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The purpose of this paper is to empirically study the necessary and sufficient antecedents of customer loyalty to logistics service providers (LSPs). Despite the abundance of loyalty studies, few studies have empirically examined the complex causal relationships between loyalty and its antecedents using the appropriate research methods.

The study uses fuzzy sets qualitative comparison analysis (FsQCA) to uncover the antecedents and antecedent configurations of customer loyalty. This study examined the necessity and sufficiency of five antecedents (operational collaboration, strategic collaboration, trust, service satisfaction and price satisfaction) and on five dimensions of loyalty (repurchase intension, switching costs, exclusiveness, referrals and overall loyalty). This study developed six research hypotheses based on existing literature and tested them empirically in a manufacturing context. This study compared results from FsQCA with regression analysis.

The findings reveal that only a few antecedent configurations lead to customer loyalty, including a combination of strategic and operational collaboration, bundling of service offerings and combining competitive price with strategic collaboration. Further, the absence of trust negatively affects loyalty, but the presence of trust does not lead to loyalty.

This study contributes to business to business literature by demonstrating how to use FsQCA analysis to test the presence of equifinality, conjunction and causal asymmetry of complex relationships. This study applies FsQCA between antecedent factors and customer loyalty. Furthermore, we demonstrate how to compare and integrate FsQCA with conventional regression analysis which is based on correlation-bounded logic.

This study shows that no single antecedents that can generate loyalty, yet, few antecedent configurations work better than others: collaborating at both strategic and operational level with your partner has the highest impact on loyalty. Another configuration that leads to loyalty is to combine competitive prices with strategic collaboration, which particularly stimulates repurchase decisions. Price is are not sufficient enough to lead to customer loyalty.

This study applied a novel analysis to uncover necessary and sufficient conditions that conventional methods such as regression analysis and structural equation modelling have limited power.

Given the complexity of building information modeling (BIM) adoption decisions in small- and medium-sized enterprises (SMEs) in the Architecture, Engineering and Construction (AEC) industry, understanding BIM adoption decision-making through the net effect of a single factor on BIM adoption decisions alone is limited. Therefore, this paper analyzed the co-movement effect of managers' psychological factors on the BIM adoption decisions from the perspective of managers' perceptions. The purpose is to let managers have a deep understanding of their BIM adoption decisions, and put forward targeted suggestions for the AEC industry to promote the adoption of BIM by SMEs.

Data from 192 managers in SMEs collected by the questionnaire were used in a fuzzy set qualitative comparative analysis (fsQCA). Due to the limitations of fsQCA in making the best use of the data used, as a complement to fsQCA, necessary conditions analysis (NCA) was used to analyze the extent to which necessary conditions influenced the outcome.

(1) NCA analysis shows that high perceived resource availability (PRA) and high performance expectancy (PE) are necessary conditions for high BIM adoption intention (AI). (2) fsQCA analysis shows that high PE is the single core condition for high AI. fsQCA analysis identifies three configurations of managers' psychological factors, reflecting three types of managers' decision preferences, namely benefit preference, loss aversion and risk avoidance, respectively. Different decision preferences may lead to different BIM adoption strategies, such as full in-house use, partial in-house/outsourcing and full outsourcing of BIM processes. (3) High perceived risk (PR) and low perceived business value of BIM (PBV) are the core conditions for low AI.

This paper expands on the application of fsQCA to context of BIM adoption decisions. Based on the results of fsQCA analysis, this paper also establishes the relationship between managers' decision-making psychology and BIM adoption strategy choice and analyzes the impact of different decision biases on BIM adoption strategy choice. It concludes with suggestions for encouraging managers to adopt BIM and for avoiding decision-making bias.

This research introduces the combined use of partial least squares–structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) that enables researchers to explore and validate hypotheses following a sufficiency logic, as well as hypotheses drawing on a necessity logic. The authors’ objective is to encourage the practice of combining PLS-SEM and NCA as complementary views of causality and data analysis.

The authors present guidelines describing how to combine PLS-SEM and NCA. These relate to the specification of the research objective and the theoretical background, the preparation and evaluation of the data set, running the analyses, the evaluation of measurements, the evaluation of the (structural) model and relationships and the interpretation of findings. In addition, the authors present an empirical illustration in the field of technology acceptance.

The use of PLS-SEM and NCA enables researchers to identify the must-have factors required for an outcome in accordance with the necessity logic. At the same time, this approach shows the should-have factors following the additive sufficiency logic. The combination of both logics enables researchers to support their theoretical considerations and offers new avenues to test theoretical alternatives for established models.

The authors provide insights into the logic, assessment, challenges and benefits of NCA for researchers familiar with PLS-SEM. This novel approach enables researchers to substantiate and improve their theories and helps practitioners disclose the must-have and should-have factors relevant to their decision-making.

Qualitative Comparative Analysis (QCA) is a promising, powerful method that is increasingly used for IS research. However, the Information Systems (IS) discipline still lacks a shared understanding of how to conduct and report QCA. This paper introduces the fundamental concepts of QCA, summarizes the status quo, and derives recommendations for future research.

A descriptive literature review in major IS outlets summarizes how and why QCA has been used in the IS discipline, critically evaluates the status quo, and derives recommendations for future QCA studies.

The literature review reveals 32 empirical research articles in major IS journals that have used the QCA method. Articles applied QCA to a broad range of research topics at the individual and organizational levels, mainly as a standalone analysis for theory development, elaboration and testing. The authors also provide evidence that most published IS research articles do not take full advantage of the potential QCA, such as analyzing necessary causal conditions or testing the robustness of QCA results. The authors provide seven actionable recommendations for future IS research using QCA.

The literature review assesses the status quo of QCA’s application in the IS discipline and provides specific recommendations on how IS researchers can leverage the full potential of QCA.

This paper aims to address which resources provided by an entrepreneurial ecosystem (EE) are necessary for deep technology entrepreneurship.

The authors used a novel approach known as necessary condition analysis (NCA) to data on EEs and deep-tech startups from 132 countries, collected in a global innovation index and Crunchbase data sets. The NCA makes it possible to identify whether an EEs resource is a necessary condition that enables entrepreneurship.

Necessary conditions are related to political and business environment; education, research and development; general infrastructure; credit; trade; diversification and market size; and knowledge absorption capacity.

The results show that business and political environments are the most necessary conditions to drive deep-tech entrepreneurship.

Policymakers could prioritize conditions that maximize entrepreneurial output levels rather than focusing on less necessary elements.

Some resources require less performance than others. So, policymakers should consider allocating policy efforts to strengthen resources that maximize output levels.

Studies on deep-tech entrepreneurship are scarce. This study provides a bottleneck analysis that can guide the formulation of policies to support deep-tech entrepreneurship, as it allows to identify priority areas for resource allocation.

The purpose of this paper is to show that necessary condition hypotheses are important in operations management (OM), and to present a consistent methodology for building and testing them. Necessary condition hypotheses (“X is necessary for Y”) express conditions that must be present in order to have a desired outcome (e.g. “success”), and to prevent guaranteed failure. These hypotheses differ fundamentally from the common co‐variational hypotheses (“more X results in more Y”) and require another methodology for building and testing them.

The paper reviews OM literature for versions of necessary condition hypotheses and combines previous theoretical and methodological work into a comprehensive and consistent methodology for building and testing such hypotheses.

Necessary condition statements are common in OM, but current formulations are not precise, and methods used for building and testing them are not always adequate. The paper outlines the methodology of necessary condition analysis consisting of two stepwise methodological approaches, one for building and one for testing necessary conditions.

Because necessary condition statements are common in OM, using methodologies that can build and test such hypotheses contributes to the advancement of OM research and theory.

The purpose of this paper is to decipher the levels of three strategic orientations – learning orientation, entrepreneurial orientation (proactiveness and risk taking) and marketing orientation (responsiveness to customers, responsiveness to competitors, responsiveness to the macro-environment and business relationship quality) – that are necessary for firm innovativeness.

Data were collected from a sample of 316 firms in Ho Chi Minh City, Vietnam. Multiple regression analysis (MRA) was employed to examine the net effects of seven factors (learning orientation, proactiveness, risk taking, responsiveness to customers, responsiveness to competitors, responsiveness to changes in the macro-environment and business relationship quality) on firm innovativeness. Necessary condition analysis (NCA) was then employed to discover the level of these factors as necessary conditions for firm innovativeness.

The results produced by MRA show that learning orientation, proactiveness, responsiveness to customers and responsiveness to competitors have positive effects on firm innovativeness. The results from the NCA reveal that six out of seven conditions exhibit varying necessary levels for firm innovativeness.

The findings are relevant to senior managers and suggest that the levels of strategic orientations necessary for firm innovativeness vary. Firms therefore should pay attention not only to the net effects (beta weights) but also to their necessary levels. Based on their resources and capabilities, firms should take into account the necessary level of each strategic orientation in order to achieve their innovativeness goal.

This study is among the first to decipher the levels of three strategic orientations (learning orientation, entrepreneurial orientation and marketing orientation) that are necessary for firm innovativeness.

In this paper, the authors aim to present a novel methodological tool – necessary condition analysis (NCA) to aid managerial psychology researchers in properly testing necessity statements.

The authors employ NCA to analyze whether three basic psychological needs for autonomy, competence and relatedness are necessary for work engagement.

The authors illustrate the value and application of NCA by revealing that basic psychological needs for autonomy, competence and relatedness are necessary for work engagement, as proposed by self-determination theory (SDT).

The authors illustrate the importance of the sufficiency-necessity distinction and the relevance of a necessity logic in managerial psychology. They also discuss NCA's methodological implications for managerial psychology research, theory and practice.

Because family businesses are highly complex enterprises, researchers need appropriate theoretical and methodological tools to study them. The neoconfigurational perspective and its accompanying method, qualitative comparative analysis, are particularly well suited to phenomena characterized by complex causality, but their uptake in family business research has been slow and fragmented. To remedy this, the authors highlight their unique ability to address research questions for which other approaches are not well suited and discuss how they might be applied to family business phenomena.

The authors introduce the core tenets of the neoconfigurational perspective and how its set-theoretic epistemology differs from traditional approaches to theorizing and analysis. The authors then use a dataset of family firms to present a primer on conducting qualitative comparative analysis and interpreting the results.

The authors find that family firm resources can be combined in multiple ways to affect business survival, suggesting that resources are substitutable and complementary. The authors discuss how the unique features of the neoconfigurational approach, namely equifinality, conjunctural causation and causal asymmetry, can be fruitfully applied to break new ground in scholarly understanding of family businesses.

This article allows family business researchers to apply the neoconfigurational approach without first having to consult multiple and disparate sources often written for other disciplines. This article explicates how to leverage the theoretical and empirical advantages of the neoconfigurational approach in the context of family businesses, supporting a more widespread adoption of the neoconfigurational perspective in family business research.

Self-service check-out technologies (SSTs) are becoming a trend across different retail settings, allowing companies to gain efficiency and reduce costs. Nevertheless, the success of SSTs implementation is still subject to challenges and uncertainties. This study aims to provide insights for theory and managers on the necessary conditions for the successful implementation of retail SSTs.

Based on an online survey, data from 251 participants were collected to understand the factors predicting SSTs adoption and realise what conditions are mandatory for the adoption. partial Least Squares Structural Equation Modelling (PLS-SEM) and necessary condition analysis (NCA) were used to analyse the data.

According to the NCA analysis results, 12 latent variables were relevant for predicting SSTs adoption, but only seven were necessary conditions for user adoption.

The complementarity of perspectives for understanding the adoption of SSTs based on the two data analysis techniques provides novel insights into theory and support for retailers' decision-making on self-service technologies (STTs) implementation.