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Business analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies to facilitate decision-making, there are as of yet limited studies on how analysts, in practice, improve decision makers' understanding of business environments. This study uses sensemaking theory and proposes a model of how data analysts generate analytical outcomes to improve decision makers' understanding of the business environment.

This study employs an interpretive field study with thematic analysis. The authors conducted 32 interviews with data analysts and consultants in Australia and New Zealand. The authors then applied thematic analysis to the collected data.

The thematic analysis discovered four main sensegiving activities, including data integration, trustworthiness analysis, appropriateness analysis and alternative selection. The proposed model demonstrates how these activities support the properties of sensemaking and result in improved decision-making.

This study provides strong empirical evidence for the theory development and practice of sensemaking. It brings together two distinct fields – sensemaking and business analytics – and demonstrates how the approaches advocated by these two fields could improve analytics applications. The findings also propose theoretical implications for information system development (ISD).

This study demonstrates how data analysts could use analytical tools and social mechanisms to improve decision makers' understanding of the business environment.

This study is the first known empirical study to conceptualize the theory of sensemaking in the context of BA and propose a model for analytical sensegiving in organizations.

An increased understanding of the capabilities needed for HR Analytics and how to build synergies from these capabilities is of practical and academic importance. Using the lens of Systems Theory, an explorative case study is performed in a multinational food distribution company that is building its HR Analytics Capabilities. In this study, the synergistic enablers and mechanisms have been examined in practice for the domain of HR Analytics and the BA Capabilities involved (clustered into Technology, Governance, Analytic Practices and Processes, People and Culture). Examples of (in)compatibilities, integration efforts, mechanisms and synergistic outcomes are given from the case organization. This study provides insights on how in practice the interaction between BA Capabilities can lead to synergistic relationships and synergistic outcomes and through what mechanisms and enablers this is being facilitated. The study contributes to HR Analytics and IS literature in terms of the use of synergistic enablers and mechanisms in practice.

Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA.

Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies.

Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment.

Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities.

This study provides useful management insights into the effective use of BA for improving organizational performance.

The purpose of this article is to identify how the organisations are able to improve their business value through acquisition of business analytics capabilities and by improving their performance.

With the help of literature survey, along with standard resource-based view framework, a conceptual model has been developed. These have been statistically tested by collecting the data using the survey questionnaire from 306 selected respondents from various service sector- and product-based organisations in India. To analyse the data we have used partial least square–based structural equation modelling.

The study highlights that by the help of data acquisition and tool acquisition as two vital components the acquisition of business analytics capabilities could improve the business value of the organisation by strengthening its organisational performance. The findings of this research also indicated that acquisition of business analytics capabilities has a significant influence on an organisation's business process performance and business decision, which in turn significantly influences organisational performance. And organisational performance eventually positively influences its business value. The model was found to provide an explanative power of 71%.

The proposed research model can provide effective recommendations to the management of the organisations to realise the importance of acquisition of effective business analytics capabilities to eventually improve the business value of the organisation.

No specific studies, as yet, have analysed the effects of acquisition of business analytics capabilities for improving organisational performance mediated through business process performance and business decision. Therefore, this research has explored the distinctive effort to empirically validate this understanding.

The study aims to provide insights in the sensemaking process and the use of business analytics (BA) for project selection and prioritisation in start-up settings. A major focus is on the various ways start-ups can understand their data through the analytical process of sensemaking.

This is a comparative case study of two start-ups that use BA in their projects. The authors follow an interpretive approach and draw from the constructivist grounded theory method (GTM) for the purpose of data analysis, whereby the theory of sensemaking functions as the sensitising device that supports the interpretation of the data.

The key findings lie within the scope of project selection and prioritisation, where the sensemaking process is implicitly influenced by each start-up's strategy and business model. BA helps start-ups notice changes within their internal and external environment and focus their attention on the more critical questions along the lines of their processes, operations and business model. However, BA alone cannot support decision-making around less structured problems such as project selection and prioritisation, where intuitive judgement and personal opinion are still heavily used.

This study extends the research on BA applied in organisations as tools for business development. Specifically, the authors draw on the literature of BA tools in support of project management from multiple perspectives. The perspectives include but are not limited to project assessment and prioritisation. The authors view the decision-making process and the path from insight to value, as a sensemaking process, where data become part of the sensemaking roadmap and BA helps start-ups navigate the decision-making process.

The purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA).

Existing theory is extended by introducing the concept of BA data-driven storytelling and by synthesizing insights from BA, storytelling, behavioral research, linguistics, psychology and neuroscience. Using theory-building methodology, a model with propositions is introduced to demonstrate the relationship between storytelling, data interpretation quality, decision-making quality, intention to use BA and actual BA use.

BA data-driven storytelling is a narrative sensemaking heuristic positively influencing human behavior towards BA use. Organizations deliberately disseminating BA data-driven stories can improve the quality of individual data interpretation and decision-making, resulting in increased individual utilization of BA on a daily basis.

To acquire a deeper understanding of BA data-driven storytelling in behavioral operational research (BOR), future studies should test the theoretical model of this study and focus on exploring the complexity and diversity in individual attitudes toward BA.

This study provides practical guidance for business practitioners who struggle with interpreting vast amounts of complex data, making data-driven decisions and incorporating BA into daily operations.

This cross-disciplinary study develops existing BOR, storytelling and BA literature by showing how a novel BA data-driven storytelling approach can facilitate BA adoption in organizations.

Drawing on the resource-based theory and dynamic capability view, this paper aims to examine the mechanisms by which business analytics (BA) capabilities (i.e. the effective use of data aggregation, analytics and data interpretation tools) in healthcare units indirectly influence decision-making effectiveness through the mediating role of knowledge absorptive capacity.

Using a survey method, this study collected data from the hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84 per cent response rate with 152 valid data points. Structural equation modeling was used to test the hypotheses.

This study conceptualizes, operationalizes and measures the BA capability as a multi-dimensional construct that is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through using the data analysis and interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity.

This study adds values to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA to the decision-making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision-making practices but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.

As businesses keep investing substantial resources in developing business analytics (BA) capabilities, it is unclear how the performance improvement transpires as BA affects performance in many different ways. This paper aims to analyze how BA capabilities affect firms’ agility through resources like information quality and innovative capacity considering industry dynamism and the resulting impact on firm performance.

This paper tested the research hypothesis using primary data collected from 192 companies operating in Bangladesh. The data were analyzed using partial least squares-based structural equation modeling.

The results indicate that BA capabilities improve business resources like information quality and innovative capacity, which, in turn, significantly impact a firm’s agility. This paper also found out that industry dynamism moderates the firms’ agility and, ultimately, firms’ performance.

The contribution of this work provides insight regarding the role of business analytics capabilities in increasing organizational agility and performance under the moderating effects of industry dynamism.

The present research is to the best of the authors’ knowledge among the first studies considering a firm’s agility to explore the impact of BA on a firm’s performance in a dynamic environment. While previous researchers discussed resources like information quality and innovative capability, current research theoretically argues that these items are a leveraging point in a BA context to increase firm agility.

This paper focuses on the “how” of business analytics (BA) value creation, which remains an open research problem and a practical challenge. The main purpose of this paper is to propose a novel BA value creation mechanism that is BA-enabled improvement of Knowledge-intensive Business Processes (KIBPs), with experiential knowledge of decision makers as the key to a more sustainable BA-enabled competitive differentiation.

This research uses a qualitative research case study, conducted in a large retail distribution company. The research insights were observed through a combined lens of work systems theory and the knowledge-based view (KBV) of the firm, using an interpretive approach.

The proposed theoretical model identifies three stages of KIBP improvement through BA and explains how they lead to a sustainable BA-enabled competitive differentiation. Stage 1 focusses on BA support for individual knowledge-intensive tasks, Stage 2 focusses on individual decision makers and their ability to gain KIBP-related analytical insights and turn them into action; and Stage 3 on sharing of the acquired experiential knowledge amongst decision makers using BA.

In addition to proposing a novel mechanism for BA value creation, this research demonstrates the importance of leveraging experiential knowledge of decision makers as a pathway to a more sustainable competitive differentiation through BA. This, in turn, creates new opportunities for knowledge management researchers to engage in BA-related research. It also opens a new approach for BA researchers to investigate BA value creation mechanisms through the lens of KBV, rather than more common resource-based or capability-based views.

Despite a growing interest in business analytics (BA) from the business and academic communities, it is still unknown what truly motivates and hinders the adoption of BA. To have a clear picture of what will lead to the successful implementation of BA, this paper identifies contextual variables (e.g. user characteristics, organizational readiness and technology infrastructure/expertise) that significantly influence the BA adoption decision.

This paper conducted a series of classification, discriminant and logistics regressions analyses to analyze the differences in mail survey responses between adopters and nonadopters of BA and then determine what either motivate or inhibit the BA adoption.

Through a series of hypothesis testing, we discovered that large firms with a greater number of information technology (IT) staff and budget tended to adopt BA more than their smaller counterparts. Also, we found that BA skeptics, who did not fully recognize BA benefit potentials, were more concerned about BA implementation costs and experienced the greater organization resistance to BA adoption than the others did. Therefore, they were less likely to adopt BA.

In the era of knowledge-based economy, the firm's ability to derive actionable insights from big data can be a game changer. Such ability can be developed and nurtured by utilizing BA which is designed to help business executives and policymakers make well-thought and informed decisions. This paper is one of the first attempts to develop practical guidelines for the successful implementation of BA based on the exploratory study of BA practices among the Korean firms.