by offering a more comprehensive and nuanced understanding

  • Novelty also requires challenging conventional approaches by introducing new constructs to capture emerging phenomena, navigate evolving industry complexities and address the ambiguity surrounding the dimensions of a business problem

3. Data, rigor and measurement issues
  • Do not ignore rigor in conceptualization and theorization

  • Post-data collection tests of rigors cannot fix conceptualization, theorization and measurement issues created during the study design

  • Put more effort into ensuring data and methods reliability and transparency to facilitate readability for reviewers and future research replication

  • Use methods with the latest guidelines with the highest rigor

  • Clearly justify the chosen research methodology

4. Descriptive analysis, lacking theoretical insights
  • Even though the description of a phenomenon (what question) is key, do not stop here. Avoid creating (vague/broad) themes that have no theoretical meanings

  • Put more effort into conceptualizing data into theoretical concepts that help theorize

  • Focus on explaining how and why things or people behave in specific manners, differentiating them under different conditions (when, where)

  • Avoid speculative claims (e.g. AI improves resilience) without explaining the underlying mechanisms. Novel theoretical insights (a deeper understanding) are the core for claiming contribution

  • Good research should be both interesting and influential, assisting decision-makers in better comprehending complexities and tackling novel problems effectively

5. Descriptive analysis of structure literature review
  • Put less effort into describing the literature, authors, studies, journals, methods, citation count, etc. The goal of a literature review is to assess how the literature uses concepts, theories, data/evidence and analyses to better understand a real-world phenomenon, not to count citations or list topics being studied

  • Focus on literature reviews that show how to advance concepts, theories, methods and analyses and add new knowledge to a phenomenon or problem

  • It aims to unravel intricate interactions among theories, themes and contextualized mechanisms, focusing on “for whom” by elucidating key actors and their impact, revealing the “in what circumstances” dimension, clarifying impactful perspectives and addressing the crucial “when” factor in practice implementation, contributing to a comprehensive understanding of the research

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