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In every market there are non-consumers – potential customers that withstand an innovation. A common reason is that products targeted to early adopters or the mass market are over-engineered. Established companies usually neglect the laggards in a market. This offers growth opportunities for new entrants. These market niches can be unlocked when products are simplified and adapted to the special needs of the laggards.

Using a short case study from an Austrian producer of cell-phones targeted to seniors, and some other examples, we show how innovative solutions can be developed by targeting to the special needs of laggards in a market.

Technologies usually evolve faster than market needs and established companies, in their efforts to grow and improve profitability, try to innovate faster than their competitors. This often leads to over-engineered products. In many markets there are consumers that withstand these innovative and over-engineered products. A vacuum for low-priced, simple, and easy-to-use solutions emerges. We describe a five-step approach for unlocking these market segments and developing solutions for laggards.

While most companies try to innovate faster than their competitors to defend their market leadership, laggard innovation targets non-consumers in the market. By simplifying over-engineered products and adapting them to the special needs of laggards, new market opportunities emerge. This paper shows how these market niches can be unlocked.

This is an exploratory study leveraging a domain-specific innovativeness (DSI) perspective to understand adoption of QR code delivered mobile marketing. Specifically, the purpose of this paper is to explore the roles of “innovativeness” and “risk aversion” on QR code adoption and usage in the low-involvement context to address tensions between risk and innovation literatures.

Participants were assigned to “laggard” (n=19) and “innovator/early adopter” (n=19) segments using the DSI scale. A combination of qualitative reductionism (means-end chain analysis) and qualitative holism (semi-structured interviewing) was employed.

Confusion regarding the functionality and purpose of QR codes adversely affected willingness to use and utility perceptions. Source trust and information credibility emerged as key concerns for those considering QR codes, with consumer risk aversion and innovativeness orientations influencing the nature of trust concerns. A perceived lack of complementarity between QR codes and retail environments reduced perceived relevance. For low-involvement products, marketers should consider moving beyond brand-level communication to align offerings to the broader foodscape.

Findings are limited to the low-involvement product context. Although DSI was considered, the impact of technological innovativeness was not explored. Future research may consider the impact of information provision at the broader foodscape rather than product-specific level when exploring QR code applications in the food domain.

This is the first study to explore the role of product category innovativeness, as distinct from technological innovativeness on the acceptance and usage of mobile marketing applications in the low-involvement context. This research builds on existing risk and innovation literatures and addresses tensions between these literatures presented by QR codes within the low-involvement context.

To date there is a dearth of research on strategic decision‐making in manufacturing small and medium sized firms. This research paper focuses on the key drivers of decision‐making and organisational performance: ownership, decision‐making (type and level) and employee deployment. These drivers are defined and operationalised.

The validity of the constructs used and their relevance was tested through the qualitative phase of the research – in‐depth interviews with chief executives and employer representative bodies. The survey instrument was also pilot tested.

The findings indicate that the sample can be divided into performance leaders and laggards. Over 75 per cent of the leaders are independently owned and over 66 per cent of the laggards are subsidiary firms. Accordingly, ownership is a key factor in overall performance. This is confirmed by the degree of profitability of both classifications.

The practical implications of the study are outlined in the paper. Chief executives are encouraged to focus on the aspects of ownership, decision making, and employee deployment that are associated with enhanced performance.

This study focuses on decision making and employee deployment according to ownership and overall performance – factors hitherto under researched in SMEs.

The purpose of this paper is to identify which factors influence mobile banking adoption and examine those factors for segmentation, using a sample of Indian consumers.

In total, 59 statements were identified based on a literature review, focus group discussions and personal interviews. Exploratory factor analysis was conducted to identify the relevant factors. An online survey of 367 mobile phone users in India was conducted. Confirmatory factor analysis was conducted using structural equation modeling. Appropriate statistical techniques (hierarchical cluster analysis, k-means cluster analysis) were used to segment the users. A profile of each segment was developed based on demographics, mobile banking services used, and attitude and intentions toward mobile banking. Further, a post hoc test was used to test the variation between the obtained clusters and user attitudes and intentions toward mobile banking. The demographic characteristics of users within each cluster were also examined.

Mobile users were segmented into three clusters based on their perceptions of various factors influencing mobile banking. These segments were labeled as technology adoption (TA) leaders, TA followers and TA laggards. The results show that both attitude and intentions toward mobile banking significantly differs across the three segments. In terms of relative positioning, TA leaders have the most favorable attitudes and intentions followed by TA followers, and TA laggards. Age was found to significantly influence TA and usage.

The findings of the study are based on responses from young, educated and salaried Indian consumers from large metro cities. Therefore, it is important to include respondents from smaller cities and towns to be able to generalize the findings. The sample is skewed toward users having accounts with private banks and hence, a balanced representation of respondents from public and private sector banks would help in identifying gaps pertaining to each sector. In future research, attempting to compare the results with other developing and developed countries may be beneficial.

The results offer service providers better knowledge about typical mobile banking user segments, providing banks with ideas for customizing their services to meet customer expectations.

This paper provides insights into factors that influence mobile banking adoption in India, which has not been investigated. In contrast to earlier studies conducted on internet banking, this study attempts to examine the perceptions, attitudes and intentions of mobile users. Although traditional TA models and theories of technology diffusion have been used, this study attempts to tailor the model specifically for mobile banking.

This paper aims to analyze the determinants of broadband diffusion, taking into account supply‐side factors such as market entry regulation and demand‐side factors such as secondary education attainment.

This paper analyzes 27 countries from the European Union (EU) from 1996 to 2009 using qualitative comparative analysis (QCA), which combines quantitative and qualitative methods.

The main findings point out that there is one way for “innovator” adopters and “laggard” adopters in broadband diffusion, while there are few ways for the remaining adopters. Moreover, high entry regulation is associated more with “innovator” adopters, “early adopters” and “early majority adopters” in broadband diffusion, while medium and low entry regulations are associated more with “late majority” and “laggard” adopters in broadband diffusion.

This paper suggests that high secondary school attainment is a necessary factor for broadband innovator countries. By contrast, low income and low secondary school attainment are factors connected with late majority and laggard broadband adopters.

At present, there is no other research about broadband diffusion or technology diffusion that uses this mixed approach. While the results may not be very conclusive, they will serve as an initial springboard for further research into more specific‐variable studies.

Despite the evident and dramatic increase in smartphone usage worldwide, some consumers continue to use traditional mobile phones. The purpose of this paper is to investigate the behavioral intentions of these laggard and non-smartphone users.

This current study examines the effects of consumer demographics, psychographics, and smartphone characteristics on the intentions of non-smartphone consumers to switch or resist the use of smartphones. Data were collected using a convenience sample of non-smartphone users in Taiwan. The proposed model is tested using the consistent partial least squares (PLSc) path modeling technique.

PLSc results indicate that consumer psychographics and smartphone characteristics play more important roles than consumer demographics. Specifically, price consciousness, nostalgia, and perceived ease of use are good predictors of intention to switch, whereas perceived usefulness and ease of use are strong predictors of the intention to resist smartphone adoption.

The results of this study have implications for mobile phone vendors and mobile manufacturers who target non-smartphone users or laggard adopters.

This study is among the few that focus on non-smartphone users’ perceptions of smartphones. Hence, this empirical study could contribute to the development and testing of theories related to the smartphone adoption process.

In the architecture, engineering and construction (AEC) industry, a “digital divide” exists in technology adoption because SMEs (who often form the bulk of AEC organisations in most countries) are thought to be “Late Majority” and “Laggards” in the adoption of Building information modelling (BIM) technology. Larger organisations not saddled with financial and socio-technical constraints might be considered as being among the “Early Majority” or “Innovators”. It is crucial to understand how these organisations differ in their speed of BIM technology adoption and the rationale for this difference. The purpose of this paper is to investigate the potential causes of the digital divide and suggest solutions for bridging the gap.

Using mixed research method, data were collected through online questionnaire survey of over 240 global respondents as well as a semi-structured interview with nine experts for which statistical and thematic analyses were used, respectively.

Organisations can be zoned into “layers” and “levels” of BIM technology adoption and their size is not always significant in terms of the speed at which they adopt BIM. The digital divide is unequal across layers/levels and large organisations utilise technologies across the BIM maturity levels depending on project circumstances. A conceptual model for BIM technology was developed to aid identification of the “Laggards” and “Late Majority” from the “Innovators” through which change agents can customise adoption strategies for each group.

The developed model could serve as a tool for engagement and policy making and it contributes to the body of knowledge in the field of BIM technology adoption.

This study aims to develop a taxonomy of organizations according to business process orientation (BPO) maturity and investigate the difference between clusters in terms of performance outcome.

A survey of various organizations in Indonesia is conducted. The main variables are critical practices (CPs) as the measurement variables of BPO maturity and performance outcome. Cluster analysis is performed to obtain an empirical taxonomy of the organizations. ANOVA test is used to test if there are statistically different performance outcomes across different clusters.

Cluster analysis resulted in six archetypes labeled according to their characteristics: Beginners, Non-technical, Domestics, IT laggards, Excellers, and Champions. The ANOVA test results show that the archetypes with high CPs tend to have high perceived performance results.

This study is limited because the authors use a single dataset from organizations in Indonesia. Further study involving more organizations will be beneficial to validate and enrich the taxonomy of organizational archetypes.

Results of the study can be used as a benchmarking tool by organizations to identify their positions against other organizations and set their areas for improvement. It can also help them identify a roadmap for improvement that will benefit their organization.

Using the CPs as a measure of BPO enables the authors to identify supplier orientation and information and technology (IT) implementation as the primary differentiators within the taxonomy. The use of IT differentiates the bottom, middle and top clusters.

This study explores the nature of critical success factors (CSFs) in UK budget hotel operations through a questionnaire survey designed to ascertain the relevance and importance of a range of factors referred to as critical in the extant academic and trade literature. From a usable sample of 239 questionnaires the results show that budget hotel unit managers from the leading brands largely agree with the criticality of the factors stated in the literature, this being verified via the independent t‐test and reliability analysis results that show most of the CSFs to be highly statistically significant (p<0.001) and the set of CSF items to be consistent (α=0.9360). The data analysis, via principal components analysis, also facilitates the creation of a parsimonious model that identifies two dimensions and five CSFs as being the “most critical”.

Healthcare networks are becoming ubiquitous, yet it is unclear how hospitals with varying quality capabilities would fare by being affiliated with large healthcare networks. The purpose of this paper is to first consider the deductive configuration perspective and distinguish high and low quality hospitals by using clinical and experiential quality as two dimensions of quality capability. Next, it examines the impact of healthcare network size on operating costs of hospitals. Additionally, the paper investigates the interaction effect of hospital demand and healthcare network size on operating costs.

The paper uses a dataset that was created by combining five separate sources. Cluster analysis technique is used to classify hospitals into four groups – holistic quality leaders (high clinical and experiential quality capability), experiential quality focusers (low clinical quality capability and high experiential quality capability), clinical quality focusers (high clinical capability and low experiential quality capability), and quality laggards (low clinical and experiential quality capability). The authors test the research hypotheses by means of regression analyses after controlling for several contextual characteristics.

The results show that affiliation with large healthcare networks reduces operating costs for quality laggards, but increases these costs for experiential quality focusers and clinical quality focusers. The hypothesized positive relationship between healthcare network size and costs is not supported for holistic quality leaders. The authors find that clinical quality focusers and holistic quality leaders can complement higher utilization levels in their operations due to increased demand and healthcare network size to reduce their operating costs per day.

There has been increasing evidence suggesting that hospitals must carefully manage both clinical and experiential quality. By focusing on both clinical and experiential quality, unlike experiential quality focusers and clinical quality focusers, holistic quality leaders are not adversely affected by the size of their network. The results suggest that experiential quality focusers and clinical quality focusers should either embrace holistic quality management or restrict the size of their networks to maintain their quality level and to reduce coordination costs.